added model

README.md

@@ -5,7 +5,7 @@ colorFrom: blue
 colorTo: red
 sdk: gradio
 sdk_version: "4.19.2"
-app_file: app.py
+app_file: util/app.py
 pinned: false
 ---
 

util/Leaderboard_scrapper.py

@@ -0,0 +1,144 @@
+import pandas as pd
+import os
+import time
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+
+def scrape_leaderboards(regions=None, pages_per_region=5, output_file=None, delay=2):
+    """
+    Scrape leaderboard data from op.gg for specified regions and return as DataFrame.
+    
+    Args:
+        regions (list): List of regions to scrape. Defaults to ["kr", "na", "vn", "euw"]
+        pages_per_region (int): Number of pages to scrape per region. Defaults to 5
+        output_file (str): Path to output file. Defaults to "util/data/leaderboard_data.csv"
+        delay (int): Delay between requests in seconds. Defaults to 2
+    
+    Returns:
+        pandas.DataFrame: Scraped leaderboard data
+    """
+    # Set defaults
+    if regions is None:
+        regions = ["kr", "na", "vn", "euw"]
+    
+    if output_file is None:
+        output_file = os.path.join("util", "data", "leaderboard_data.csv")
+    
+    # Initialize data list to store rows
+    leaderboard_data = []
+
+    try:
+        # Setup Chrome options
+        chrome_options = Options()
+        chrome_options.add_argument("--headless")
+        chrome_options.add_argument("--no-sandbox")
+        chrome_options.add_argument("--disable-dev-shm-usage")
+        chrome_options.add_argument("--disable-gpu")
+        chrome_options.add_argument("--disable-logging")
+        chrome_options.add_argument("--log-level=3")
+        chrome_options.add_argument("--disable-extensions")
+        chrome_options.page_load_strategy = 'eager'
+        chrome_options.add_argument(
+            "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+        )
+
+        # Initialize WebDriver
+        driver = webdriver.Chrome(
+            service=Service(ChromeDriverManager().install()),
+            options=chrome_options
+        )
+
+        for region in regions:
+            print(f"\nScraping {region.upper()} region...")
+            for page in range(1, pages_per_region + 1):
+                print(f"Processing page {page}/{pages_per_region}")
+                url = f"https://www.op.gg/leaderboards/tier?region={region}&type=ladder&page={page}"
+                
+                try:
+                    # Access the webpage
+                    driver.get(url)
+
+                    # Wait for table to load
+                    table = WebDriverWait(driver, 15).until(
+                        EC.presence_of_element_located((By.CSS_SELECTOR, "table.css-1l95r9q.e4dns9u11"))
+                    )
+
+                    # Process rows
+                    rows = table.find_elements(By.TAG_NAME, "tr")[1:]  # Skip header row
+                    for row in rows:
+                        try:
+                            cells = row.find_elements(By.TAG_NAME, "td")
+                            if len(cells) >= 7:
+                                # Extract basic data
+                                summoner = cells[1].text.strip().replace("\n", " ")
+                                rank = cells[0].text.strip()
+                                tier = cells[2].text.strip()
+                                lp = cells[3].text.strip()
+                                level = cells[5].text.strip()
+
+                                # Extract champion data
+                                champion_imgs = cells[4].find_elements(By.TAG_NAME, "img")
+                                champions = [img.get_attribute("alt") for img in champion_imgs]
+                                champion_data = champions + [""] * (3 - len(champions))
+
+                                # Parse win/loss data
+                                winrate_text = cells[6].text.strip().split("\n")
+                                wins = winrate_text[0].rstrip("W") if len(winrate_text) > 0 else ""
+                                losses = winrate_text[1].rstrip("L") if len(winrate_text) > 1 else ""
+                                winrate = winrate_text[2] if len(winrate_text) > 2 else ""
+
+                                # Append row data
+                                leaderboard_data.append({
+                                    "summoner": summoner,
+                                    "region": region,
+                                    "rank": rank,
+                                    "tier": tier,
+                                    "lp": lp,
+                                    "most_champion_1": champion_data[0],
+                                    "most_champion_2": champion_data[1],
+                                    "most_champion_3": champion_data[2],
+                                    "level": level,
+                                    "win": wins,
+                                    "loss": losses,
+                                    "winrate": winrate
+                                })
+
+                        except Exception as e:
+                            print(f"Error processing row in {region} page {page}: {e}")
+                            continue
+
+                except Exception as e:
+                    print(f"Error processing {region} page {page}: {e}")
+                    continue
+
+                time.sleep(delay)
+
+    except Exception as e:
+        print(f"Fatal error: {e}")
+        return None
+
+    finally:
+        driver.quit()
+
+    # Create DataFrame
+    df = pd.DataFrame(leaderboard_data)
+    
+    # Clean and convert data types
+    df['lp'] = df['lp'].str.replace(',', '').str.replace('LP', '').astype(float)
+    df['level'] = df['level'].astype(int)
+    df['win'] = pd.to_numeric(df['win'], errors='coerce')
+    df['loss'] = pd.to_numeric(df['loss'], errors='coerce')
+    df['winrate'] = df['winrate'].str.rstrip('%').astype(float) / 100
+    
+    # Save to CSV if output_file is specified
+    if output_file:
+        os.makedirs(os.path.dirname(output_file), exist_ok=True)
+        df.to_csv(output_file, index=False)
+        print(f"Leaderboard data saved to {output_file}")
+
+    return df

util/Meta_scrapper.py

@@ -0,0 +1,147 @@
+import re
+import os
+import pandas as pd
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+
+# Constants
+ROLES = ["top", "jungle", "mid", "adc", "support"]
+BASE_URL = "https://www.op.gg/champions?position={role}"
+TIER_COLOR_MAPPING = {
+    "#0093FF": 1,  # Blue
+    "#00BBA3": 2,  # Teal
+    "#FFB900": 3,  # Yellow
+    "#9AA4AF": 4,  # Gray
+}
+
+def setup_driver():
+    """Setup and return a configured Chrome WebDriver with optimized settings"""
+    chrome_options = Options()
+    chrome_options.add_argument("--headless")
+    chrome_options.add_argument("--no-sandbox")
+    chrome_options.add_argument("--disable-dev-shm-usage")
+    chrome_options.add_argument("--disable-gpu")
+    chrome_options.add_argument("--disable-extensions")
+    chrome_options.add_argument("--disable-logging")
+    chrome_options.add_argument("--log-level=3")
+    chrome_options.add_argument("--silent")
+    chrome_options.add_argument(
+        "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    )
+    
+    # Remove log_level parameter from ChromeDriverManager
+    service = Service(ChromeDriverManager().install())
+    return webdriver.Chrome(service=service, options=chrome_options)
+
+def parse_rate(rate_str):
+    """Convert percentage string to float"""
+    try:
+        return float(rate_str.strip().rstrip('%')) / 100
+    except:
+        return 0.0
+
+def extract_counter_champions(counter_column):
+    """Extract counter champions from column"""
+    counter_champions = []
+    try:
+        counter_list = counter_column.find_elements(By.TAG_NAME, "a")
+        for counter in counter_list[:3]:
+            img_element = counter.find_element(By.TAG_NAME, "img")
+            champion_name = img_element.get_attribute("alt")
+            counter_champions.append(champion_name)
+    except Exception:
+        pass
+    return counter_champions + [""] * (3 - len(counter_champions))
+
+def get_champion_table_data(driver, url, role):
+    """Extract champion data from a specific role page with optimized parsing"""
+    try:
+        driver.get(url)
+        table = WebDriverWait(driver, 20).until(
+            EC.presence_of_element_located((By.CSS_SELECTOR, "#content-container > div.flex.gap-2.md\\:mx-auto.md\\:w-width-limit.mt-2.flex-col.overflow-hidden > div.flex.flex-row-reverse.gap-2 > main > div:nth-child(2) > table"))
+        )
+
+        champions_data = []
+        for row in table.find_elements(By.TAG_NAME, "tr"):
+            cols = row.find_elements(By.TAG_NAME, "td")
+            if len(cols) <= 1:
+                continue
+
+            # Get tier value
+            tier_element = cols[2].find_element(By.TAG_NAME, "svg")
+            tier = 5
+            if tier_element:
+                for path in tier_element.find_elements(By.TAG_NAME, "path"):
+                    fill_color = path.get_attribute("fill")
+                    if fill_color in TIER_COLOR_MAPPING:
+                        tier = TIER_COLOR_MAPPING[fill_color]
+                        break
+
+            # Extract ban rate
+            ban_rate_html = cols[6].get_attribute("innerHTML").strip()
+            ban_rate_match = re.search(r"([\d.]+)", ban_rate_html.replace("<!-- -->", ""))
+            ban_rate = float(ban_rate_match.group(1)) / 100 if ban_rate_match else 0.0
+
+            # Get counter champions
+            counter1, counter2, counter3 = extract_counter_champions(cols[7])
+
+            champions_data.append({
+                "rank": cols[0].text.strip(),
+                "champion": cols[1].text.strip(),
+                "tier": tier,
+                "role": role,
+                "win_rate": parse_rate(cols[4].text),
+                "pick_rate": parse_rate(cols[5].text),
+                "ban_rate": ban_rate,
+                "counter1": counter1,
+                "counter2": counter2,
+                "counter3": counter3,
+            })
+
+        return champions_data
+
+    except Exception as e:
+        print(f"Error extracting table data for {role}: {e}")
+        return []
+
+def get_meta_stats():
+    """Main function to scrape champion data with improved error handling and logging"""
+    driver = None
+    
+    try:
+        driver = setup_driver()
+        all_roles_data = []
+
+        for role in ROLES:
+            role_url = BASE_URL.format(role=role)
+            role_data = get_champion_table_data(driver, role_url, role)
+            all_roles_data.extend(role_data)
+
+        if not all_roles_data:
+            print("No data was collected from any role")
+            return pd.DataFrame()
+
+        df = pd.DataFrame(all_roles_data)
+        
+        # Save data
+        save_dir = os.path.join("util", "data")
+        os.makedirs(save_dir, exist_ok=True)
+        filepath = os.path.join(save_dir, "meta_stats.csv")
+        df.to_csv(filepath, index=False)
+        print(f"Saved meta stats to {filepath}")
+        return df
+
+    except Exception as e:
+        print(f"Error in get_meta_stats: {e}")
+        return pd.DataFrame()
+
+    finally:
+        if driver:
+            driver.quit()
+
+    

util/Player_scrapper.py

@@ -0,0 +1,544 @@
+import os
+import time
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+import pandas as pd
+from helper import format_summoner_name
+
+# Constants
+BASE_URL = "https://www.op.gg/summoners/{region}/{username}?queue_type=SOLORANKED"
+MASTERY_URL = "https://www.op.gg/summoners/{region}/{username}/mastery"
+
+def setup_driver():
+    """Setup optimized Chrome WebDriver"""
+    options = Options()
+    options.add_argument("--headless")
+    options.add_argument("--no-sandbox")
+    options.add_argument("--disable-dev-shm-usage")
+    options.add_argument("--disable-gpu")
+    options.add_argument("--disable-logging")
+    options.add_argument("--log-level=3")
+    options.add_argument("--disable-extensions")
+    options.page_load_strategy = 'eager'
+    options.add_argument(
+        "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    )
+    return webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
+
+def wait_and_find_element(driver, selector, timeout=20, description="element"):
+    """Utility function for waiting and finding elements"""
+    try:
+        element = WebDriverWait(driver, timeout).until(
+            EC.presence_of_element_located((By.CSS_SELECTOR, selector))
+        )
+        return element
+    except Exception as e:
+        print(f"Error finding {description}: {e}")
+        return None
+
+def get_recent_stats(stats_box):
+    """Extract recent statistics from stats box"""
+    try:
+        stats = stats_box.find_element(By.CSS_SELECTOR, "div.stats")
+        recent_stats = stats.text.strip().split("\n")
+        
+        # Parse the stats into a structured format
+        games_info = recent_stats[0].split()  # ['20G', '13W', '7L']
+        total_games = int(games_info[0].replace('G', ''))
+        wins = int(games_info[1].replace('W', ''))
+        losses = int(games_info[2].replace('L', ''))
+        
+        win_rate = float(recent_stats[1].replace('%', '')) / 100
+        
+        kda_parts = recent_stats[2].split(' / ')  # ['5.1', '4.0', '7.9']
+        kills = float(kda_parts[0])
+        deaths = float(kda_parts[1])
+        assists = float(kda_parts[2])
+        
+        kda_ratio = float(recent_stats[3].replace(':1', ''))
+        kill_participation = float(recent_stats[4].replace('P/Kill ', '').replace('%', '')) / 100
+
+        recent_stats = {
+            "total_games": total_games,
+            "wins": wins,
+            "losses": losses,
+            "win_rate": win_rate,
+            "avg_kills": kills,
+            "avg_deaths": deaths,
+            "avg_assists": assists,
+            "kda_ratio": kda_ratio,
+            "kill_participation": kill_participation,
+        }
+    
+    except Exception as e:
+        print(f"Error extracting recent stats: {e}")
+        return None
+    
+    return recent_stats
+
+def get_recent_champions(stats_box):
+    champions = stats_box.find_element(By.CSS_SELECTOR, "div.champions")
+    champion_elements = champions.find_elements(By.CSS_SELECTOR, "li")
+    
+    # Initialize flat dictionary with defaults
+    recent_champ_stats = {
+        "most_champ_1": None, "WR_1": 0.0, "W_1": 0, "L_1": 0, "KDA_1": 0.0,
+        "most_champ_2": None, "WR_2": 0.0, "W_2": 0, "L_2": 0, "KDA_2": 0.0,
+        "most_champ_3": None, "WR_3": 0.0, "W_3": 0, "L_3": 0, "KDA_3": 0.0
+    }
+
+    for i, champion in enumerate(champion_elements, 1):
+        try:
+            # Initialize kda for this iteration
+            kda = 0.0
+            
+            # Extract champion name and image source
+            champ_name = champion.find_element(By.TAG_NAME, "img").get_attribute("alt")
+            
+            # Extract win/lose stats and KDA
+            win_lose = champion.find_element(By.CSS_SELECTOR, ".win-lose").text.strip()
+            win_rate = float(win_lose.split('%')[0]) / 100  # "75%" -> 0.75
+            wins = int(win_lose.split('(')[1].split('W')[0])  # "(3W 1L)" -> 3
+            losses = int(win_lose.split('W')[1].split('L')[0])  # "1L)" -> 1
+           
+            # KDA processing with a more precise selector
+            try:
+                kda_element = champion.find_element(By.CSS_SELECTOR, "div[class*='e1t9nk8i2']")
+                if kda_element:
+                    kda_text = kda_element.text.strip()
+                    #print(f"Found KDA text for champion {i}: '{kda_text}'")  # Debug print
+                    if kda_text and "KDA" in kda_text:
+                        kda = float(kda_text.split("KDA")[0].strip())
+                        #print(f"Parsed KDA value: {kda}")  # Debug print
+                    else:
+                        print(f"Invalid KDA text format for champion {i}: '{kda_text}'")
+                else:
+                    print(f"No KDA element found for champion {i}")
+            except Exception as e:
+                print(f"Error processing KDA: {e}")
+                kda = 0.0
+                
+            # Update flat dictionary
+            recent_champ_stats[f"most_champ_{i}"] = champ_name
+            recent_champ_stats[f"WR_{i}"] = win_rate
+            recent_champ_stats[f"W_{i}"] = wins
+            recent_champ_stats[f"L_{i}"] = losses
+            recent_champ_stats[f"KDA_{i}"] = kda
+            
+        except Exception as e:
+            print(f"Error processing champion {i}: {e}")
+            # Dictionary already has default values for this champion
+            continue
+    
+    return recent_champ_stats
+
+def get_preferred_role(stats_box):
+    # Role priority (higher index = higher priority when tied)
+    role_priority = {
+        'SUPPORT': 0,
+        'ADC': 1,
+        'TOP': 2,
+        'JUNGLE': 3,
+        'MID': 4
+    }
+    
+    # Find the positions section
+    positions = stats_box.find_element(By.CSS_SELECTOR, "div.positions")
+    role_elements = positions.find_elements(By.CSS_SELECTOR, "li")
+    
+    preferred_roles = {
+        'TOP': 0.0, 'JUNGLE': 0.0, 'MID': 0.0, 'ADC': 0.0, 'SUPPORT': 0.0,
+        'most_role_1': None, 'most_role_2': None,
+        'most_role_1_value': 0.0, 'most_role_2_value': 0.0
+    }
+    
+    # First, collect all role percentages
+    for role in role_elements:
+        role_name = role.find_element(By.CSS_SELECTOR, "div.position img").get_attribute("alt")
+        percentage = role.find_element(By.CSS_SELECTOR, "div.gauge").get_attribute("style")
+        
+        if percentage:
+            percentage_value = percentage.split(":")[1].strip().replace("%", "").strip(';')
+            try:
+                preferred_roles[role_name] = int(percentage_value)/100
+            except ValueError:
+                preferred_roles[role_name] = 0
+    
+    # Sort roles by percentage first, then by priority when tied
+    sorted_roles = sorted(
+        [(role, value) for role, value in preferred_roles.items() if role in role_priority],
+        key=lambda x: (x[1], role_priority[x[0]]),  # Sort by percentage first, then role priority
+        reverse=True
+    )
+    
+    # Add top 2 roles if they exist
+    if len(sorted_roles) > 0:
+        preferred_roles['most_role_1'] = sorted_roles[0][0]
+        preferred_roles['most_role_1_value'] = sorted_roles[0][1]
+    if len(sorted_roles) > 1:
+        preferred_roles['most_role_2'] = sorted_roles[1][0]
+        preferred_roles['most_role_2_value'] = sorted_roles[1][1]
+    
+    return preferred_roles
+
+def get_weekly_stats(ranked_7d_box):
+    # Find the list of champions in the ranked 7d box
+    champion_elements = ranked_7d_box.find_elements(By.CSS_SELECTOR, "ul li")[:3]
+    
+    # Initialize flat dictionary with defaults for 3 champions
+    weekly_stats = {
+        "7d_champ_1": None, "7d_total_1": 0, "7d_W_1": 0, "7d_L_1": 0, "7d_WR_1": 0.0,
+        "7d_champ_2": None, "7d_total_2": 0, "7d_W_2": 0, "7d_L_2": 0, "7d_WR_2": 0.0,
+        "7d_champ_3": None, "7d_total_3": 0, "7d_W_3": 0, "7d_L_3": 0, "7d_WR_3": 0.0
+    }
+    
+    # Find the list of champions and take first 3
+    for i, champion in enumerate(champion_elements, 1):
+        try:
+            # Extract champion name
+            champ_name = champion.find_element(By.CSS_SELECTOR, "div.info > div.name > a").text.strip()
+
+            # Extract wins and losses
+            try:
+                win_text = champion.find_element(By.XPATH, ".//div[@class='graph']//div[@class='text left']").text.strip()
+                loss_text = champion.find_element(By.XPATH, ".//div[@class='graph']//div[@class='text right']").text.strip()
+                wins = int(win_text.replace('W', '').strip()) if 'W' in win_text else 0
+                losses = int(loss_text.replace('L', '').strip()) if 'L' in loss_text else 0
+            except Exception:
+                wins = 0
+                losses = 0
+
+            # Calculate total games
+            total_games = wins + losses
+            
+            # Extract win rate
+            try:
+                win_rate_text = champion.find_element(By.CSS_SELECTOR, "div.winratio").text.strip()
+                win_rate = float(win_rate_text.replace('%', '').strip()) / 100 if win_rate_text else 0
+            except Exception:
+                win_rate = 0
+            
+            # Update flat dictionary with dynamic numbering
+            weekly_stats[f"7d_champ_{i}"] = champ_name
+            weekly_stats[f"7d_total_{i}"] = total_games
+            weekly_stats[f"7d_W_{i}"] = wins
+            weekly_stats[f"7d_L_{i}"] = losses
+            weekly_stats[f"7d_WR_{i}"] = win_rate
+            
+        except Exception as e:
+            print(f"Error processing champion {i} in 7d stats: {e}")
+            # Add default values for error cases
+            weekly_stats[f"7d_champ_{i}"] = None
+            weekly_stats[f"7d_total_{i}"] = 0
+            weekly_stats[f"7d_W_{i}"] = 0
+            weekly_stats[f"7d_L_{i}"] = 0
+            weekly_stats[f"7d_WR_{i}"] = 0.0
+    
+    return weekly_stats
+
+def get_season_data(season_champ_box):   
+    # Initialize flat dictionary with defaults for 7 champions
+    season_data = {
+        "season_champ_1": None, "cs_ssn_1": "0", "cpm_ssn_1": "0", "kda_ssn_1": "0", "k_ssn_1": "0", "d_ssn_1": "0", "a_ssn_1": "0", "wr_ssn_1": 0.0, "games_ssn_1": "0",
+        "season_champ_2": None, "cs_ssn_2": "0", "cpm_ssn_2": "0", "kda_ssn_2": "0", "k_ssn_2": "0", "d_ssn_2": "0", "a_ssn_2": "0", "wr_ssn_2": 0.0, "games_ssn_2": "0",
+        "season_champ_3": None, "cs_ssn_3": "0", "cpm_ssn_3": "0", "kda_ssn_3": "0", "k_ssn_3": "0", "d_ssn_3": "0", "a_ssn_3": "0", "wr_ssn_3": 0.0, "games_ssn_3": "0",
+        "season_champ_4": None, "cs_ssn_4": "0", "cpm_ssn_4": "0", "kda_ssn_4": "0", "k_ssn_4": "0", "d_ssn_4": "0", "a_ssn_4": "0", "wr_ssn_4": 0.0, "games_ssn_4": "0",
+        "season_champ_5": None, "cs_ssn_5": "0", "cpm_ssn_5": "0", "kda_ssn_5": "0", "k_ssn_5": "0", "d_ssn_5": "0", "a_ssn_5": "0", "wr_ssn_5": 0.0, "games_ssn_5": "0",
+        "season_champ_6": None, "cs_ssn_6": "0", "cpm_ssn_6": "0", "kda_ssn_6": "0", "k_ssn_6": "0", "d_ssn_6": "0", "a_ssn_6": "0", "wr_ssn_6": 0.0, "games_ssn_6": "0",
+        "season_champ_7": None, "cs_ssn_7": "0", "cpm_ssn_7": "0", "kda_ssn_7": "0", "k_ssn_7": "0", "d_ssn_7": "0", "a_ssn_7": "0", "wr_ssn_7": 0.0, "games_ssn_7": "0"
+    }
+    
+    try:
+        # Find all champion boxes directly
+        champion_boxes = season_champ_box.find_elements(By.CSS_SELECTOR, "div.champion-box")
+        
+        for i, box in enumerate(champion_boxes[:7], 1):
+            try:
+                # Extract champion name
+                champ_name = box.find_element(By.CSS_SELECTOR, "div.name a").text.strip()
+                # Extract CS stats and CPM
+                cs_text = box.find_element(By.CSS_SELECTOR, "div.cs").text.strip()
+                cs_parts = cs_text.split()
+                cs_stats = cs_parts[1] if len(cs_parts) > 1 else "0"
+                # Extract CPM from parentheses
+                cpm = cs_parts[2].strip('()') if len(cs_parts) > 2 else "0"
+                
+                # Extract KDA ratio
+                kda_element = box.find_element(By.CSS_SELECTOR, "div.kda div[class^='css-']")
+                kda_text = kda_element.text.strip()
+                kda_ratio = kda_text.replace(" KDA", "").replace(":1", "").strip()
+                
+                # Extract K/D/A averages
+                kda_detail = box.find_element(By.CSS_SELECTOR, "div.kda div.detail").text.strip()
+                k, d, a = map(str.strip, kda_detail.split('/'))
+                
+                # Extract win rate
+                win_rate_element = box.find_element(By.CSS_SELECTOR, "div.played div[class^='css-']")
+                win_rate_text = win_rate_element.text.strip()
+                win_rate = float(win_rate_text.replace('%', '')) / 100
+                
+                # Extract games played
+                games_text = box.find_element(By.CSS_SELECTOR, "div.played div.count").text.strip()
+                games_played = games_text.replace(" Played", "")
+                
+                # Update flat dictionary
+                season_data[f"season_champ_{i}"] = champ_name
+                season_data[f"cs_ssn_{i}"] = cs_stats
+                season_data[f"cpm_ssn_{i}"] = cpm
+                season_data[f"kda_ssn_{i}"] = kda_ratio
+                season_data[f"k_ssn_{i}"] = k
+                season_data[f"d_ssn_{i}"] = d
+                season_data[f"a_ssn_{i}"] = a
+                season_data[f"wr_ssn_{i}"] = win_rate
+                season_data[f"games_ssn_{i}"] = games_played
+
+            except Exception as e:
+                print(f"Error processing champion {i}: {str(e)}")
+                print(f"Error type: {type(e).__name__}")
+                
+    except Exception as e:
+        print(f"Error in get_season_data main block: {str(e)}")
+        print(f"Error type: {type(e).__name__}")
+    
+    return season_data
+
+def get_mastery_data(driver):
+    # Initialize dictionary with metadata
+    mastery_data = { }
+    
+    try:
+        # Wait for container to load
+        WebDriverWait(driver, 20).until(
+            EC.presence_of_element_located((By.CSS_SELECTOR, "div.css-zefc5s.e1poynyt0"))
+        )
+        
+        # Get all champion boxes (limiting to first 16)
+        champion_boxes = driver.find_elements(By.CSS_SELECTOR, "div.css-8fea4f.e1poynyt1")[:16]
+        
+        # Process each champion
+        for i, champion in enumerate(champion_boxes, 1):
+            try:
+                name = champion.find_element(By.CSS_SELECTOR, "strong.champion-name").text.strip()
+                level = champion.find_element(By.CSS_SELECTOR, "div.champion-level__text > span").text.strip()
+                #points = champion.find_element(By.CSS_SELECTOR, "div.champion-point > span").text.strip()
+                
+                mastery_data[f"mastery_champ_{i}"] = name
+                mastery_data[f"m_lv_{i}"] = level
+                #mastery_data[f"m_points_{i}"] = points.replace(",", "")
+                
+            except Exception as e:
+                print(f"Error processing champion {i}: {e}")
+                mastery_data[f"mastery_champ_{i}"] = None
+                mastery_data[f"m_lv_{i}"] = "0"
+                #mastery_data[f"m_points_{i}"] = "0"
+
+    except Exception as e:
+        print(f"Error scraping mastery data: {e}")
+
+    return mastery_data
+
+
+def get_player_stats(region, username):
+    """Main function to get player statistics"""
+    driver = None
+    try:
+        driver = setup_driver()
+        
+        # Format URLs
+        profile_url = BASE_URL.format(region=region, username=username)
+        mastery_url = MASTERY_URL.format(region=region, username=username)
+        
+        # Get main profile data
+        driver.get(profile_url)
+        
+        # Find main containers
+        main_container = wait_and_find_element(driver, "#content-container")
+        if not main_container:
+            raise Exception("Could not find main container")
+            
+        stats_box = wait_and_find_element(
+            driver,
+            "div.stats-box.stats-box--SOLORANKED"
+        )
+        
+        season_champ_box = wait_and_find_element(
+            driver,
+            "div:nth-child(1) > div.css-18w3o0f.ere6j7v0"
+        )
+
+        ranked_7d_box = wait_and_find_element(
+            driver,
+            "div[class*='efsztyx0']"
+        )
+
+        # Extract all stats
+        player_data = {
+            'recent_stats': get_recent_stats(stats_box) if stats_box else None,
+            'recent_champions': get_recent_champions(stats_box) if stats_box else None,
+            'preferred_roles': get_preferred_role(stats_box) if stats_box else None,
+            'season_data': get_season_data(season_champ_box) if season_champ_box else None,
+            'weekly_stats': get_weekly_stats(ranked_7d_box) if ranked_7d_box else None,
+        }
+        
+        # Get mastery data
+        driver.get(mastery_url)
+        mastery_data = get_mastery_data(driver)
+        player_data['mastery_data'] = mastery_data
+
+        # Create DataFrames
+        dfs = {}
+        for key, data in player_data.items():
+            if data:
+                dfs[key] = pd.DataFrame([data])
+
+        # Add player ID and region to each DataFrame
+        for df in dfs.values():
+            df.insert(0, 'player_id', username)  # Insert player_id as first column
+            df.insert(1, 'region', region)      # Insert region as second column
+
+        # Merge all DataFrames into one
+        merged_df = None
+        for name, df in dfs.items():
+            if merged_df is None:
+                merged_df = df
+            else:
+                # Drop common columns except player_id and region
+                common_cols = df.columns.intersection(merged_df.columns)
+                cols_to_drop = [col for col in common_cols if col not in ['player_id', 'region']]
+                df_to_merge = df.drop(columns=cols_to_drop, errors='ignore')
+                merged_df = pd.merge(merged_df, df_to_merge, on=['player_id', 'region'], how='outer')
+
+        # Ensure player_id and region are the first columns in final order
+        if merged_df is not None and not merged_df.empty:
+            # Get all columns except player_id and region
+            other_cols = [col for col in merged_df.columns if col not in ['player_id', 'region']]
+            # Reorder columns with player_id and region first
+            merged_df = merged_df[['player_id', 'region'] + other_cols]
+
+        # # Save merged DataFrame
+        # save_dir = "util/data"
+        # os.makedirs(save_dir, exist_ok=True)
+        
+        # if merged_df is not None and not merged_df.empty:
+        #     filepath = os.path.join(save_dir, f"player_stats.csv")
+        #     merged_df.to_csv(filepath, index=False)
+        #     print(f"Saved player stats to {filepath}")
+
+        return merged_df, dfs
+
+    except Exception as e:
+        print(f"Error in get_player_stats: {e}")
+        return None, {}
+
+    finally:
+        if driver:
+            driver.quit()
+
+def get_multiple_player_stats(players_df):
+    """
+    Get stats for multiple players from a DataFrame
+    
+    Parameters:
+    players_df: DataFrame with columns 'region' and 'username'
+    """
+    all_merged_dfs = []
+    error_players = []
+    
+    save_dir = "util/data"
+    os.makedirs(save_dir, exist_ok=True)
+    checkpoint_file = os.path.join(save_dir, "player_stats_checkpoint.csv")
+    all_merged_dfs = []
+    error_players = []
+    
+    # Load checkpoint if exists
+    start_idx = 0
+    if os.path.exists(checkpoint_file):
+        try:
+            checkpoint_df = pd.read_csv(checkpoint_file)
+            all_merged_dfs = [checkpoint_df]
+            # Get the number of players already processed
+            processed_players = set(checkpoint_df['player_id'])
+            # Filter out already processed players
+            players_df = players_df[~players_df['username'].isin(processed_players)]
+            print(f"Loaded checkpoint with {len(processed_players)} players already processed")
+        except Exception as e:
+            print(f"Error loading checkpoint: {e}")
+
+    print(f"Processing {len(players_df)} remaining players...")
+    
+    for idx, row in players_df.iterrows():
+        region = row['region'].lower()  # Ensure region is lowercase
+        username = row['username']
+        
+        try:
+            # Format the username
+            formatted_username = format_summoner_name(username)
+            print(f"\nProcessing player {idx + 1}/{len(players_df)}: {username} ({region})")
+            print(f"Formatted username: {formatted_username}")
+            
+            # Add delay between requests
+            if idx > 0:
+                time.sleep(2)
+                
+            merged_df, _ = get_player_stats(region, formatted_username)
+            if merged_df is not None and not merged_df.empty:
+                # Store original username in the DataFrame
+                merged_df['player_id'] = username  # Store original username
+                all_merged_dfs.append(merged_df)
+                print(f"Successfully processed {username}")
+
+                # Save checkpoint every 10 players
+                if len(all_merged_dfs) % 10 == 0:
+                    checkpoint_save = pd.concat(all_merged_dfs, ignore_index=True)
+                    checkpoint_save.to_csv(checkpoint_file, index=False)
+                    print(f"Saved checkpoint after processing {len(all_merged_dfs)} players")
+
+            else:
+                print(f"No data found for {username}")
+                error_players.append({
+                    'region': region,
+                    'username': username,
+                    'formatted_username': formatted_username,
+                    'error': 'No data found'
+                })
+                
+        except Exception as e:
+            print(f"Error processing {username}: {e}")
+            error_players.append({
+                'region': region,
+                'username': username,
+                'formatted_username': formatted_username if 'formatted_username' in locals() else 'Error in formatting',
+                'error': str(e)
+            })
+            continue
+
+    # Combine and save final results
+    if all_merged_dfs:
+        final_df = pd.concat(all_merged_dfs, ignore_index=True)
+        
+        # Save final combined stats
+        filepath = os.path.join(save_dir, "player_stats.csv")
+        final_df.to_csv(filepath, index=False)
+        print(f"\nSaved combined stats for {len(all_merged_dfs)} players to {filepath}")
+        
+        # Clean up checkpoint file
+        if os.path.exists(checkpoint_file):
+            os.remove(checkpoint_file)
+            print("Removed checkpoint file after successful completion")
+        
+        # Save error log
+        if error_players:
+            error_df = pd.DataFrame(error_players)
+            error_filepath = os.path.join(save_dir, "player_stats_errors.csv")
+            error_df.to_csv(error_filepath, index=False)
+            print(f"Saved error log to {error_filepath}")
+        
+        return final_df
+    else:
+        print("\nNo player data was collected")
+        return None

util/Recent_match_scrapper.py

@@ -0,0 +1,393 @@
+import time, os
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+import pandas as pd
+from urllib.parse import unquote
+from webdriver_manager.chrome import ChromeDriverManager
+from helper import convert_to_minutes, convert_percentage_to_decimal, convert_tier_to_number, convert_result_to_binary, format_summoner_name, convert_to_displayname
+
+def setup_driver():
+    options = Options()
+    prefs = {
+        'profile.default_content_setting_values': {'notifications': 2},
+        'profile.managed_default_content_settings': {'images': 2}
+    }
+    options.add_experimental_option('prefs', prefs)
+    options.add_experimental_option('excludeSwitches', ['enable-logging'])
+    for arg in ['--headless', '--no-sandbox', '--disable-dev-shm-usage', 
+                '--disable-gpu', '--window-size=1920,1080']:
+        options.add_argument(arg)
+    options.add_argument('user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/91.0.4472.124')
+    return webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
+
+def get_tooltip_date(driver, element):
+    try:
+        driver.execute_script("""
+            arguments[0].scrollIntoView({block: 'center'});
+            document.querySelectorAll('span.react-tooltip-lite').forEach(e => e.remove());
+            arguments[0].dispatchEvent(new MouseEvent('mouseover', {
+                view: window, bubbles: true, cancelable: true
+            }));
+        """, element)
+        time.sleep(0.3)
+        return driver.execute_script("""
+            return Array.from(document.querySelectorAll('span.react-tooltip-lite'))
+                .find(t => t.offsetParent !== null)?.textContent || null;
+        """)
+    except: return None
+    
+def extract_match_data(match):
+    selectors = {
+        'time_stamp': "div.time-stamp > div",
+        'game_type': "div.game-type",
+        'result': "div.result",
+        'length': "div.length",
+        'kda': "div.kda",
+        'kda_ratio': "div.kda-ratio",
+        'cs': "div.cs",
+        'avg_tier': "div.avg-tier",
+        'laning': "div.laning",
+        'kill_participation': "div.p-kill",
+        'champion_img': "div.info a.champion img",
+        'champion_level': "div.info a.champion span.champion-level"
+    }
+    
+    data = {}
+    try:
+        for key, selector in selectors.items():
+            element = match.find_element(By.CSS_SELECTOR, selector)
+            if key == 'champion_img':
+                data[key] = element.get_attribute('alt')
+            elif key == 'laning':
+                data[key] = element.text.replace('\n', '')  # Remove newlines from laning data
+            else:
+                data[key] = element.text
+    except Exception as e:
+        print(f"Error extracting match data: {e}")
+    return data
+
+def get_players_info(match):
+    try:
+        players = []
+        player_elements = match.find_elements(By.CSS_SELECTOR, "div.css-pp7uqb.e1xevas21")[:10]
+        for player in player_elements:
+            champion = player.find_element(By.CSS_SELECTOR, "div.icon img").get_attribute("alt")
+            href = player.find_element(By.CSS_SELECTOR, "div.name a").get_attribute("href")
+            region, name = href.split('/')[-2:]
+            # Decode the URL-encoded name
+            decoded_name = unquote(name)
+            #print(f"Found player: {decoded_name} with champion {champion}")
+            players.append({
+                "champion": champion, 
+                "region": region, 
+                "name": decoded_name
+            })
+        return players
+    except Exception as e:
+        print(f"Error getting players info: {e}")
+        return []
+
+def convert_laning_ratio(laning_str):
+    """Convert laning string (e.g., 'Laning 70:30') to decimal ratio"""
+    try:
+        # Extract the ratio part (e.g., '70:30' from 'Laning 70:30')
+        ratio_part = laning_str.split('Laning')[-1].strip()
+        
+        # Split by ':' and convert to numbers
+        first_num, second_num = map(int, ratio_part.split(':'))
+        
+        # Calculate ratio
+        if second_num != 0:  # Avoid division by zero
+            ratio = round(first_num / second_num, 2)
+            return ratio
+        return 0.0
+    except Exception as e:
+        print(f"Laning conversion error for '{laning_str}': {e}")
+        return 0.0
+    
+def extract_cs_number(cs_str):
+    """Extract pure CS number from string (e.g., 'CS 123 (7.9)' -> 123)"""
+    try:
+        # Extract first number from the string
+        cs_number = ''.join(filter(str.isdigit, cs_str.split('(')[0]))
+        return int(cs_number) if cs_number else 0
+    except:
+        return 0
+    
+def extract_cs_per_min(cs_str):
+    """Extract CS per minute from string (e.g., 'CS 123 (7.9)' -> 7.9)"""
+    try:
+        # Extract number between parentheses
+        cs_per_min = cs_str.split('(')[1].split(')')[0]
+        return float(cs_per_min)
+    except:
+        return 0.0
+
+def process_match_data(match_data, username, players):
+    try:
+        # Format username for comparison - ensure it's in display format
+        display_name = convert_to_displayname(username)
+        #print(f"\nInput username: {username}")
+        #print(f"Converted display name: {display_name}")
+        
+        # # Debug print all players and their converted names
+        # print("\nAll players:")
+        # for p in players:
+        #     orig_name = p['name']
+        #     conv_name = convert_to_displayname(orig_name)
+        #     print(f"Original: {orig_name} -> Converted: {conv_name}")
+        
+        # Find player index using normalized comparison
+        player_index = next((i for i, p in enumerate(players) 
+                           if convert_to_displayname(p['name']).lower().replace(' ', '') == 
+                           display_name.lower().replace(' ', '')), -1)
+        
+        if player_index == -1:
+            print(f"\nWarning: Player {display_name} not found in players list")
+            print("Available players:", [convert_to_displayname(p['name']) for p in players])
+            return None
+            
+        #print(f"\nFound player at index: {player_index}")
+        team = "blue" if player_index < 5 else "red"
+        #print(f"Team: {team}")
+            
+        
+        # Modify how teammates and opponents are filtered
+        if player_index < 5:
+            # Player is on blue team
+            teammates = [p for i, p in enumerate(players[:5]) 
+                       if i != player_index]  # Use index comparison instead of name
+            opponents = players[5:]  # All red team players
+        else:
+            # Player is on red team
+            teammates = [p for i, p in enumerate(players[5:]) 
+                       if i != (player_index - 5)]  # Adjust index for red team
+            opponents = players[:5]  # All blue team players
+        
+        kda_parts = match_data.get('kda', '0/0/0').strip().split('/')
+        kills, deaths, assists = [kda_parts[i] if i < len(kda_parts) else "0" for i in range(3)]
+        kda_ratio = match_data.get("kda_ratio", "0").strip().replace(":1 KDA", "")
+
+        kill_participation = convert_percentage_to_decimal(match_data.get("kill_participation", "0%"))
+
+        laning_ratio = convert_laning_ratio(match_data.get("laning", "0:0"))
+
+        cs = extract_cs_number(match_data.get("cs", "0"))
+        cpm = extract_cs_per_min(match_data.get("cs", "0"))
+
+        match_length_str = match_data.get("length", "0m 0s")
+        match_length_mins = convert_to_minutes(match_length_str)
+
+        # Convert tier to number
+        avg_tier_num = convert_tier_to_number(match_data.get("avg_tier", ""))
+
+        result_num = convert_result_to_binary(match_data.get("result", ""))
+        
+        match_row = {
+            "player_id": display_name,  # Use display_name here
+            "date": match_data.get("match_date", ""),
+            "champion": match_data.get("champion_img", ""),
+            "level": match_data.get("champion_level", ""),
+            "team": team,
+            "result": result_num,
+            "match_length_mins": match_length_mins, 
+            "kill": kills.strip(),
+            "death": deaths.strip(),
+            "assist": assists.strip(),
+            "kda_ratio": kda_ratio,
+            "kill_participation": kill_participation,
+            "laning": laning_ratio,
+            "cs": cs,
+            "cs_per_min": cpm,
+            "avg_tier": avg_tier_num
+        }
+        
+        # Add teammates and opponents with display format
+        for i, (team_list, prefix) in enumerate([(teammates, "team"), (opponents, "opp")]):
+            for j, player in enumerate(team_list, 1):
+                if j <= 5:  # Ensure we don't exceed 5 players per team
+                    match_row[f"{prefix}mates{j}"] = convert_to_displayname(player["name"])
+                    match_row[f"{prefix}_champ{j}"] = player["champion"]
+                
+        return match_row
+    except Exception as e:
+        print(f"Error processing match: {e}")
+        return None
+
+def get_matches_stats(region, username, max_retries=2):
+    """
+    Get match stats for a single player with retry mechanism
+    """
+    driver = None
+    retry_count = 0
+    
+    while retry_count <= max_retries:
+        try:
+            driver = setup_driver()
+            driver.set_page_load_timeout(20)  # Set page load timeout
+            
+            url = f"https://www.op.gg/summoners/{region}/{username}?queue_type=SOLORANKED"
+            print(f"Accessing URL: {url}")
+            driver.get(url)
+            
+            matches_container = WebDriverWait(driver, 20).until(
+                EC.presence_of_element_located((By.CSS_SELECTOR, "div.css-1jxewmm.ek41ybw0"))
+            )
+            
+            matches_data = []
+            match_elements = matches_container.find_elements(By.CSS_SELECTOR, "div.css-j7qwjs.ery81n90")
+            
+            #print(f"Found {len(match_elements)} matches")
+            
+            for i, match in enumerate(match_elements, 1):
+                try:
+                    match_data = extract_match_data(match)
+                    players = get_players_info(match)
+                    match_data['match_date'] = get_tooltip_date(
+                        driver, 
+                        match.find_element(By.CSS_SELECTOR, "div.time-stamp > div")
+                    )
+                    
+                    processed_data = process_match_data(match_data, username, players)
+                    if processed_data:
+                        matches_data.append(processed_data)
+                except Exception as e:
+                    print(f"Error processing match {i}: {e}")
+                    continue
+            
+            if matches_data:
+                return pd.DataFrame(matches_data)
+            else:
+                raise Exception("No valid matches found")
+                
+        except Exception as e:
+            retry_count += 1
+            print(f"Attempt {retry_count} failed: {e}")
+            if retry_count <= max_retries:
+                print(f"Retrying... ({retry_count}/{max_retries})")
+                time.sleep(5)  # Wait 5 seconds before retrying
+            else:
+                print(f"Max retries reached")
+                return pd.DataFrame()
+                
+        finally:
+            if driver:
+                driver.quit()
+    
+    return pd.DataFrame()
+
+def get_multiple_matches_stats(players_df):
+    """
+    Get match stats for multiple players from a DataFrame
+    
+    Parameters:
+    players_df: DataFrame with columns 'region' and 'username'
+    """
+    save_dir = "util/data"
+    os.makedirs(save_dir, exist_ok=True)
+    checkpoint_file = os.path.join(save_dir, "recent_matches_checkpoint.csv")
+    all_matches_dfs = []
+    error_players = []
+    
+    # Load checkpoint if exists
+    start_idx = 0
+    if os.path.exists(checkpoint_file):
+        try:
+            checkpoint_df = pd.read_csv(checkpoint_file)
+            all_matches_dfs = [checkpoint_df]
+            # Get the number of players already processed
+            processed_players = set(checkpoint_df['player_id'])
+            # Filter out already processed players
+            players_df = players_df[~players_df['username'].isin(processed_players)]
+            print(f"Loaded checkpoint with {len(processed_players)} players already processed")
+        except Exception as e:
+            print(f"Error loading checkpoint: {e}")
+    
+    print(f"Processing matches for {len(players_df)} remaining players...")
+    
+    for idx, row in players_df.iterrows():
+        region = row['region'].lower()  # Ensure region is lowercase
+        username = row['username']
+        
+        try:
+            # Format the username
+            formatted_username = format_summoner_name(username)
+            print(f"\nProcessing matches for player {idx + 1}/{len(players_df)}: {username} ({region})")
+            #print(f"Formatted username: {formatted_username}")
+            
+            # Add delay between requests
+            if idx > 0:
+                time.sleep(2)
+                
+            matches_df = get_matches_stats(region, formatted_username)
+            
+            if matches_df is not None and not matches_df.empty:
+                # Add player identification columns
+                matches_df['player_id'] = username  # Original username
+                matches_df['region'] = region
+                all_matches_dfs.append(matches_df)
+                print(f"Successfully processed matches for {username}")
+                #print(f"Found {len(matches_df)} matches")
+
+                 # Save checkpoint every 5 players
+                if len(all_matches_dfs) % 5 == 0:
+                    checkpoint_save = pd.concat(all_matches_dfs, ignore_index=True)
+                    checkpoint_save.to_csv(checkpoint_file, index=False)
+                    print(f"Saved checkpoint after processing {len(all_matches_dfs)} players")
+
+            else:
+                print(f"No match data found for {username}")
+                error_players.append({
+                    'region': region,
+                    'username': username,
+                    'formatted_username': formatted_username,
+                    'error': 'No match data found'
+                })
+                
+        except Exception as e:
+            print(f"Error processing matches for {username}: {e}")
+            error_players.append({
+                'region': region,
+                'username': username,
+                'formatted_username': formatted_username if 'formatted_username' in locals() else 'Error in formatting',
+                'error': str(e)
+            })
+            continue
+
+    # Combine all match stats
+    if all_matches_dfs:
+        final_df = pd.concat(all_matches_dfs, ignore_index=True)
+                
+        filepath = os.path.join(save_dir, f"recent_matches.csv")
+        final_df.to_csv(filepath, index=False)
+        print(f"\nSaved combined match stats for {len(all_matches_dfs)} players to {filepath}")
+
+        # Clean up checkpoint file
+        if os.path.exists(checkpoint_file):
+            os.remove(checkpoint_file)
+            print("Removed checkpoint file after successful completion")
+        
+        # Save error log if any errors occurred
+        if error_players:
+            error_df = pd.DataFrame(error_players)
+            error_filepath = os.path.join(save_dir, f"recent_matches_error.csv")
+            error_df.to_csv(error_filepath, index=False)
+            print(f"Saved error log to {error_filepath}")
+        
+        # Print summary
+        print("\nSummary:")
+        print(f"Total players processed: {len(players_df)}")
+        print(f"Successful: {len(all_matches_dfs)}")
+        print(f"Failed: {len(error_players)}")
+        print(f"Total matches collected: {len(final_df)}")
+        
+        return final_df
+    else:
+        print("\nNo match data was collected")
+        return None
+    
+

util/Weekly_meta_scrapper.py

@@ -0,0 +1,92 @@
+import os
+import pandas as pd
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from helper import convert_percentage_to_decimal
+
+def setup_driver():
+    """Setup and return a configured Chrome WebDriver with optimized settings"""
+    chrome_options = Options()
+    chrome_options.add_argument("--headless")
+    chrome_options.add_argument("--no-sandbox")
+    chrome_options.add_argument("--disable-dev-shm-usage")
+    chrome_options.add_argument("--disable-gpu")
+    chrome_options.add_argument("--disable-extensions")
+    chrome_options.add_argument("--disable-logging")
+    chrome_options.add_argument("--log-level=3")
+    chrome_options.add_argument("--silent")
+    chrome_options.page_load_strategy = 'eager'
+    chrome_options.add_argument(
+        "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    )
+    
+    service = Service(ChromeDriverManager().install())
+    return webdriver.Chrome(service=service, options=chrome_options)
+
+def get_weekly_meta():
+    BASE_URL = "https://www.op.gg/statistics/champions?tier=challenger&period=week&mode=ranked"
+    driver = setup_driver()
+    
+    try:
+        driver.get(BASE_URL)
+        table = WebDriverWait(driver, 20).until(
+            EC.presence_of_element_located((By.CSS_SELECTOR, "#content-container > div:nth-child(2) > table"))
+        )
+        
+        # Extract table rows
+        rows = table.find_elements(By.TAG_NAME, "tr")
+        
+        # Define the column order
+        columns = ["rank", "champion", "games", "KDA", "WR", "pick", "ban", "cs", "gold"]
+        
+        data = []
+        for row in rows[1:]:  # Skip the header row
+            cells = row.find_elements(By.TAG_NAME, "td")
+            row_data = [cell.text for cell in cells]
+            
+            if len(row_data) >= len(columns):
+                # Remove ":1" from KDA format
+                row_data[3] = row_data[3].replace(":1", "")
+                # Convert WR, pick, and ban percentages to decimals
+                row_data[4] = convert_percentage_to_decimal(row_data[4])
+                row_data[5] = convert_percentage_to_decimal(row_data[5])
+                row_data[6] = convert_percentage_to_decimal(row_data[6])
+                # Remove commas from the gold values
+                row_data[8] = int(row_data[8].replace(",", ""))
+                
+                data.append(row_data[:len(columns)])
+        
+        # Create a DataFrame with the extracted data
+        df = pd.DataFrame(data, columns=columns)
+        
+        # Ensure the directory exists
+        os.makedirs('./util/data', exist_ok=True)
+        
+        # Define the save path
+        save_path = "./util/data/weekly_meta_stats.csv"
+        
+        # Automatically save the DataFrame to a CSV file in the specified directory
+        df.to_csv(save_path, index=False)
+        
+        # Print confirmation message
+        print(f"Saved weekly meta to {save_path}")
+        
+        return df
+    
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+    
+    finally:
+        driver.quit()
+
+# if __name__ == "__main__":
+#     weekly_meta_data = get_weekly_meta()
+    
+#     if weekly_meta_data is not None:
+#         print(weekly_meta_data)

app.py → util/app.py

@@ -5,79 +5,124 @@ import requests
 import xgboost as xgb
 from huggingface_hub import hf_hub_download
 
+from Recent_match_scrapper import get_multiple_matches_stats
+from Meta_scrapper import get_meta_stats
+from Leaderboard_scrapper import scrape_leaderboards
+from connection_check import check_connection
+from helper import merge_stats, filter_leaderboard, get_player_list
+from Player_scrapper import get_multiple_player_stats, get_player_stats
+from feature_eng import create_champion_features
+from Weekly_meta_scrapper import get_weekly_meta
+from app_training_df_getter import create_app_user_training_df
+from sklearn.metrics import top_k_accuracy_score
+
+import os
+import time
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+import pandas as pd
+from helper import format_summoner_name
+
 # Download the model from Hugging Face Hub
 model_path = hf_hub_download(
     repo_id="ivwhy/champion-predictor-model",  # Replace with your model repo
     filename="champion_predictor.json"  # Replace with your model filename
+
 )
 model = xgb.Booster()
 model.load_model(model_path)
 
-# Rest of your code remains the same as before, but remove demo.launch()
 # Define your interface
 with gr.Blocks() as demo:
    # Assuming you have these helper functions implemented
-    def get_player_stats(player_name):
-        """Get player statistics from API"""
-        # Placeholder - implement actual API call
-        return {
-            'wins': 120,
-            'losses': 80,
-            'winrate': '60%',
-            'favorite_champions': ['Ahri', 'Zed', 'Yasuo']
-        }
+    def get_user_training_df(player_opgg_url):
+       
+       training_df = create_app_user_training_df(player_opgg_url)
 
-    def get_recent_matches(player_name):
-        """Get recent match history"""
-        # Placeholder - implement actual API call
-        return pd.DataFrame({
-            'champion': ['Ahri', 'Zed', 'Yasuo'],
-            'result': ['Win', 'Loss', 'Win'],
-            'kda': ['8/2/10', '4/5/3', '12/3/7']
-        })
+       return training_df
+   
 
-    def prepare_features(player_name, champions):
-        """Prepare features for model prediction"""
-        # Placeholder - implement actual feature engineering
-        features = []  # Transform champions into model features
-        return pd.DataFrame([features])
 
     # Load the model from Hugging Face
     model = xgb.Booster()  # Initialize model
-    #model.load_model(" ")  # Load your model
+    model.load_model("ivwhy/champion-predictor-model")  # Load your model
 
-    # Define champion list for dropdowns
+        # Define champion list for dropdowns
     CHAMPIONS = [
-        "Aatrox", "Ahri", "Akali", "Alistar", "Amumu",
-        # Add more champions...
+        "Aatrox", "Ahri", "Akali", "Akshan", "Alistar", "Amumu", "Anivia", "Annie", "Aphelios", "Ashe",
+        "Aurelion Sol", "Azir", "Bard", "Bel'Veth", "Blitzcrank", "Brand", "Braum", "Caitlyn", "Camille",
+        "Cassiopeia", "Cho'Gath", "Corki", "Darius", "Diana", "Dr. Mundo", "Draven", "Ekko", "Elise",
+        "Evelynn", "Ezreal", "Fiddlesticks", "Fiora", "Fizz", "Galio", "Gangplank", "Garen", "Gnar",
+        "Gragas", "Graves", "Gwen", "Hecarim", "Heimerdinger", "Illaoi", "Irelia", "Ivern", "Janna",
+        "Jarvan IV", "Jax", "Jayce", "Jhin", "Jinx", "Kai'Sa", "Kalista", "Karma", "Karthus", "Kassadin",
+        "Katarina", "Kayle", "Kayn", "Kennen", "Kha'Zix", "Kindred", "Kled", "Kog'Maw", "KSante", "LeBlanc",
+        "Lee Sin", "Leona", "Lillia", "Lissandra", "Lucian", "Lulu", "Lux", "Malphite", "Malzahar", "Maokai",
+        "Master Yi", "Milio", "Miss Fortune", "Mordekaiser", "Morgana", "Naafiri", "Nami", "Nasus", "Nautilus",
+        "Neeko", "Nidalee", "Nilah", "Nocturne", "Nunu & Willump", "Olaf", "Orianna", "Ornn", "Pantheon",
+        "Poppy", "Pyke", "Qiyana", "Quinn", "Rakan", "Rammus", "Rek'Sai", "Rell", "Renata Glasc", "Renekton",
+        "Rengar", "Riven", "Rumble", "Ryze", "Samira", "Sejuani", "Senna", "Seraphine", "Sett", "Shaco",
+        "Shen", "Shyvana", "Singed", "Sion", "Sivir", "Skarner", "Sona", "Soraka", "Swain", "Sylas",
+        "Syndra", "Tahm Kench", "Taliyah", "Talon", "Taric", "Teemo", "Thresh", "Tristana", "Trundle",
+        "Tryndamere", "Twisted Fate", "Twitch", "Udyr", "Urgot", "Varus", "Vayne", "Veigar", "Vel'Koz",
+        "Vex", "Vi", "Viego", "Viktor", "Vladimir", "Volibear", "Warwick", "Wukong", "Xayah", "Xerath",
+        "Xin Zhao", "Yasuo", "Yone", "Yorick", "Yuumi", "Zac", "Zed", "Zeri", "Ziggs", "Zilean", "Zoe", "Zyra"
     ]
 
-    def show_stats(player_name):
+
+    def show_stats(player_opgg_url):
         """Display player statistics and recent matches"""
-        if not player_name:
-            return "Please enter a player name", None
-        
-        stats = get_player_stats(player_name)
-        recent = get_recent_matches(player_name)
+        if not player_opgg_url:
+            return "Please enter a player link to OPGG", None
         
+        training_features = get_user_training_df(player_opgg_url)
+
+        # Assume `training_features` is the DataFrame provided
+        # Calculate total wins and losses
+        wins = training_features['result'].sum()
+        losses = len(training_features) - wins
+
+        # Calculate winrate
+        winrate = f"{(wins / len(training_features)) * 100:.0f}%"
+
+        # Calculate favorite champions
+        favorite_champions = (
+            training_features['champion']
+            .value_counts()
+            .head(3)
+            .index.tolist()
+        )
+
+        # Create the summary dictionary
+        summary_data = {
+            'wins': wins,
+            'losses': losses,
+            'winrate': winrate,
+            'favorite_champions': favorite_champions
+        }
+
         stats_html = f"""
         <div style='padding: 20px; background: #f5f5f5; border-radius: 10px;'>
-            <h3>Player Stats: {player_name}</h3>
+            <h3>Player Stats: {player_opgg_url}</h3>
             <p>Wins: {stats['wins']} | Losses: {stats['losses']}</p>
             <p>Winrate: {stats['winrate']}</p>
             <p>Favorite Champions: {', '.join(stats['favorite_champions'])}</p>
         </div>
         """
         
-        return stats_html, recent.to_html(index=False)
+        return stats_html
 
-    def predict_champion(player_name, *champions):
+    def predict_champion(player_opgg_url, *champions):
         """Make prediction based on selected champions"""
-        if not player_name or None in champions:
+        if not player_opgg_url or None in champions:
             return "Please fill in all fields"
         
         # Prepare features
-        features = prepare_features(player_name, champions)
+        features = get_user_training_df(player_opgg_url, champions)
         
         # Make prediction
         prediction = model.predict(features)
@@ -92,7 +137,7 @@ with gr.Blocks() as demo:
         gr.Markdown("# League of Legends Champion Prediction")
         
         with gr.Row():
-            player_name = gr.Textbox(label="Player Name")
+            player_opgg_url = gr.Textbox(label="OPGG Player URL")
             show_button = gr.Button("Show Stats")
         
         with gr.Row():
@@ -112,15 +157,49 @@ with gr.Blocks() as demo:
         # Set up event handlers
         show_button.click(
             fn=show_stats,
-            inputs=[player_name],
+            inputs=[player_opgg_url],
             outputs=[stats_output, recent_matches]
         )
         
         predict_button.click(
             fn=predict_champion,
-            inputs=[player_name] + champion_dropdowns,
+            inputs=[player_opgg_url] + champion_dropdowns,
             outputs=prediction_output
         )
 
 # Add this line at the end
-demo.queue()  # Enable queuing for better handling of multiple users
+demo.queue()  # Enable queuing for better handling of multiple users
+
+
+
+
+''' code graveyard
+
+    def get_player_stats(player_opgg_url):
+        """Get player statistics from API"""
+        # Placeholder - implement actual API call
+
+        return {
+            'wins': 120,
+            'losses': 80,
+            'winrate': '60%',
+            'favorite_champions': ['Ahri', 'Zed', 'Yasuo']
+        }
+
+    def get_recent_matches(player_opgg_url):
+        """Get recent match history"""
+        # Placeholder - implement actual API call
+        return pd.DataFrame({
+            'champion': ['Ahri', 'Zed', 'Yasuo'],
+            'result': ['Win', 'Loss', 'Win'],
+            'kda': ['8/2/10', '4/5/3', '12/3/7']
+        })
+
+    def prepare_features(player_opgg_url, champions):
+        """Prepare features for model prediction"""
+        # Placeholder - implement actual feature engineering
+        features = []  # Transform champions into model features
+        return pd.DataFrame([features])
+   
+
+'''

util/app_training_df_getter.py

@@ -0,0 +1,388 @@
+from Recent_match_scrapper import get_multiple_matches_stats, get_matches_stats
+import os
+import pandas as pd
+import numpy as np
+from Meta_scrapper import get_meta_stats
+from Leaderboard_scrapper import scrape_leaderboards
+from connection_check import check_connection
+from helper import merge_stats, filter_leaderboard, get_player_list, process_kda_perfect, ChampionConverter
+from Player_scrapper import get_multiple_player_stats, get_player_stats
+from feature_eng import create_champion_features
+from Weekly_meta_scrapper import get_weekly_meta
+import pandas as pd
+import re
+
+# ============================================ my functions =========================================================
+
+
+def create_champion_features_and_return_df(merged_player_stats=None, meta_stats=None, weekly_meta=None, debug=None, consider_team_comp=True, test_mode=False):
+    """
+    Create features for champion prediction using player data.
+    Champion names will be used as column headers.
+    Uses pd.concat to avoid DataFrame fragmentation.
+    """
+    try:
+        if merged_player_stats is None:
+            print("Loading merged player stats...")
+            input_file = os.path.join("util", "data", "player_stats_merged.csv")
+            merged_player_stats = pd.read_csv(input_file, low_memory=False)
+            
+        #processing kda value
+        merged_player_stats = process_kda_perfect(merged_player_stats)
+   
+
+        if test_mode:
+            print("Test mode: Using only first 100 rows")
+            merged_player_stats = merged_player_stats.head(100)
+        
+        if meta_stats is None:
+            print("Loading meta stats...")
+            meta_file = os.path.join("util", "data", "meta_stats.csv")
+            meta_stats = pd.read_csv(meta_file, low_memory=False)
+
+        if weekly_meta is None:
+            print("Loading weekly meta stats...")
+            weekly_file = os.path.join("util", "data", "weekly_meta_stats.csv")
+            weekly_meta = pd.read_csv(weekly_file, low_memory=False)
+        
+        
+        # Initialize variables
+        debug_data = []
+        original_columns = merged_player_stats.columns.tolist()
+        feature_dict = {}
+
+        # Copy original columns
+        for col in merged_player_stats.columns:
+            feature_dict[col] = merged_player_stats[col].values.copy()
+
+
+        # Initialize the champion converter
+        converter = ChampionConverter()
+        all_champions = converter.champions
+        #total_champions = len(converter.champions)
+        
+            
+
+        # Get low tier champions and counter information
+        tier_penalties = {3: 0.9, 4: 0.85, 5: 0.8}
+
+        # Create tier_map as a dictionary of lists
+        tier_map = {}
+        for _, row in meta_stats.iterrows():
+            champ = row['champion']
+            tier = row['tier']
+            if pd.notna(tier):
+                if champ in tier_map:
+                    tier_map[champ].append(tier)
+                else:
+                    tier_map[champ] = [tier]
+
+        counter_map = {}
+        for _, row in meta_stats.iterrows():
+            if pd.notna(row['counter1']):
+                champ = row['champion']
+                counters = [row['counter1'], row['counter2'], row['counter3']]
+                if champ in counter_map:
+                    counter_map[champ].extend([c for c in counters if pd.notna(c)])
+                else:
+                    counter_map[champ] = [c for c in counters if pd.notna(c)]
+
+        # Ensure unique counters and remove duplicates
+        for champ, counters in counter_map.items():
+            counter_map[champ] = list(set(counters))
+
+        # Move 'champion' column to the first position
+        cols = ['champion'] + [col for col in merged_player_stats if col != 'champion']
+        merged_player_stats = merged_player_stats[cols]
+
+        # Define importance weights
+        weights = {
+            'recent': 0.3,    # Last 20 games
+            'weekly': 0.4,    # Last 7 days
+            'meta': 0.2,      # Only from weekly_stats
+            'season': 0.06,   # Current season
+            'mastery': 0.04   # All-time mastery
+        }
+
+        # Process rows in batches
+        batch_size = 100
+        total_rows = len(merged_player_stats)
+        
+        print(f"Total rows: {total_rows}")
+
+        for batch_start in range(0, total_rows, batch_size):
+            batch_end = min(batch_start + batch_size, total_rows)
+            batch_rows = merged_player_stats.iloc[batch_start:batch_end]
+            print(f"\nProcessing rows {batch_start} to {batch_end} ({batch_start/total_rows*100:.2f}% complete)")
+
+            # Initialize batch scores dictionary
+            batch_scores = {champion: np.zeros(len(batch_rows)) for champion in all_champions}
+            
+            # Process each row in this batch
+            for batch_idx, (idx, row) in enumerate(batch_rows.iterrows()):
+                # Process each champion for this row
+                for champion in all_champions:
+                    # Initialize scores for this champion and row
+                    champion_scores = {
+                        'recent_score': 0,
+                        'weekly_score': 0,
+                        'meta_score': 0,
+                        'season_score': 0,
+                        'mastery_score': 0
+                    }
+
+                    # Store debug info if needed
+                    base_score_before_penalty = 0
+                    counter_penalty = 0
+                    counter_debug = []
+
+                    # 1. Recent Performance
+                    for i in range(1, 4):
+                        if row.get(f'most_champ_{i}') == champion:
+                            wr = float(row[f'WR_{i}']) if pd.notna(row[f'WR_{i}']) else 0
+                            kda = float(row[f'KDA_{i}']) if pd.notna(row[f'KDA_{i}']) else 0
+                            wins = float(row[f'W_{i}']) if pd.notna(row[f'W_{i}']) else 0
+                            losses = float(row[f'L_{i}']) if pd.notna(row[f'L_{i}']) else 0
+                            games = wins + losses
+                            total_games = float(row['total_games']) if pd.notna(row['total_games']) else 20
+                            
+                            performance_quality = (
+                                (wr * 0.7) +
+                                (min(kda, 10) / 10 * 0.3)
+                            )
+                            
+                            games_factor = min(games / 5, 1.0)
+                            games_ratio = games / total_games
+                            
+                            if games >= 5:
+                                if performance_quality < 0.4:
+                                    performance_quality *= 0.8
+                                elif performance_quality > 0.7:
+                                    performance_quality *= 1.2
+                            
+                            champion_scores['recent_score'] = (
+                                performance_quality * (0.7 + (0.3 * games_factor))
+                            ) * (1 + games_ratio * 0.2)
+                            break  # Exit loop once found
+                    
+                    # 2. Weekly Performance
+                    for i in range(1, 4):
+                        if row.get(f'7d_champ_{i}') == champion:
+                            weekly_wins = float(row[f'7d_W_{i}']) if pd.notna(row[f'7d_W_{i}']) else 0
+                            weekly_losses = float(row[f'7d_L_{i}']) if pd.notna(row[f'7d_L_{i}']) else 0
+                            weekly_games = float(row[f'7d_total_{i}']) if pd.notna(row[f'7d_total_{i}']) else 0
+                            weekly_wr = float(row[f'7d_WR_{i}']) if pd.notna(row[f'7d_WR_{i}']) else 0
+                            profile_wr = float(row['win_rate']) if pd.notna(row['win_rate']) else 0.5
+                            
+                            if weekly_games > 0:
+                                wr_trend = (weekly_wr - profile_wr) / profile_wr if profile_wr > 0 else 0
+                                weekly_intensity = min(weekly_games / 10, 1.0)
+                                win_ratio = weekly_wins / weekly_games if weekly_games > 0 else 0
+                                
+                                weekly_performance = (
+                                    (weekly_wr * 0.4) +
+                                    (max(min(wr_trend, 1), -1) * 0.2) +
+                                    (weekly_intensity * 0.2) +
+                                    (win_ratio * 0.2)
+                                )
+                                
+                                if weekly_games >= 5:
+                                    if weekly_performance < 0.4:
+                                        weekly_performance *= 0.8
+                                    elif weekly_performance > 0.7:
+                                        weekly_performance *= 1.2
+                                
+                                champion_scores['weekly_score'] = weekly_performance * (
+                                    0.7 + (0.3 * min(weekly_games / 5, 1.0))
+                                )
+                                break  # Exit loop once found
+
+                    # 3. Meta Score
+                    if champion in weekly_meta['champion'].values:
+                        weekly_row = weekly_meta[weekly_meta['champion'] == champion].iloc[0]
+                        rank = weekly_row['rank']
+                        games = weekly_row['games']
+                        pick_rate = weekly_row['pick']
+                        ban_rate = weekly_row['ban']
+                        
+                        weight = (
+                            1 / rank * 0.5 +
+                            games / 100 * 0.3 +
+                            pick_rate * 0.1 -
+                            ban_rate * 0.1
+                        )
+                        
+                        champion_scores['meta_score'] = weight
+
+                    # 4. Season Performance
+                    for i in range(1, 8):
+                        if row.get(f'season_champ_{i}') == champion:
+                            wr = float(row[f'wr_ssn_{i}']) if pd.notna(row[f'wr_ssn_{i}']) else 0
+                            games = float(row[f'games_ssn_{i}']) if pd.notna(row[f'games_ssn_{i}']) else 0
+                            kda = float(row[f'kda_ssn_{i}']) if pd.notna(row[f'kda_ssn_{i}']) else 0
+                            
+                            champion_scores['season_score'] = (
+                                wr * 0.7 +
+                                (kda / 10) * 0.3 
+                            ) * (games / 100)
+                            break  # Exit loop once found
+                    
+                    # 5. Mastery Score
+                    for i in range(1, 17):
+                        if row.get(f'mastery_champ_{i}') == champion:
+                            mastery = float(row[f'm_lv_{i}']) if pd.notna(row[f'm_lv_{i}']) else 0            
+                            champion_scores['mastery_score'] = mastery / 7
+                            break  # Exit loop once found
+
+                    # Calculate base score for this champion and row
+                    base_score = (
+                        champion_scores['recent_score'] * weights['recent'] +
+                        champion_scores['weekly_score'] * weights['weekly'] +
+                        champion_scores['meta_score'] * weights['meta'] +
+                        champion_scores['season_score'] * weights['season'] +
+                        champion_scores['mastery_score'] * weights['mastery']
+                    )
+
+                    
+                    # Store the pre-penalty score for debugging
+                    base_score_before_penalty = base_score
+
+                    # Apply tier penalties
+                    if champion in tier_map:
+                        highest_tier = min(tier_map[champion])
+                        if highest_tier in tier_penalties:
+                            base_score *= tier_penalties[highest_tier]
+
+                    # Process team composition and counter penalties
+                    if consider_team_comp:
+                        # Check team champions
+                        for i in range(1, 5):
+                            team_col = f'team_champ{i}'
+                            if team_col in row and pd.notna(row[team_col]):
+                                if row[team_col] == champion:
+                                    base_score = 0
+                                    break
+                        
+                        # Only check opponents if base_score isn't already 0
+                        if base_score != 0:
+                            counter_penalty = 0
+                            counter_debug = []  # For debug information
+                            
+                            for i in range(1, 6):
+                                opp_col = f'opp_champ{i}'
+                                if opp_col in row and pd.notna(row[opp_col]):
+                                    opp_champ = row[opp_col]
+                                    if opp_champ == champion:
+                                        base_score = 0
+                                        break
+                                    if champion in counter_map and opp_champ in counter_map[champion]:
+                                        counter_penalty += 0.1
+                                        counter_debug.append(opp_champ)
+                            
+                            if counter_penalty > 0:
+                                base_score = base_score * (1 - counter_penalty)
+
+                    # Store the final score for this champion and row
+                    batch_scores[champion][batch_idx] = max(base_score, 0)
+
+                    # Collect debug data if this is the debug champion
+                    if debug == champion:
+                        counter_list = []
+                        for i in range(1, 6):
+                            opp_col = f'opp_champ{i}'
+                            if opp_col in row and pd.notna(row[opp_col]):
+                                if champion in counter_map and row[opp_col] in counter_map[champion]:
+                                    counter_list.append(row[opp_col])
+
+                        debug_row = {
+                            'champion': row['champion'],
+                            'recent_score': champion_scores['recent_score'],
+                            'weekly_score': champion_scores['weekly_score'],
+                            'meta_score': champion_scores['meta_score'],
+                            'base_score': base_score_before_penalty,
+                            'final_score': base_score,
+                            'counter_penalty': counter_penalty if consider_team_comp else 0,
+                            'final_score_actual': feature_dict[row['champion']][idx] if row['champion'] in feature_dict else base_score,
+                            'counter_list_debug': counter_list
+                        }
+                        debug_data.append(debug_row)
+
+            # Update feature_dict with batch results
+            for champion in batch_scores:
+                if champion not in feature_dict:
+                    feature_dict[champion] = np.zeros(total_rows)
+                feature_dict[champion][batch_start:batch_end] = batch_scores[champion]
+
+            # Save after each batch with timestamp
+            temp_df = pd.DataFrame({
+                **{col: feature_dict[col] for col in original_columns},  # Original columns first
+                **{champion: feature_dict[champion] for champion in all_champions}  # Then champion columns
+            })
+            
+            batch_save_file = os.path.join("util", "data", f"feature_eng_stats.csv")
+            temp_df.to_csv(batch_save_file, index=False)
+            print(f"Saved batch progress to {batch_save_file}")
+
+            if debug:
+                print(f"{debug} is countered by: {counter_map[debug]}")
+
+        # Process debug data if any
+        if debug:
+            debug_df = pd.DataFrame(debug_data)
+            print("\nDebug Data:")
+            print(debug_df)
+
+        # Create final DataFrame
+        champion_features = pd.DataFrame(feature_dict)
+
+        # Create the final DataFrame by combining original data with new features
+        features = pd.concat([
+            merged_player_stats[original_columns],  # Keep all original columns
+            champion_features[[col for col in champion_features.columns if col not in original_columns]]  # Only new champion columns
+        ], axis=1)
+
+        # Move the champion column to be the first column
+        if 'champion' in features.columns:
+            columns = ['champion'] + [col for col in features.columns if col != 'champion']
+            features = features[columns]
+        
+        # Print confirmation message
+        print(f"Saved features in data frame.")
+            
+        return features
+
+    except Exception as e:
+        print(f"\nError occurred: {str(e)}")
+        return None
+
+def create_app_user_training_df(url):
+
+    # EXAMPLE OF INPUT: url = "https://www.op.gg/summoners/euw/Agurin-EUW?queue_type=SOLORANKED"
+
+    match = re.search(r"/summoners/(\w+)/([\w\-]+)\?", url)
+    if match:
+        region = match.group(1)
+        username = match.group(2)
+        print(f"Region: {region}, Username: {username}")
+
+
+    recent_stats = get_matches_stats(region, username)
+    # Replace " #" with "-" in the player_id column
+    recent_stats['player_id'] = recent_stats['player_id'].str.replace(" #", "-", regex=False)
+
+    player_stats = get_player_stats(region, username)
+
+    merged_stats = merge_stats(recent_stats, player_stats)
+    print("merged stats:\n")
+    print(merge_stats)
+
+    #save to feature_eng_stats.csv
+    training_features = create_champion_features_and_return_df(merged_player_stats=merged_stats, debug=None, consider_team_comp=True, test_mode=False)
+    return training_features
+
+# ========================================= end of my functions =====================================================
+
+# meta_stats = get_meta_stats() 
+# print(meta_stats)
+
+

util/connection_check.py

@@ -0,0 +1,63 @@
+import csv
+import time
+import requests
+from urllib.parse import quote
+
+# Input file path
+input_file = "util/data/lb_filtered.csv"
+
+# List to store failed summoner names
+failed_summoners = []
+
+# Function to check if the URL is accessible
+def check_connection(region, summoner):
+    # Format the summoner name: Replace spaces with '-' and '#' with '-'
+    formatted_summoner = summoner.replace(" ", "-").replace("#", "-")
+    
+    # Encode the summoner name to handle special characters like Korean or other symbols
+    formatted_summoner = quote(formatted_summoner)
+    
+    # Construct the full URL: region is correctly placed before the summoner name
+    url = f"https://www.op.gg/summoners/{region}/{formatted_summoner}?queue_type=SOLORANKED"
+    
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url)
+        
+        # Check if the response status is 200 (OK)
+        if response.status_code == 200:
+            print(f"Connection successful to {url}")
+            return True
+        else:
+            print(f"Failed to connect to {url}. Status code: {response.status_code}")
+            return False
+    except requests.exceptions.RequestException as e:
+        print(f"Error connecting to {url}: {e}")
+        return False
+
+# # Open the input CSV file
+# with open(input_file, mode="r", encoding="utf-8") as infile:
+#     csv_reader = csv.reader(infile)
+#     header = next(csv_reader)  # Skip the header row
+    
+#     # Process each row in the CSV file
+#     for row in csv_reader:
+#         region = row[1].strip()  # Get region from the first column
+#         summoner = row[0].strip()  # Get summoner name from the second column
+        
+#         # Check connection for the region and summoner
+#         if not check_connection(region, summoner):
+#             failed_summoners.append(summoner)
+        
+#         # Pause for a short time between requests to avoid overloading the server
+#         time.sleep(2)
+
+# # Print out failed summoner names if any
+# if failed_summoners:
+#     print("\nFailed to connect to the following summoners:")
+#     for summoner in failed_summoners:
+#         print(summoner)
+# else:
+#     print("\nAll connections were successful.")
+
+# print("Connection check completed.")

util/data/feature_eng_stats.csv

@@ -0,0 +1,21 @@
+player_id,region,date,champion,level,team,result,match_length_mins,kill,death,assist,kda_ratio,kill_participation,laning,cs,cs_per_min,avg_tier,teammates1,team_champ1,teammates2,team_champ2,teammates3,team_champ3,teammates4,team_champ4,oppmates1,opp_champ1,oppmates2,opp_champ2,oppmates3,opp_champ3,oppmates4,opp_champ4,oppmates5,opp_champ5,total_games,wins,losses,win_rate,avg_kills,avg_deaths,avg_assists,kda_ratio_profile,kill_participation_profile,most_champ_1,WR_1,W_1,L_1,KDA_1,most_champ_2,WR_2,W_2,L_2,KDA_2,most_champ_3,WR_3,W_3,L_3,KDA_3,TOP,JUNGLE,MID,ADC,SUPPORT,most_role_1,most_role_2,most_role_1_value,most_role_2_value,season_champ_1,cs_ssn_1,cpm_ssn_1,kda_ssn_1,k_ssn_1,d_ssn_1,a_ssn_1,wr_ssn_1,games_ssn_1,season_champ_2,cs_ssn_2,cpm_ssn_2,kda_ssn_2,k_ssn_2,d_ssn_2,a_ssn_2,wr_ssn_2,games_ssn_2,season_champ_3,cs_ssn_3,cpm_ssn_3,kda_ssn_3,k_ssn_3,d_ssn_3,a_ssn_3,wr_ssn_3,games_ssn_3,season_champ_4,cs_ssn_4,cpm_ssn_4,kda_ssn_4,k_ssn_4,d_ssn_4,a_ssn_4,wr_ssn_4,games_ssn_4,season_champ_5,cs_ssn_5,cpm_ssn_5,kda_ssn_5,k_ssn_5,d_ssn_5,a_ssn_5,wr_ssn_5,games_ssn_5,season_champ_6,cs_ssn_6,cpm_ssn_6,kda_ssn_6,k_ssn_6,d_ssn_6,a_ssn_6,wr_ssn_6,games_ssn_6,season_champ_7,cs_ssn_7,cpm_ssn_7,kda_ssn_7,k_ssn_7,d_ssn_7,a_ssn_7,wr_ssn_7,games_ssn_7,7d_champ_1,7d_total_1,7d_W_1,7d_L_1,7d_WR_1,7d_champ_2,7d_total_2,7d_W_2,7d_L_2,7d_WR_2,7d_champ_3,7d_total_3,7d_W_3,7d_L_3,7d_WR_3,mastery_champ_1,m_lv_1,mastery_champ_2,m_lv_2,mastery_champ_3,m_lv_3,mastery_champ_4,m_lv_4,mastery_champ_5,m_lv_5,mastery_champ_6,m_lv_6,mastery_champ_7,m_lv_7,mastery_champ_8,m_lv_8,mastery_champ_9,m_lv_9,mastery_champ_10,m_lv_10,mastery_champ_11,m_lv_11,mastery_champ_12,m_lv_12,mastery_champ_13,m_lv_13,mastery_champ_14,m_lv_14,mastery_champ_15,m_lv_15,mastery_champ_16,m_lv_16,Aatrox,Ahri,Akali,Akshan,Alistar,Ambessa,Amumu,Anivia,Annie,Aphelios,Ashe,Aurelion Sol,Aurora,Azir,Bard,Bel'Veth,Blitzcrank,Brand,Braum,Briar,Caitlyn,Camille,Cassiopeia,Cho'Gath,Corki,Darius,Diana,Dr. Mundo,Draven,Ekko,Elise,Evelynn,Ezreal,Fiddlesticks,Fiora,Fizz,Galio,Gangplank,Garen,Gnar,Gragas,Graves,Gwen,Hecarim,Heimerdinger,Hwei,Illaoi,Irelia,Ivern,Janna,Jarvan IV,Jax,Jayce,Jhin,Jinx,K'Sante,Kai'Sa,Kalista,Karma,Karthus,Kassadin,Katarina,Kayle,Kayn,Kennen,Kha'Zix,Kindred,Kled,Kog'Maw,LeBlanc,Lee Sin,Leona,Lillia,Lissandra,Lucian,Lulu,Lux,Malphite,Malzahar,Maokai,Master Yi,Milio,Miss Fortune,Mordekaiser,Morgana,Naafiri,Nami,Nasus,Nautilus,Neeko,Nidalee,Nilah,Nocturne,Nunu & Willump,Olaf,Orianna,Ornn,Pantheon,Poppy,Pyke,Qiyana,Quinn,Rakan,Rammus,Rek'Sai,Rell,Renata Glasc,Renekton,Rengar,Riven,Rumble,Ryze,Samira,Sejuani,Senna,Seraphine,Sett,Shaco,Shen,Shyvana,Singed,Sion,Sivir,Skarner,Smolder,Sona,Soraka,Swain,Sylas,Syndra,Tahm Kench,Taliyah,Talon,Taric,Teemo,Thresh,Tristana,Trundle,Tryndamere,Twisted Fate,Twitch,Udyr,Urgot,Varus,Vayne,Veigar,Vel'Koz,Vex,Vi,Viego,Viktor,Vladimir,Volibear,Warwick,Wukong,Xayah,Xerath,Xin Zhao,Yasuo,Yone,Yorick,Yuumi,Zac,Zed,Zeri,Ziggs,Zilean,Zoe,Zyra
+Agurin-EUW,euw,"Sun, Jan 5, 2025 6:25 PM",Jarvan IV,16,blue,1,26.68,9,2,19,14.0,0.67,1.08,183,6.9,2,skygge kriger #EUV,Rumble,성진우 #789,Vladimir,JEZUUU #0814,Yasuo,Papadodi #1010,Lulu,the inescapable #EUW,Maokai,edge of night #999,Kindred,Macaquiño #GAP,LeBlanc,Wanna be a pro #03120,Corki,Walid Georgey #EUW,Rell,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.19435304347826085,0.003040336134453781,0.07462158415841585,0.009793636363636234,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.2269628571428571,0.029494444444444448,0.0789936507936508,0.0,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.14430505263157895,0.2668461538461539,0.22659214285714285,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.25233,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3240156445714286,0.0,0.02140645161290322,0.0,0.03248357142857143,0.028233786407766977,0.07952651162790698,1.1184574571428572,0.0,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.09255750000000001,0.1480488311688312,0.0,0.23643000000000006,0.0,0.01380975609756098,0.0,0.04587567567567568,0.035337421487603306,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.23344400000000007,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1338443389830509,0.3205411764705883,0.0,0.07963456790123458,0.010558843373493977,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.0,0.060766500000000015,0.19474545454545455,0.0,0.047811724137931055,0.0,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1494712857142857,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.03712064516129032,0.0,0.13844842105263158,1.0648,0.2742,0.0,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.0,0.5096700000000001,0.038388235294117654,0.0,0.07933015384615384,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04188557142857143,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sun, Jan 5, 2025 5:48 PM",Wukong,16,red,1,28.68,3,5,11,2.8,0.47,1.33,220,7.7,2,S8DiamondDog #EUW,Yasuo,Owen Mcdade #BLACK,Fiora,AllYouHadToDoWas #Stay,Karthus,Depressed Loser #ff15,LeBlanc,Drali #Drali,K'Sante,Kiki #COYG,Viego,222 #EUW3,Syndra,JEZUUU #0814,Xayah,grisha turbina #ADHD,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.1186316129032258,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.0,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.28848705882352943,0.0,0.07963456790123458,0.011732048192771086,0.2609019310344827,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.0,0.06274568807339448,0.13989927272727273,0.46191111111111116,0.0,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,0.0,0.2742,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0,0.15617647058823528,0.13391111111111112,0.0,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04188557142857143,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sun, Jan 5, 2025 5:10 PM",Wukong,12,blue,0,20.65,6,4,2,2.0,0.57,0.89,153,7.4,2,Novachrono #Back,Cho'Gath,2025 #favo,Gragas,Jeyrus #Jey,Corki,Papadodi #1010,Lulu,S8DiamondDog #EUW,Dr. Mundo,Thomas Shelby #SOLOQ,Vi,222 #EUW3,Ekko,JEZUUU #0814,Caitlyn,Pablo #boda,Bard,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.0,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.0,0.25218095238095234,0.029494444444444448,0.0,0.0,0.0,0.2694728571428571,0.0,0.0,0.0,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.0,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07157386046511628,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.0,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.025276438356164395,0.15057488135593225,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03430963636363637,0.021356934306569337,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02518335971223022,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.04834862068965517,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.17214545454545455,0.06470422535211266,0.2696914285714285,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.0,1.0648,0.24678,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.008393023255813903,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sun, Jan 5, 2025 4:30 PM",Elise,15,blue,1,24.22,10,3,6,5.33,0.62,1.08,194,8.0,2,SatØrius #EUW,Rumble,AbbedaggÆ #EUW,Jayce,Kite Machine #EUW,Jhin,Vento #TAIF,Bard,KOI Spooder #1352,K'Sante,NazgulsBlade #LOTR,Skarner,TwitchTVxKenzuke #EUW,Aurora,Unkn0wn #EUW11,Ezreal,Paolocannone #EUW,Karma,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.0,0.11204658536585367,0.0,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.0,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.22659214285714285,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.0,0.0,0.3157333333333334,0.0,0.287661935483871,0.0953066666666667,0.0,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07167111111111112,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.0,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.0426944680851064,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sun, Jan 5, 2025 1:34 AM",Wukong,14,blue,0,26.37,7,10,4,1.1,0.65,0.89,169,6.4,2,Vandett0 #ETABL,Teemo,Grunge #010,Yone,222 #EUW3,Ekko,Crixus #ARENA,Leona,SenSen #EUWFR,Nidalee,AllYouHadToDoWas #Stay,Karthus,SRTSS #EUW,Corki,Keduii #GOAT,Caitlyn,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.009793636363636234,0.00015814285714285965,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.07212857142857136,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.13102493650793653,0.09564000000000002,0.06285470085470087,0.0,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.0,0.0,0.2994142857142857,0.10848695652173915,0.0,0.0,1.5788843958188155,0.2125009523809524,0.6077082857142858,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.1186316129032258,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.25889574193548387,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03248357142857143,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05226402684563758,0.0,0.5258000000000002,0.0,0.08244909090909093,0.09255750000000001,0.13324394805194809,0.17259999999999998,0.23643000000000006,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.09970235294117646,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.0,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.28848705882352943,0.0,0.07963456790123458,0.011732048192771086,0.2609019310344827,0.03812181818181819,0.021356934306569337,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.0960627272727273,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.0,0.06274568807339448,0.13989927272727273,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0,0.11976986301369862,0.13286336507936505,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.04596140816326531,0.004684955752212394,0.04743829787234045,0.2676453157894737,0.04035187969924811,0.035477704918032794,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.24678,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.8993138297142858,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.32300952380952386,0.0,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.07570324528301889,0.12691246153846156,0.04188557142857143,0.048898901098901104,0.17487454945054942
+Agurin-EUW,euw,"Sun, Jan 5, 2025 12:48 AM",Wukong,17,blue,1,31.83,4,3,4,2.67,0.33,1.13,267,8.4,2,Bobsik #EUW,Camille,SRTSS #EUW,LeBlanc,PsychoZedPlayer #EUW,Hwei,mein herz #197,Janna,Patrick Jane #Tea,Vladimir,Seonsu #Noula,Viego,Odysseus #131,Taliyah,Keduii #GOAT,Ezreal,Crixus #ARENA,Zilean,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.19435304347826085,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.0,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.19125085714285717,0.0,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2401615384615385,0.2517690476190476,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.0,0.0,0.2964923076923078,0.049657674418604664,0.0,1.3217776953613445,0.09771054545454544,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.23643000000000006,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.23108081833746633,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.09545744680851065,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.41572000000000003,0.19127272727272726,0.06470422535211266,0.0,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,0.0,0.2742,0.0,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.3633857142857143,0.5096700000000001,0.038388235294117654,0.0,0.07933015384615384,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.0,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 11:56 PM",Wukong,16,red,1,25.28,7,2,7,7.0,0.61,0.89,208,8.2,2,Szygenda #EUW,Aurora,Dr Orange #Caps,Galio,J1HUIV #000,Kalista,ttveminencjasupp #kick,Renata Glasc,Patrick Jane #Tea,Vladimir,Thors Snoresson #3327,Graves,222 #EUW3,Syndra,LeonardoDiCassio #EUW,Caitlyn,Maynter #EUW,Nautilus,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.0,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.0,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.0,0.07816271186440678,0.16033894736842105,0.2401615384615385,0.2517690476190476,0.0,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.09771054545454544,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.0,0.2599659206296496,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.0,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.0,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.0,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 10:39 PM",Wukong,13,red,1,21.02,5,1,5,10.0,0.56,1.44,147,7.0,2,Desperate Nasus #EUW,Kayle,Yoo Jonghyuk333 #EUW,Aurora,LeonardoDiCassio #EUW,Viktor,Canam #123,Nami,Makkro #EUW,Ornn,Kiki #COYG,Skarner,SRTSS #EUW,Yone,avarice61 #EUW,Kog'Maw,Sergeant Sona #KJP,Seraphine,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.0,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.15991411764705876,0.25218095238095234,0.029494444444444448,0.07109428571428572,0.14954666666666666,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.22659214285714285,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.60018,0.2841600000000001,0.37839999999999996,0.287661935483871,0.08577600000000003,0.3600173828571429,0.0,0.02140645161290322,0.0,0.0,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.0,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.09970235294117646,0.02524102564102564,0.0,0.026839006211180128,0.0,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.0,0.1673054237288136,0.3205411764705883,0.0,0.07167111111111112,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.0,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.060945000000000006,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.0,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0843490093457944,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.0,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 10:04 PM",Elise,17,blue,1,25.4,15,3,6,7.0,0.53,1.56,225,8.9,2,choilot #LOT,Gnar,a winds rhythm #Aang,Yasuo,Boring Cycle #Abner,Swain,ataraxia #7z7,Bard,Riihonen #EUW,Vladimir,AkaManto #LGM,Hecarim,SRTSS #EUW,Yone,Keduii #GOAT,Ashe,Peak Level #123,Zac,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.0,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.0,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.2269628571428571,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.19472769230769235,0.07816271186440678,0.16033894736842105,0.0,0.2517690476190476,0.49085,0.13604625,0.0,0.0241679746835443,0.28036666666666665,0.0,0.266843076923077,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.09771054545454544,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2599659206296496,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.09545744680851065,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.06472285714285714,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.17886272727272726,0.06274568807339448,0.0,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.09101284615384615,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.0,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.0,0.0,0.038388235294117654,0.0,0.0,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 9:01 PM",Wukong,13,blue,0,27.2,6,11,5,1.0,0.58,0.82,159,5.8,2,Ronaldo Nazário #12345,Irelia,TATA #HOPE,Galio,chöego #EUW,Corki,Sergeant Sona #KJP,Sona,drunkenztain #EUW,Fiddlesticks,Foutriquet #EUW,Jarvan IV,silkysmath #6206,Syndra,Kushala Daora #MHVV,Caitlyn,darkchri99 #EUW,Lux,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.07212857142857136,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.252378,0.12472359550561801,0.14949,0.13102493650793653,0.09564000000000002,0.06285470085470087,0.0,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.0,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.0,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.0,0.049657674418604664,0.13181290322580644,0.0,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.2841600000000001,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3240156445714286,0.0,0.02140645161290322,0.0,0.03609285714285714,0.028233786407766977,0.07952651162790698,1.0066117114285715,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.09255750000000001,0.1480488311688312,0.15533999999999998,0.0,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.16348434782608695,0.0,0.2593822222222223,0.2599659206296496,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.11667457894736842,0.0,0.06274568807339448,0.1243549090909091,0.46191111111111116,0.0,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06057779527559057,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.035477704918032794,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 8:23 PM",Elise,12,red,0,24.83,1,6,2,0.5,0.2,0.92,163,6.6,2,Bent246 #Bent,Akshan,c0cky movement #tukan,Irelia,fjgyqwjsaqxca #LUDER,Jinx,gummyjuiceULTRA #11cmP,Pyke,أستولفو بن لادن #UwU,Singed,bluerzor #grind,Wukong,나프켈라 #BOAT,Akali,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Jhin,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.0,0.0,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.0804266666666667,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.033897974025974026,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.12244162500000001,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.0,0.049657674418604664,0.1186316129032258,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.0,0.0,0.34056,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.028233786407766977,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.028697433962264155,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.35172611162297496,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.15057488135593225,0.28848705882352943,0.0,0.07963456790123458,0.011732048192771086,0.2609019310344827,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.0,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.0,0.06274568807339448,0.13989927272727273,0.46191111111111116,0.19127272727272726,0.051763380281690134,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.12460357894736843,1.0648,0.24678,0.03872190361445785,0.11894942528735633,0.05994626865671641,0.0,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.3633857142857143,0.4587030000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04188557142857143,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 7:42 PM",Wukong,14,blue,1,21.82,5,2,8,6.5,0.39,1.0,174,8.0,2,skygge kriger #EUV,Aatrox,bvffi #meow,Corki,Kushala Daora #MHVV,Hwei,Zoo port #LOL,Zoe,c0cky movement #tukan,Yone,noname #gromp,Taliyah,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Cho'Gath,twtv exofeng #IWNL,Draven,Sergeant Sona #KJP,Rakan,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.0,0.290865306122449,0.19435304347826085,0.003040336134453781,0.07462158415841585,0.009793636363636234,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0,0.0,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.19125085714285717,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.0703464406779661,0.16033894736842105,0.2668461538461539,0.22659214285714285,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.0,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.0192658064516129,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.09255750000000001,0.1480488311688312,0.17259999999999998,0.23643000000000006,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.024155105590062117,0.18164927536231884,0.0,0.2593822222222223,0.2599659206296496,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.1398809552238806,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.0,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.09545744680851065,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.41572000000000003,0.19127272727272726,0.06470422535211266,0.0,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.050300433566433565,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.03872190361445785,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.3633857142857143,0.0,0.038388235294117654,0.0,0.07933015384615384,0.008393023255813903,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.0,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 7:13 PM",Wukong,14,red,0,24.2,3,6,7,1.67,0.53,0.28,172,7.1,2,DREILIX #000,Jayce,Bobsbag #EUW,Gragas,Rafitta #EUW,Corki,eXoValdo #EUW,Brand,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Sion,wοkeupIikethis #EUW,Viego,Eternal Blizzard #carot,Hwei,PAKO ETERNAL #EUW,Ezreal,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.0,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.0,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.0,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.0,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.0,0.0,0.2964923076923078,0.049657674418604664,0.1186316129032258,1.3217776953613445,0.1085672727272727,0.0,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.28848705882352943,0.0,0.07963456790123458,0.011732048192771086,0.2609019310344827,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.0,0.07618125,0.0,0.12963842105263157,0.0,0.06274568807339448,0.13989927272727273,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2676453157894737,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,0.0,0.2742,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04188557142857143,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Sat, Jan 4, 2025 12:41 AM",Wukong,17,blue,1,34.25,5,11,18,2.09,0.51,0.67,194,5.7,2,batubabaHOPPALA #HOPPA,Riven,Fool Night #Ivy,Veigar,Sintemon #gumo,Ashe,ScoobaTroopah #10000,Blitzcrank,Pablo #boda,Aurora,TWTV DETDERT #12MAJ,Graves,J1HUIV #000,Karma,Mandaryñ #EUW,Corki,erdt #EUW,Leona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.009793636363636234,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.0,0.060601764705882355,0.0,0.11204658536585367,0.28042,0.12472359550561801,0.0,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.0,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.0,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.0,0.0,0.02140645161290322,0.0,0.03248357142857143,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.0,0.08244909090909093,0.09255750000000001,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1494712857142857,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.0426944680851064,0.2973836842105263,0.04035187969924811,0.0,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.3633857142857143,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Fri, Jan 3, 2025 11:54 PM",Wukong,15,red,1,23.03,7,3,5,4.0,0.48,1.56,197,8.6,2,Fool Night #Ivy,Aurora,Dr Orange #Caps,Sylas,Sintemon #gumo,Ashe,ScoobaTroopah #10000,Blitzcrank,modernillo #moder,Ambessa,Eternal Blizzard #carot,Viego,J1HUIV #000,Karma,Beanovich #bean,Varus,niudefuwuqi2007 #8085,Braum,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.0,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.0,0.060601764705882355,0.0,0.11204658536585367,0.28042,0.12472359550561801,0.0,0.14558326278659614,0.0,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0953066666666667,0.0,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.15057488135593225,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.0,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.0,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,0.0,0.2742,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Fri, Jan 3, 2025 11:23 PM",Jarvan IV,17,blue,1,31.22,1,4,20,5.25,0.54,0.67,217,7.0,2,Vertigo #4810,Ambessa,LëFacteur Chance #EUW,Akali,Chrisberg #6103,Corki,raikooooo #EUW,Blitzcrank,modernillo #moder,Gragas,mikusik998 #gaz,Bel'Veth,Imma touch you #131,Hwei,Beanovich #bean,Kalista,Jordæn #EUW,Pantheon,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.0,0.003040336134453781,0.07462158415841585,0.0,0.00015814285714285965,0.024356026490066237,0.020584932515337426,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.09048000000000003,0.11204658536585367,0.28042,0.0,0.0,0.14558326278659614,0.09564000000000002,0.05656923076923078,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.0,0.0,0.2694728571428571,0.10848695652173915,0.0,0.18454666666666675,1.4209959562369339,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.0,0.49085,0.12244162500000001,0.013109090909090916,0.0241679746835443,0.0,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.1895999258252101,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.026960720720720713,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.0,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03430963636363637,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.09545744680851065,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.259552051948052,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.14568216216216215,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2676453157894737,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,0.95832,0.2742,0.043024337349397604,0.1070544827586207,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.12052000000000002,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Fri, Jan 3, 2025 10:40 PM",Elise,16,red,1,29.27,6,3,18,8.0,0.71,1.27,197,6.7,2,blueskies777 #EUW,Renekton,Return to dust #Dust,Viktor,Beanovich #bean,Jinx,twojstary2komary #win,Rakan,나프켈라 #BOAT,Jax,bluerzor #grind,Skarner,LëFacteur Chance #EUW,Akali,Dr Mango #Hans,Kalista,Sergeant Sona #KJP,Lulu,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.0,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.08014285714285707,0.060601764705882355,0.09048000000000003,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.05656923076923078,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.033897974025974026,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.22659214285714285,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.1895999258252101,0.0,0.5364771428571429,0.6668666666666667,0.0,0.37839999999999996,0.287661935483871,0.0,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.028697433962264155,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.0,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07167111111111112,0.011732048192771086,0.0,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.0,0.0,0.0531241379310345,0.15295135135135138,0.10606382978723405,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.46191111111111116,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.0,0.03872190361445785,0.1070544827586207,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.12052000000000002,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Fri, Jan 3, 2025 9:59 PM",Wukong,15,red,1,23.2,10,3,4,4.67,0.54,1.38,203,8.8,2,yuqi #golem,Galio,Mein Königreich #917n3,Sylas,batubabaHOPPALA #HOPPA,Varus,MORG3NSHTERN #EUW,Rakan,KOI Spooder #1352,Rumble,mikusik998 #gaz,Bel'Veth,RKR #123,Viktor,supershy #jblss,Ashe,Drututts Lisp #EUW,Gragas,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.11383934426229508,0.32318367346938776,0.2159478260869565,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.024356026490066237,0.020584932515337426,0.11887238095238094,0.0,0.060601764705882355,0.09048000000000003,0.11204658536585367,0.28042,0.0,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.16823999999999997,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.2125009523809524,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.0,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.0,0.49085,0.10883700000000002,0.013109090909090916,0.0241679746835443,0.25233,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.1895999258252101,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.2841600000000001,0.37839999999999996,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.26270000000000004,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2888510229218329,0.09398000000000001,0.0,0.3908067906921944,0.0303308108108108,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.0,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.0,0.10606382978723405,0.03868266666666668,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.259552051948052,0.05303217391304348,0.05372068965517241,0.068563125,0.0,0.12963842105263157,0.19873636363636363,0.06274568807339448,0.15544363636363637,0.0,0.19127272727272726,0.06470422535211266,0.2996571428571428,0.16389243243243243,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.0,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,0.95832,0.0,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0843490093457944,0.15617647058823528,0.13391111111111112,0.40376190476190477,0.5096700000000001,0.038388235294117654,0.0,0.08814461538461538,0.009325581395348781,0.0841147169811321,0.12691246153846156,0.04653952380952381,0.048898901098901104,0.1943050549450549
+Agurin-EUW,euw,"Fri, Jan 3, 2025 9:13 PM",Elise,16,red,1,28.07,8,2,12,10.0,0.51,0.75,207,7.4,1,washed úp #EUW,Gwen,SMRADOCH RANDOM #EUW,Ziggs,RKR #123,Ashe,Frank Castle #2610,Blitzcrank,Shourdy #EUW,K'Sante,Purple Place #Moe,Rengar,WORLDS2025 #AIS,Irelia,Odysseus #131,Taliyah,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.10245540983606558,0.32318367346938776,0.19435304347826085,0.003040336134453781,0.07462158415841585,0.009793636363636234,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.0,0.060601764705882355,0.09048000000000003,0.11204658536585367,0.28042,0.12472359550561801,0.0,0.13102493650793653,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.18454666666666675,1.5788843958188155,0.19125085714285717,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.0,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.0,0.049657674418604664,0.1186316129032258,1.3217776953613445,0.1085672727272727,0.4828294285714286,0.6668666666666667,0.3157333333333334,0.0,0.287661935483871,0.0953066666666667,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.23643000000000006,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.2593822222222223,0.2599659206296496,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.28848705882352943,0.0,0.07963456790123458,0.011732048192771086,0.2609019310344827,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.08485106382978724,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.0,0.06274568807339448,0.13989927272727273,0.41572000000000003,0.19127272727272726,0.06470422535211266,0.0,0.16389243243243243,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.24678,0.043024337349397604,0.11894942528735633,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.13391111111111112,0.3633857142857143,0.4587030000000001,0.038388235294117654,0.0,0.07933015384615384,0.009325581395348781,0.0841147169811321,0.0,0.04188557142857143,0.048898901098901104,0.17487454945054942
+Agurin-EUW,euw,"Fri, Jan 3, 2025 8:38 PM",Wukong,12,red,0,21.68,3,5,7,2.0,0.48,0.67,141,6.5,2,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Volibear,wymówkomat #escik,Ahri,Percy Magic #1234,Kalista,cross the edge #fear,Nautilus,ΑrchΦiend #666,Aatrox,Razørk Activoo #razzz,Xin Zhao,yebit #haha,Taliyah,RKR #123,Ashe,Shourdy #EUW,Sylas,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0,0.0,0.0,0.19435304347826085,0.003040336134453781,0.07462158415841585,0.010881818181818038,0.0001757142857142885,0.02706225165562915,0.022872147239263806,0.11887238095238094,0.0,0.060601764705882355,0.10053333333333336,0.11204658536585367,0.28042,0.12472359550561801,0.14949,0.14558326278659614,0.09564000000000002,0.06285470085470087,0.1776823529411764,0.25218095238095234,0.029494444444444448,0.0789936507936508,0.1869333333333333,0.0,0.2994142857142857,0.10848695652173915,0.0,0.20505185185185193,1.5788843958188155,0.19125085714285717,0.6752314285714287,0.0,0.0052499999999999995,0.037664415584415585,0.2163641025641026,0.07816271186440678,0.16033894736842105,0.2668461538461539,0.2517690476190476,0.49085,0.13604625,0.013109090909090916,0.0241679746835443,0.28036666666666665,0.0,0.2964923076923078,0.049657674418604664,0.13181290322580644,1.3217776953613445,0.1085672727272727,0.5364771428571429,0.6668666666666667,0.3157333333333334,0.37839999999999996,0.287661935483871,0.0,0.3600173828571429,0.0,0.02140645161290322,0.0,0.03609285714285714,0.03137087378640775,0.07952651162790698,1.1184574571428572,0.09357652173913049,0.03188603773584906,0.05807114093959731,0.0,0.5258000000000002,0.12630055555555558,0.08244909090909093,0.10284166666666668,0.1480488311688312,0.17259999999999998,0.23643000000000006,0.0,0.01380975609756098,0.2749276595744681,0.04587567567567568,0.03926380165289256,0.11078039215686274,0.02524102564102564,0.0,0.026839006211180128,0.18164927536231884,0.0,0.0,0.2599659206296496,0.09398000000000001,0.0,0.3908067906921944,0.03370090090090089,0.0464123076923077,0.15542328358208957,0.028084931506849327,0.1673054237288136,0.3205411764705883,0.0,0.07963456790123458,0.011732048192771086,0.2898910344827586,0.03812181818181819,0.023729927007299263,0.2623725,0.06751833333333335,0.19474545454545455,0.0,0.0531241379310345,0.15295135135135138,0.09545744680851065,0.04298074074074075,0.02798151079136691,0.15588760563380283,0.10673636363636366,0.07191428571428571,0.0,0.08626206896551725,0.28839116883116883,0.05303217391304348,0.05372068965517241,0.07618125,0.0,0.12963842105263157,0.19873636363636363,0.05647111926605503,0.15544363636363637,0.0,0.19127272727272726,0.06470422535211266,0.0,0.18210270270270268,0.06730866141732285,0.0734526315789474,0.11976986301369862,0.1660792063492063,0.10112538461538462,0.055889370629370626,0.04235210526315789,0.051068231292517016,0.004684955752212394,0.04743829787234045,0.2973836842105263,0.04035187969924811,0.03941967213114755,0.04124516129032258,0.0,0.13844842105263158,1.0648,0.2742,0.043024337349397604,0.0,0.05994626865671641,0.9992375885714286,0.0937211214953271,0.15617647058823528,0.0,0.3633857142857143,0.5096700000000001,0.038388235294117654,0.0,0.07933015384615384,0.008393023255813903,0.0841147169811321,0.14101384615384618,0.04653952380952381,0.048898901098901104,0.17487454945054942

util/data/meta_stats.csv

@@ -0,0 +1,239 @@
+rank,champion,tier,role,win_rate,pick_rate,ban_rate,counter1,counter2,counter3
+1,Irelia,1,top,0.5186,0.0576,0.0956,Warwick,Volibear,Zac
+2,Maokai,1,top,0.5162,0.0622,0.032799999999999996,Heimerdinger,Olaf,Mordekaiser
+3,Warwick,1,top,0.5251,0.0305,0.08380000000000001,Cassiopeia,Teemo,Heimerdinger
+4,Aatrox,1,top,0.5024000000000001,0.0855,0.1023,Cassiopeia,Warwick,Irelia
+5,Riven,1,top,0.5138,0.052000000000000005,0.027000000000000003,Quinn,Maokai,Kennen
+6,Gragas,1,top,0.5123,0.0492,0.0229,Cho'Gath,Vayne,Maokai
+7,Darius,1,top,0.5056,0.0623,0.1333,Cassiopeia,Teemo,Wukong
+8,Malphite,1,top,0.5131,0.044199999999999996,0.0617,Maokai,Dr. Mundo,Tahm Kench
+9,Volibear,1,top,0.509,0.0489,0.0338,Quinn,Teemo,Kennen
+10,Mordekaiser,2,top,0.5064,0.048,0.0575,Cassiopeia,Heimerdinger,Vayne
+11,Cho'Gath,2,top,0.5186999999999999,0.028399999999999998,0.0044,Olaf,Ornn,Dr. Mundo
+12,Ornn,2,top,0.5117,0.0388,0.005600000000000001,Warwick,Tryndamere,Dr. Mundo
+13,Camille,2,top,0.5105,0.04019999999999999,0.0189,Zac,Ryze,Teemo
+14,Renekton,2,top,0.4997,0.0588,0.048499999999999995,Cassiopeia,Teemo,Warwick
+15,Tahm Kench,2,top,0.5106,0.0329,0.040999999999999995,Wukong,Heimerdinger,Singed
+16,Dr. Mundo,2,top,0.5082,0.037000000000000005,0.0195,Riven,Udyr,Warwick
+17,Teemo,2,top,0.515,0.0255,0.033,Ryze,Olaf,Cassiopeia
+18,Sett,2,top,0.5025999999999999,0.0444,0.0088,Zac,Warwick,Volibear
+19,Shen,2,top,0.5103,0.0313,0.0038,Cassiopeia,Teemo,Tryndamere
+20,K'Sante,2,top,0.48979999999999996,0.0704,0.07730000000000001,Singed,Kayle,Kennen
+21,Fiora,2,top,0.5038,0.0366,0.0584,Quinn,Ryze,Warwick
+22,Kayle,2,top,0.5155,0.0217,0.006500000000000001,Ryze,Malphite,Cassiopeia
+23,Gnar,2,top,0.4986,0.0455,0.0195,Quinn,Vladimir,Malphite
+24,Ambessa,3,top,0.4866,0.054299999999999994,0.2948,Warwick,Camille,Irelia
+25,Poppy,3,top,0.5157,0.0181,0.0285,Warwick,Cassiopeia,Darius
+26,Gwen,3,top,0.5065999999999999,0.0288,0.0245,Warwick,Singed,Viktor
+27,Viktor,3,top,0.4988,0.0178,0.0348,Tryndamere,Kled,Irelia
+28,Vladimir,3,top,0.5093,0.022400000000000003,0.0182,Cassiopeia,Nasus,Akali
+29,Urgot,3,top,0.511,0.0217,0.0036,Cassiopeia,Aurora,Olaf
+30,Sion,3,top,0.4982,0.0377,0.004699999999999999,Riven,Dr. Mundo,Gwen
+31,Trundle,3,top,0.5136999999999999,0.018600000000000002,0.0042,Heimerdinger,Zac,Quinn
+32,Garen,3,top,0.494,0.043899999999999995,0.0212,Camille,Maokai,Kayle
+33,Singed,3,top,0.5135000000000001,0.0177,0.0025,Vayne,Trundle,Ryze
+34,Jax,3,top,0.4892,0.05,0.0716,Cassiopeia,Heimerdinger,Vladimir
+35,Olaf,3,top,0.5164,0.015300000000000001,0.0095,Vayne,Heimerdinger,Kayle
+36,Tryndamere,3,top,0.5062,0.0231,0.0134,Warwick,Volibear,Cho'Gath
+37,Cassiopeia,3,top,0.5281,0.0075,0.0045000000000000005,Kled,Garen,Udyr
+38,Yorick,3,top,0.5009,0.0258,0.0262,Warwick,Kayle,Tryndamere
+39,Kennen,3,top,0.5122,0.015300000000000001,0.0087,Udyr,Galio,Dr. Mundo
+40,Zac,3,top,0.517,0.011699999999999999,0.0069,Galio,Volibear,Riven
+41,Nasus,3,top,0.4988,0.027000000000000003,0.0321,Ryze,Zac,Heimerdinger
+42,Jayce,3,top,0.4826,0.054900000000000004,0.0172,Quinn,Tahm Kench,Irelia
+43,Gangplank,3,top,0.49420000000000003,0.0322,0.011899999999999999,Quinn,Cho'Gath,Tahm Kench
+44,Pantheon,3,top,0.5027,0.0206,0.0048,Heimerdinger,Maokai,Malphite
+45,Ryze,3,top,0.5169,0.0108,0.0013,Zac,Irelia,Pantheon
+46,Kled,3,top,0.5111,0.014199999999999999,0.005,Camille,Akali,Riven
+47,Wukong,3,top,0.513,0.0118,0.005,Cassiopeia,Singed,Ryze
+48,Yone,3,top,0.4765,0.055099999999999996,0.0432,Irelia,Riven,Wukong
+49,Illaoi,4,top,0.494,0.0235,0.0508,Heimerdinger,Kayle,Teemo
+50,Aurora,4,top,0.4936,0.0191,0.0414,Irelia,Akali,Wukong
+51,Yasuo,4,top,0.485,0.023799999999999998,0.0385,Singed,Cassiopeia,Riven
+52,Heimerdinger,4,top,0.5097999999999999,0.009300000000000001,0.0044,Galio,Ryze,Cassiopeia
+53,Quinn,4,top,0.512,0.0076,0.0024,Olaf,Malphite,Maokai
+54,Akali,4,top,0.4924,0.015600000000000001,0.019299999999999998,Galio,Poppy,Maokai
+55,Sylas,4,top,0.4874,0.0159,0.0152,Urgot,Poppy,Kled
+1,Warwick,1,jungle,0.5162,0.0556,0.1527,Rek'Sai,Ivern,Nunu & Willump
+2,Wukong,1,jungle,0.5143,0.066,0.0281,Zyra,Karthus,Ivern
+3,Viego,1,jungle,0.5003,0.16820000000000002,0.1338,Rammus,Qiyana,Bel'Veth
+4,Nocturne,1,jungle,0.5117,0.0575,0.0784,Udyr,Gwen,Wukong
+5,Graves,1,jungle,0.5017,0.1361,0.19190000000000002,Gwen,Rammus,Qiyana
+6,Kayn,1,jungle,0.509,0.0733,0.0512,Kindred,Fiddlesticks,Wukong
+7,Kha'Zix,1,jungle,0.5083,0.0729,0.0632,Fiddlesticks,Zyra,Shaco
+8,Shaco,1,jungle,0.5115,0.0466,0.12990000000000002,Briar,Karthus,Graves
+9,Nunu & Willump,1,jungle,0.5162,0.0333,0.0069,Qiyana,Pantheon,Bel'Veth
+10,Lee Sin,1,jungle,0.4966,0.1702,0.1137,Shaco,Rek'Sai,Warwick
+11,Ekko,1,jungle,0.509,0.055,0.0103,Pantheon,Ivern,Rengar
+12,Elise,2,jungle,0.5056,0.0487,0.055099999999999996,Nocturne,Qiyana,Shaco
+13,Bel'Veth,2,jungle,0.5117,0.026099999999999998,0.0444,Sejuani,Ivern,Rek'Sai
+14,Jarvan IV,2,jungle,0.5036999999999999,0.0558,0.014199999999999999,Gwen,Jax,Bel'Veth
+15,Xin Zhao,2,jungle,0.5078,0.0359,0.0053,Pantheon,Jax,Udyr
+16,Fiddlesticks,2,jungle,0.5114,0.0246,0.0137,Nunu & Willump,Rammus,Shaco
+17,Skarner,2,jungle,0.5056,0.0393,0.0883,Lillia,Gwen,Udyr
+18,Hecarim,2,jungle,0.5039,0.0466,0.0327,Ivern,Volibear,Udyr
+19,Briar,2,jungle,0.5093,0.0258,0.0252,Pantheon,Jax,Teemo
+20,Diana,2,jungle,0.5025999999999999,0.0464,0.0139,Warwick,Qiyana,Dr. Mundo
+21,Rek'Sai,2,jungle,0.5138,0.0158,0.006500000000000001,Dr. Mundo,Karthus,Volibear
+22,Udyr,2,jungle,0.5093,0.0233,0.0242,Qiyana,Briar,Poppy
+23,Master Yi,2,jungle,0.5017,0.0454,0.0704,Shaco,Elise,Warwick
+24,Karthus,2,jungle,0.5064,0.028300000000000002,0.059500000000000004,Shyvana,Ivern,Volibear
+25,Kindred,3,jungle,0.5008,0.0392,0.029300000000000003,Ivern,Qiyana,Elise
+26,Vi,3,jungle,0.4951,0.0647,0.0171,Dr. Mundo,Rek'Sai,Wukong
+27,Ivern,3,jungle,0.5114,0.0131,0.0051,Amumu,Briar,Nunu & Willump
+28,Gwen,3,jungle,0.5114,0.0115,0.0098,Bel'Veth,Master Yi,Qiyana
+29,Talon,3,jungle,0.5046,0.0215,0.0066,Bel'Veth,Pantheon,Rengar
+30,Rammus,3,jungle,0.5081,0.0139,0.0282,Pantheon,Lillia,Dr. Mundo
+31,Volibear,3,jungle,0.5033,0.0197,0.013600000000000001,Jax,Ivern,Pantheon
+32,Zyra,3,jungle,0.5047999999999999,0.015,0.009000000000000001,Gwen,Karthus,Rengar
+33,Amumu,3,jungle,0.4988,0.0273,0.0154,Zed,Pantheon,Karthus
+34,Evelynn,3,jungle,0.5008,0.0217,0.0329,Taliyah,Briar,Teemo
+35,Nidalee,3,jungle,0.4927,0.0449,0.0321,Rammus,Briar,Nocturne
+36,Taliyah,3,jungle,0.5034000000000001,0.016200000000000003,0.0045000000000000005,Dr. Mundo,Zed,Qiyana
+37,Zac,3,jungle,0.4949,0.0346,0.0206,Ivern,Qiyana,Taliyah
+38,Shyvana,3,jungle,0.5039,0.0148,0.003,Nunu & Willump,Bel'Veth,Udyr
+39,Lillia,3,jungle,0.49520000000000003,0.033,0.0326,Qiyana,Talon,Briar
+40,Zed,3,jungle,0.5005,0.014199999999999999,0.0305,Dr. Mundo,Xin Zhao,Master Yi
+41,Rengar,3,jungle,0.4925,0.0317,0.0484,Rammus,Gwen,Jax
+42,Pantheon,3,jungle,0.5055,0.0072,0.0016,Dr. Mundo,Lillia,Wukong
+43,Qiyana,4,jungle,0.5056,0.0063,0.0015,Volibear,Skarner,Shaco
+44,Poppy,4,jungle,0.4965,0.0088,0.013999999999999999,Rammus,Warwick,Shaco
+45,Gragas,4,jungle,0.4843,0.0288,0.0134,Poppy,Nocturne,Skarner
+46,Sejuani,4,jungle,0.484,0.0248,0.0029,Qiyana,Rek'Sai,Dr. Mundo
+47,Jax,4,jungle,0.49670000000000003,0.005600000000000001,0.008100000000000001,Amumu,Udyr,Nunu & Willump
+48,Dr. Mundo,4,jungle,0.4962,0.0051,0.0027,Qiyana,Ivern,Gwen
+49,Teemo,4,jungle,0.48710000000000003,0.0111,0.0144,Qiyana,Zyra,Gwen
+50,Ambessa,5,jungle,0.43770000000000003,0.0096,0.052199999999999996,Rammus,Master Yi,Rek'Sai
+1,Viktor,1,mid,0.5057,0.1827,0.3603,Akali,Ekko,Xerath
+2,Vex,1,mid,0.5247999999999999,0.0337,0.0524,Cho'Gath,Annie,Corki
+3,Ahri,1,mid,0.5082,0.0971,0.0415,Cho'Gath,Akshan,Cassiopeia
+4,Sylas,2,mid,0.4999,0.1033,0.099,Taliyah,Garen,Vex
+5,Galio,2,mid,0.5094,0.0525,0.0364,Garen,Swain,Lux
+6,Anivia,2,mid,0.5211,0.0216,0.0147,Gragas,Garen,Neeko
+7,Irelia,2,mid,0.5115999999999999,0.037200000000000004,0.0622,Vex,Malphite,Galio
+8,Akali,2,mid,0.503,0.07200000000000001,0.08960000000000001,Twisted Fate,Akshan,Taliyah
+9,Katarina,2,mid,0.5039,0.0616,0.067,Kayle,Malphite,Cho'Gath
+10,Lux,2,mid,0.5093,0.0394,0.015700000000000002,Naafiri,Aurelion Sol,Taliyah
+11,Taliyah,2,mid,0.5206000000000001,0.0177,0.0049,Annie,Kayle,Fizz
+12,Hwei,2,mid,0.503,0.0587,0.0553,Malphite,Katarina,Talon
+13,Syndra,2,mid,0.5013000000000001,0.061,0.038,Ekko,Katarina,Aurelion Sol
+14,Talon,2,mid,0.5112,0.0287,0.0089,Malphite,Garen,Kayle
+15,Aurora,2,mid,0.5015,0.0518,0.11259999999999999,Vel'Koz,Vex,Gragas
+16,Lissandra,2,mid,0.5096,0.0316,0.014499999999999999,Cho'Gath,Corki,Lux
+17,Malzahar,2,mid,0.5067,0.0368,0.0359,Kayle,Garen,Swain
+18,Akshan,2,mid,0.5127,0.0227,0.0207,Anivia,Kayle,Annie
+19,Qiyana,2,mid,0.5132,0.022000000000000002,0.0054,Neeko,Garen,Vel'Koz
+20,Veigar,2,mid,0.5092,0.0285,0.005699999999999999,Cassiopeia,Naafiri,Xerath
+21,Ekko,2,mid,0.5074000000000001,0.030899999999999997,0.0058,Garen,Qiyana,Kassadin
+22,Cassiopeia,2,mid,0.5166,0.015700000000000002,0.0096,Neeko,Talon,Vel'Koz
+23,Vel'Koz,2,mid,0.5186999999999999,0.013300000000000001,0.0028000000000000004,Naafiri,Ziggs,Katarina
+24,Xerath,2,mid,0.5059,0.0319,0.0253,Garen,Naafiri,Fizz
+25,Yasuo,2,mid,0.4921,0.077,0.1255,Annie,Taliyah,Vex
+26,Diana,2,mid,0.5091,0.024700000000000003,0.0074,Pantheon,Neeko,Brand
+27,Ryze,2,mid,0.5083,0.0271,0.0033,Cassiopeia,Neeko,Taliyah
+28,Zoe,2,mid,0.5088,0.0241,0.0102,Naafiri,Garen,Malzahar
+29,Zed,2,mid,0.4941,0.059000000000000004,0.1274,Kayle,Cho'Gath,Akshan
+30,Kassadin,2,mid,0.5061,0.0265,0.031,Brand,Naafiri,Cho'Gath
+31,Garen,3,mid,0.5259,0.0053,0.0026,Annie,Ryze,Aurelion Sol
+32,Vladimir,3,mid,0.5017,0.0326,0.026699999999999998,Anivia,Cho'Gath,Vel'Koz
+33,Aurelion Sol,3,mid,0.5081,0.019799999999999998,0.0070999999999999995,Vel'Koz,Zed,Fizz
+34,Fizz,3,mid,0.5024000000000001,0.0297,0.0335,Akali,Garen,Neeko
+35,Naafiri,3,mid,0.5157,0.01,0.0040999999999999995,Cho'Gath,Neeko,Kayle
+36,Neeko,3,mid,0.5201,0.0063,0.002,Taliyah,Lissandra,Vladimir
+37,Twisted Fate,3,mid,0.5003,0.026600000000000002,0.0023,Naafiri,Neeko,Diana
+38,Orianna,3,mid,0.49369999999999997,0.0429,0.0079,Ziggs,Garen,Cho'Gath
+39,Annie,3,mid,0.512,0.0102,0.0016,Neeko,Gragas,Talon
+40,Gragas,3,mid,0.5106,0.0102,0.0048,Cho'Gath,Vel'Koz,Naafiri
+41,Yone,3,mid,0.48229999999999995,0.0746,0.0588,Cho'Gath,Vex,Akshan
+42,Cho'Gath,3,mid,0.5186,0.0051,0.0008,Ziggs,Vel'Koz,Qiyana
+43,LeBlanc,3,mid,0.48700000000000004,0.0484,0.17420000000000002,Garen,Naafiri,Pantheon
+44,Malphite,3,mid,0.5108,0.0076,0.010700000000000001,Cassiopeia,Lissandra,Sylas
+45,Kayle,3,mid,0.5129,0.006500000000000001,0.0019,Neeko,Annie,Corki
+46,Swain,3,mid,0.4966,0.0177,0.0105,Kayle,Cassiopeia,Xerath
+47,Pantheon,4,mid,0.5075,0.0066,0.0015,Vel'Koz,Swain,Anivia
+48,Ambessa,4,mid,0.4959,0.0069,0.038,Cho'Gath,Corki,Malphite
+49,Azir,4,mid,0.48590000000000005,0.0222,0.003,Annie,Kayle,Neeko
+50,Jayce,4,mid,0.4842,0.0213,0.0067,Naafiri,Brand,Vel'Koz
+51,Brand,4,mid,0.5028,0.0051,0.0026,Garen,Anivia,Irelia
+52,Ziggs,4,mid,0.4899,0.0066,0.0017000000000000001,Brand,Neeko,Garen
+53,Corki,4,mid,0.4808,0.0096,0.0089,Garen,Cho'Gath,Gragas
+54,Smolder,5,mid,0.4278,0.0166,0.0068000000000000005,Neeko,Diana,Kayle
+1,Viktor,1,adc,0.5273,0.022099999999999998,0.044000000000000004,Vayne,Karthus,Yasuo
+2,Nilah,1,adc,0.5359,0.018600000000000002,0.035,Veigar,Hwei,Brand
+3,Jinx,1,adc,0.5055,0.2144,0.0426,Lux,Seraphine,Viktor
+4,Miss Fortune,1,adc,0.5194,0.0662,0.0068000000000000005,Karthus,Seraphine,Yasuo
+5,Ashe,1,adc,0.5045000000000001,0.16219999999999998,0.182,Karthus,Lux,Nilah
+6,Kog'Maw,2,adc,0.5257999999999999,0.0256,0.0172,Karthus,Seraphine,Ziggs
+7,Jhin,2,adc,0.5006,0.1929,0.038,Yasuo,Nilah,Seraphine
+8,Vayne,2,adc,0.5156000000000001,0.0472,0.026099999999999998,Nilah,Veigar,Miss Fortune
+9,Caitlyn,2,adc,0.4954,0.1984,0.2798,Lux,Seraphine,Brand
+10,Corki,2,adc,0.5082,0.0674,0.063,Veigar,Kog'Maw,Seraphine
+11,Karthus,2,adc,0.5359,0.0059,0.0127,Nilah,Hwei,Vayne
+12,Seraphine,2,adc,0.5292,0.0101,0.0022,Veigar,Brand,Karthus
+13,Sivir,2,adc,0.5102,0.049699999999999994,0.009300000000000001,Viktor,Seraphine,Kog'Maw
+14,Draven,2,adc,0.5057,0.062400000000000004,0.2262,Lux,Seraphine,Swain
+15,Lux,2,adc,0.5274,0.009899999999999999,0.0039000000000000003,Karthus,Ziggs,Lucian
+16,Hwei,2,adc,0.5232,0.013500000000000002,0.0128,Viktor,Brand,Sivir
+17,Ezreal,3,adc,0.4907,0.1653,0.0509,Karthus,Nilah,Yasuo
+18,Ziggs,3,adc,0.5165,0.020499999999999997,0.0053,Yasuo,Karthus,Viktor
+19,Xayah,3,adc,0.5086999999999999,0.0398,0.0058,Viktor,Miss Fortune,Kog'Maw
+20,Lucian,3,adc,0.5034000000000001,0.0603,0.0089,Karthus,Brand,Nilah
+21,Yasuo,3,adc,0.519,0.0106,0.0174,Nilah,Karthus,Corki
+22,Brand,4,adc,0.5242,0.0078000000000000005,0.0040999999999999995,Karthus,Lux,Yasuo
+23,Swain,4,adc,0.514,0.0155,0.009300000000000001,Veigar,Vayne,Lux
+24,Kai'Sa,4,adc,0.4892,0.1195,0.017,Karthus,Nilah,Veigar
+25,Tristana,4,adc,0.5049,0.0342,0.0037,Karthus,Yasuo,Corki
+26,Varus,4,adc,0.4951,0.0719,0.0111,Hwei,Ziggs,Karthus
+27,Veigar,4,adc,0.5214,0.0067,0.0013,Karthus,Lux,Ziggs
+28,Smolder,4,adc,0.4941,0.061900000000000004,0.025699999999999997,Brand,Lux,Yasuo
+29,Twitch,4,adc,0.5011,0.0323,0.0141,Karthus,Nilah,Yasuo
+30,Zeri,5,adc,0.4985,0.0285,0.003,Karthus,Brand,Swain
+31,Samira,5,adc,0.49219999999999997,0.034300000000000004,0.052199999999999996,Veigar,Viktor,Nilah
+32,Aphelios,5,adc,0.4851,0.0452,0.0070999999999999995,Brand,Nilah,Yasuo
+33,Kalista,5,adc,0.4828,0.0209,0.0054,Kog'Maw,Brand,Miss Fortune
+1,Lulu,1,support,0.5163,0.1495,0.1777,Fiddlesticks,Tahm Kench,Elise
+2,Nami,1,support,0.5131,0.10339999999999999,0.0101,Fiddlesticks,Elise,Galio
+3,Janna,2,support,0.5168,0.060700000000000004,0.0213,Zoe,Blitzcrank,Sona
+4,Tahm Kench,2,support,0.5175,0.0508,0.06269999999999999,Elise,Janna,Taric
+5,Thresh,2,support,0.5049,0.1288,0.0546,Elise,Taric,Vel'Koz
+6,Poppy,2,support,0.5196999999999999,0.034300000000000004,0.0535,Taric,Elise,Sona
+7,Braum,3,support,0.5111,0.062,0.054400000000000004,Senna,Vel'Koz,Taric
+8,Pyke,3,support,0.5035000000000001,0.0806,0.3077,Elise,Maokai,LeBlanc
+9,Senna,3,support,0.5104,0.0594,0.017,Elise,Vel'Koz,Neeko
+10,Taric,3,support,0.527,0.0174,0.0036,Fiddlesticks,Soraka,Janna
+11,Rell,3,support,0.5133,0.0464,0.0232,Taric,Janna,Poppy
+12,Soraka,3,support,0.5122,0.049100000000000005,0.018600000000000002,Elise,Galio,Sylas
+13,Karma,3,support,0.5018,0.0965,0.0398,Fiddlesticks,Maokai,Pyke
+14,Sona,3,support,0.5184000000000001,0.0263,0.0012,Zac,Elise,Camille
+15,Vel'Koz,3,support,0.5149,0.030600000000000002,0.0064,Camille,Elise,LeBlanc
+16,Bard,3,support,0.5091,0.044199999999999996,0.0060999999999999995,Elise,Fiddlesticks,Tahm Kench
+17,Milio,3,support,0.5046,0.0593,0.0326,Maokai,Tahm Kench,Blitzcrank
+18,Elise,3,support,0.5292,0.0077,0.0086,Galio,Neeko,Pantheon
+19,Maokai,3,support,0.5118,0.027999999999999997,0.0146,Taric,Galio,Elise
+20,Leona,4,support,0.49829999999999997,0.0572,0.0496,Galio,Janna,Taric
+21,Zilean,4,support,0.5074000000000001,0.028900000000000002,0.008100000000000001,Galio,LeBlanc,Sona
+22,Blitzcrank,4,support,0.49560000000000004,0.0604,0.1734,Elise,Taric,Braum
+23,Lux,4,support,0.4925,0.0703,0.0275,Shen,Poppy,LeBlanc
+24,Zyra,4,support,0.5045000000000001,0.030899999999999997,0.0182,Taric,Sylas,Tahm Kench
+25,Xerath,4,support,0.5003,0.039,0.0304,Elise,Shen,Zoe
+26,Rakan,4,support,0.4982,0.046900000000000004,0.0063,Sona,Elise,Janna
+27,Nautilus,4,support,0.48840000000000006,0.0814,0.12380000000000001,Taric,Renata Glasc,Rell
+28,Morgana,4,support,0.4997,0.0353,0.1139,Shaco,Sona,Xerath
+29,Seraphine,4,support,0.4984,0.0374,0.008,Elise,Vel'Koz,Poppy
+30,Yuumi,4,support,0.4888,0.0588,0.07200000000000001,Taric,Rell,Braum
+31,Neeko,4,support,0.5045000000000001,0.0196,0.0063,Fiddlesticks,Camille,Janna
+32,Pantheon,4,support,0.5011,0.019299999999999998,0.0044,Taric,LeBlanc,Braum
+33,Alistar,4,support,0.4918,0.0365,0.0124,Janna,Soraka,Morgana
+34,Renata Glasc,4,support,0.4984,0.0213,0.0037,Vel'Koz,Xerath,Maokai
+35,LeBlanc,5,support,0.5043,0.0095,0.0336,Taric,Galio,Fiddlesticks
+36,Brand,5,support,0.4968,0.0192,0.0098,Galio,Camille,Shaco
+37,Shaco,5,support,0.4976,0.014199999999999999,0.0391,Hwei,Zoe,Lulu
+38,Sylas,5,support,0.4971,0.014199999999999999,0.0134,Poppy,Renata Glasc,Taric
+39,Galio,5,support,0.5039,0.008199999999999999,0.0055000000000000005,Zac,Milio,Janna
+40,Zoe,5,support,0.5001,0.009899999999999999,0.0040999999999999995,Taric,Elise,Poppy
+41,Fiddlesticks,5,support,0.5071,0.0052,0.0028000000000000004,Maokai,Poppy,Brand
+42,Shen,5,support,0.5006,0.0058,0.0007000000000000001,Galio,Taric,Swain
+43,Camille,5,support,0.4991,0.0059,0.0027,Galio,Elise,LeBlanc
+44,Swain,5,support,0.47609999999999997,0.022799999999999997,0.0134,Fiddlesticks,Sona,Vel'Koz
+45,Hwei,5,support,0.48840000000000006,0.0087,0.008,LeBlanc,Zoe,Taric
+46,Zac,5,support,0.4827,0.0054,0.0031,Taric,LeBlanc,Poppy

util/data/player_stats_merged_2025-01-08.csv

@@ -0,0 +1,21 @@
+player_id,region,date,champion,level,team,result,match_length_mins,kill,death,assist,kda_ratio,kill_participation,laning,cs,cs_per_min,avg_tier,teammates1,team_champ1,teammates2,team_champ2,teammates3,team_champ3,teammates4,team_champ4,oppmates1,opp_champ1,oppmates2,opp_champ2,oppmates3,opp_champ3,oppmates4,opp_champ4,oppmates5,opp_champ5,total_games,wins,losses,win_rate,avg_kills,avg_deaths,avg_assists,kda_ratio_profile,kill_participation_profile,most_champ_1,WR_1,W_1,L_1,KDA_1,most_champ_2,WR_2,W_2,L_2,KDA_2,most_champ_3,WR_3,W_3,L_3,KDA_3,TOP,JUNGLE,MID,ADC,SUPPORT,most_role_1,most_role_2,most_role_1_value,most_role_2_value,season_champ_1,cs_ssn_1,cpm_ssn_1,kda_ssn_1,k_ssn_1,d_ssn_1,a_ssn_1,wr_ssn_1,games_ssn_1,season_champ_2,cs_ssn_2,cpm_ssn_2,kda_ssn_2,k_ssn_2,d_ssn_2,a_ssn_2,wr_ssn_2,games_ssn_2,season_champ_3,cs_ssn_3,cpm_ssn_3,kda_ssn_3,k_ssn_3,d_ssn_3,a_ssn_3,wr_ssn_3,games_ssn_3,season_champ_4,cs_ssn_4,cpm_ssn_4,kda_ssn_4,k_ssn_4,d_ssn_4,a_ssn_4,wr_ssn_4,games_ssn_4,season_champ_5,cs_ssn_5,cpm_ssn_5,kda_ssn_5,k_ssn_5,d_ssn_5,a_ssn_5,wr_ssn_5,games_ssn_5,season_champ_6,cs_ssn_6,cpm_ssn_6,kda_ssn_6,k_ssn_6,d_ssn_6,a_ssn_6,wr_ssn_6,games_ssn_6,season_champ_7,cs_ssn_7,cpm_ssn_7,kda_ssn_7,k_ssn_7,d_ssn_7,a_ssn_7,wr_ssn_7,games_ssn_7,7d_champ_1,7d_total_1,7d_W_1,7d_L_1,7d_WR_1,7d_champ_2,7d_total_2,7d_W_2,7d_L_2,7d_WR_2,7d_champ_3,7d_total_3,7d_W_3,7d_L_3,7d_WR_3,mastery_champ_1,m_lv_1,mastery_champ_2,m_lv_2,mastery_champ_3,m_lv_3,mastery_champ_4,m_lv_4,mastery_champ_5,m_lv_5,mastery_champ_6,m_lv_6,mastery_champ_7,m_lv_7,mastery_champ_8,m_lv_8,mastery_champ_9,m_lv_9,mastery_champ_10,m_lv_10,mastery_champ_11,m_lv_11,mastery_champ_12,m_lv_12,mastery_champ_13,m_lv_13,mastery_champ_14,m_lv_14,mastery_champ_15,m_lv_15,mastery_champ_16,m_lv_16
+Agurin-EUW,euw,"Sun, Jan 5, 2025 6:25 PM",Jarvan IV,16,blue,1,26.68,9,2,19,14.00,0.67,1.08,183,6.9,2,skygge kriger #EUV,Rumble,성진우 #789,Vladimir,JEZUUU #0814,Yasuo,Papadodi #1010,Lulu,the inescapable #EUW,Maokai,edge of night #999,Kindred,Macaquiño #GAP,LeBlanc,Wanna be a pro #03120,Corki,Walid Georgey #EUW,Rell,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sun, Jan 5, 2025 5:48 PM",Wukong,16,red,1,28.68,3,5,11,2.80,0.47,1.33,220,7.7,2,S8DiamondDog #EUW,Yasuo,Owen Mcdade #BLACK,Fiora,AllYouHadToDoWas #Stay,Karthus,Depressed Loser #ff15,LeBlanc,Drali #Drali,K'Sante,Kiki #COYG,Viego,222 #EUW3,Syndra,JEZUUU #0814,Xayah,grisha turbina #ADHD,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sun, Jan 5, 2025 5:10 PM",Wukong,12,blue,0,20.65,6,4,2,2.00,0.57,0.89,153,7.4,2,Novachrono #Back,Cho'Gath,2025 #favo,Gragas,Jeyrus #Jey,Corki,Papadodi #1010,Lulu,S8DiamondDog #EUW,Dr. Mundo,Thomas Shelby #SOLOQ,Vi,222 #EUW3,Ekko,JEZUUU #0814,Caitlyn,Pablo #boda,Bard,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sun, Jan 5, 2025 4:30 PM",Elise,15,blue,1,24.22,10,3,6,5.33,0.62,1.08,194,8.0,2,SatØrius #EUW,Rumble,AbbedaggÆ #EUW,Jayce,Kite Machine #EUW,Jhin,Vento #TAIF,Bard,KOI Spooder #1352,K'Sante,NazgulsBlade #LOTR,Skarner,TwitchTVxKenzuke #EUW,Aurora,Unkn0wn #EUW11,Ezreal,Paolocannone #EUW,Karma,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sun, Jan 5, 2025 1:34 AM",Wukong,14,blue,0,26.37,7,10,4,1.10,0.65,0.89,169,6.4,2,Vandett0 #ETABL,Teemo,Grunge #010,Yone,222 #EUW3,Ekko,Crixus #ARENA,Leona,SenSen #EUWFR,Nidalee,AllYouHadToDoWas #Stay,Karthus,SRTSS #EUW,Corki,Keduii #GOAT,Caitlyn,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sun, Jan 5, 2025 12:48 AM",Wukong,17,blue,1,31.83,4,3,4,2.67,0.33,1.13,267,8.4,2,Bobsik #EUW,Camille,SRTSS #EUW,LeBlanc,PsychoZedPlayer #EUW,Hwei,mein herz #197,Janna,Patrick Jane #Tea,Vladimir,Seonsu #Noula,Viego,Odysseus #131,Taliyah,Keduii #GOAT,Ezreal,Crixus #ARENA,Zilean,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 11:56 PM",Wukong,16,red,1,25.28,7,2,7,7.00,0.61,0.89,208,8.2,2,Szygenda #EUW,Aurora,Dr Orange #Caps,Galio,J1HUIV #000,Kalista,ttveminencjasupp #kick,Renata Glasc,Patrick Jane #Tea,Vladimir,Thors Snoresson #3327,Graves,222 #EUW3,Syndra,LeonardoDiCassio #EUW,Caitlyn,Maynter #EUW,Nautilus,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 10:39 PM",Wukong,13,red,1,21.02,5,1,5,10.00,0.56,1.44,147,7.0,2,Desperate Nasus #EUW,Kayle,Yoo Jonghyuk333 #EUW,Aurora,LeonardoDiCassio #EUW,Viktor,Canam #123,Nami,Makkro #EUW,Ornn,Kiki #COYG,Skarner,SRTSS #EUW,Yone,avarice61 #EUW,Kog'Maw,Sergeant Sona #KJP,Seraphine,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 10:04 PM",Elise,17,blue,1,25.4,15,3,6,7.00,0.53,1.56,225,8.9,2,choilot #LOT,Gnar,a winds rhythm #Aang,Yasuo,Boring Cycle #Abner,Swain,ataraxia #7z7,Bard,Riihonen #EUW,Vladimir,AkaManto #LGM,Hecarim,SRTSS #EUW,Yone,Keduii #GOAT,Ashe,Peak Level #123,Zac,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 9:01 PM",Wukong,13,blue,0,27.2,6,11,5,1.00,0.58,0.82,159,5.8,2,Ronaldo Nazário #12345,Irelia,TATA #HOPE,Galio,chöego #EUW,Corki,Sergeant Sona #KJP,Sona,drunkenztain #EUW,Fiddlesticks,Foutriquet #EUW,Jarvan IV,silkysmath #6206,Syndra,Kushala Daora #MHVV,Caitlyn,darkchri99 #EUW,Lux,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 8:23 PM",Elise,12,red,0,24.83,1,6,2,0.50,0.2,0.92,163,6.6,2,Bent246 #Bent,Akshan,c0cky movement #tukan,Irelia,fjgyqwjsaqxca #LUDER,Jinx,gummyjuiceULTRA #11cmP,Pyke,أستولفو بن لادن #UwU,Singed,bluerzor #grind,Wukong,나프켈라 #BOAT,Akali,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Jhin,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 7:42 PM",Wukong,14,blue,1,21.82,5,2,8,6.50,0.39,1.0,174,8.0,2,skygge kriger #EUV,Aatrox,bvffi #meow,Corki,Kushala Daora #MHVV,Hwei,Zoo port #LOL,Zoe,c0cky movement #tukan,Yone,noname #gromp,Taliyah,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Cho'Gath,twtv exofeng #IWNL,Draven,Sergeant Sona #KJP,Rakan,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 7:13 PM",Wukong,14,red,0,24.2,3,6,7,1.67,0.53,0.28,172,7.1,2,DREILIX #000,Jayce,Bobsbag #EUW,Gragas,Rafitta #EUW,Corki,eXoValdo #EUW,Brand,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Sion,wοkeupIikethis #EUW,Viego,Eternal Blizzard #carot,Hwei,PAKO ETERNAL #EUW,Ezreal,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Sat, Jan 4, 2025 12:41 AM",Wukong,17,blue,1,34.25,5,11,18,2.09,0.51,0.67,194,5.7,2,batubabaHOPPALA #HOPPA,Riven,Fool Night #Ivy,Veigar,Sintemon #gumo,Ashe,ScoobaTroopah #10000,Blitzcrank,Pablo #boda,Aurora,TWTV DETDERT #12MAJ,Graves,J1HUIV #000,Karma,Mandaryñ #EUW,Corki,erdt #EUW,Leona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Fri, Jan 3, 2025 11:54 PM",Wukong,15,red,1,23.03,7,3,5,4.00,0.48,1.56,197,8.6,2,Fool Night #Ivy,Aurora,Dr Orange #Caps,Sylas,Sintemon #gumo,Ashe,ScoobaTroopah #10000,Blitzcrank,modernillo #moder,Ambessa,Eternal Blizzard #carot,Viego,J1HUIV #000,Karma,Beanovich #bean,Varus,niudefuwuqi2007 #8085,Braum,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Fri, Jan 3, 2025 11:23 PM",Jarvan IV,17,blue,1,31.22,1,4,20,5.25,0.54,0.67,217,7.0,2,Vertigo #4810,Ambessa,LëFacteur Chance #EUW,Akali,Chrisberg #6103,Corki,raikooooo #EUW,Blitzcrank,modernillo #moder,Gragas,mikusik998 #gaz,Bel'Veth,Imma touch you #131,Hwei,Beanovich #bean,Kalista,Jordæn #EUW,Pantheon,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Fri, Jan 3, 2025 10:40 PM",Elise,16,red,1,29.27,6,3,18,8.00,0.71,1.27,197,6.7,2,blueskies777 #EUW,Renekton,Return to dust #Dust,Viktor,Beanovich #bean,Jinx,twojstary2komary #win,Rakan,나프켈라 #BOAT,Jax,bluerzor #grind,Skarner,LëFacteur Chance #EUW,Akali,Dr Mango #Hans,Kalista,Sergeant Sona #KJP,Lulu,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Fri, Jan 3, 2025 9:59 PM",Wukong,15,red,1,23.2,10,3,4,4.67,0.54,1.38,203,8.8,2,yuqi #golem,Galio,Mein Königreich #917n3,Sylas,batubabaHOPPALA #HOPPA,Varus,MORG3NSHTERN #EUW,Rakan,KOI Spooder #1352,Rumble,mikusik998 #gaz,Bel'Veth,RKR #123,Viktor,supershy #jblss,Ashe,Drututts Lisp #EUW,Gragas,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Fri, Jan 3, 2025 9:13 PM",Elise,16,red,1,28.07,8,2,12,10.00,0.51,0.75,207,7.4,1,washed úp #EUW,Gwen,SMRADOCH RANDOM #EUW,Ziggs,RKR #123,Ashe,Frank Castle #2610,Blitzcrank,Shourdy #EUW,K'Sante,Purple Place #Moe,Rengar,WORLDS2025 #AIS,Irelia,Odysseus #131,Taliyah,Sergeant Sona #KJP,Sona,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0
+Agurin-EUW,euw,"Fri, Jan 3, 2025 8:38 PM",Wukong,12,red,0,21.68,3,5,7,2.00,0.48,0.67,141,6.5,2,e7c3c0fd cee3 4399 877b baf89c9f6f46 #EUW,Volibear,wymówkomat #escik,Ahri,Percy Magic #1234,Kalista,cross the edge #fear,Nautilus,ΑrchΦiend #666,Aatrox,Razørk Activoo #razzz,Xin Zhao,yebit #haha,Taliyah,RKR #123,Ashe,Shourdy #EUW,Sylas,20,14,6,0.7,6.1,4.5,8.5,3.24,0.53,Wukong,0.62,8,5,2.39,Elise,0.8,4,1,4.94,Jarvan IV,1.0,2,0,8.17,0.0,1.0,0.0,0.0,0.0,JUNGLE,MID,1.0,0.0,Wukong,198,7.2,2.99,6.2,4.6,7.6,0.61,193,Elise,195.4,7.2,3.1,6.7,4.5,7.2,0.58,138,Neeko,205.5,7.7,3.26,6.5,4.1,6.9,0.49,82,Jarvan IV,196.2,7,3.94,5.8,4.6,12.4,0.55,65,Karma,165.5,6.6,1.92,2.9,5.5,7.7,0.55,60,Kha'Zix,213.1,7.8,2.35,5.4,5.2,6.8,0.45,42,Nocturne,194.1,7.6,2.86,4.9,4.4,7.5,0.53,34,Wukong,27,18,9,0.67,Jarvan IV,9,6,3,0.67,Elise,8,5,3,0.63,Elise,170,Kha'Zix,162,Jarvan IV,129,Shyvana,51,Nocturne,46,Evelynn,40,Wukong,28,Ezreal,25,Brand,25,Diana,16,Karma,15,Gragas,13,Zyra,12,Neeko,12,Ahri,10,,0

util/data/weekly_meta_stats.csv

@@ -0,0 +1,170 @@
+rank,champion,games,KDA,WR,pick,ban,cs,gold
+1,Viego,760,3.17,52.63,27.68,2.24,178.06,11606
+2,Viktor,750,2.41,49.47,27.32,38.61,207.51,11291
+3,Corki,593,2.53,52.61,2.16,12.27,213.81,11783
+4,Ezreal,559,2.67,50.63,20.36,8.01,200.67,11262
+5,Lee Sin,532,3.46,54.14,19.38,10.05,164.78,11453
+6,Jhin,507,3.28,53.65,18.47,1.17,200.76,12199
+7,Jayce,504,2.05,48.81,18.36,4.39,196.81,11154
+8,Yone,485,1.95,48.66,17.67,4.53,214.17,11573
+9,Sylas,469,2.4,50.75,17.08,8.61,158.36,10355
+10,K'Sante,468,2.52,53.21,17.05,12.67,184.34,10361
+11,Ambessa,462,2.14,4.87,16.83,30.54,185,10813
+12,Aurora,445,2.66,47.64,16.21,24.95,194.38,11137
+13,Irelia,411,1.64,49.88,14.97,12.86,219.41,12287
+14,Ashe,406,2.49,47.29,14.79,23.32,193.82,10898
+15,Wukong,399,2.9,51.88,14.53,8.29,177.15,11337
+16,Graves,394,2.96,52.54,14.35,1.94,199.89,12615
+17,Caitlyn,351,2.45,53.28,12.79,14.73,209.86,12096
+18,Nautilus,342,2.53,49.42,12.46,7.74,25.32,7240
+19,Varus,337,2.49,52.82,12.28,5.16,195.14,11350
+20,Skarner,332,3.82,53.92,12.09,26.68,143.07,10124
+21,Yasuo,327,1.68,48.32,11.91,1.77,212.88,11655
+22,Renekton,313,2.16,48.24,1.14,1.02,203.99,11361
+23,Akali,306,2.63,47.71,11.15,9.75,186.42,11203
+24,Hwei,306,2.6,46.08,11.15,6.52,192.2,10838
+25,Lulu,305,3.37,52.46,11.11,11.83,32.77,7476
+26,Gnar,292,1.97,45.55,10.64,6.25,195.43,11034
+27,Pyke,273,2.38,47.25,9.94,45.36,40.46,9196
+28,Karma,270,3.22,55.93,9.83,3.19,44.89,8080
+29,Rakan,270,3.24,4.63,9.83,1.05,27.77,7221
+30,Jinx,268,2.28,44.03,9.76,2.18,199.54,11034
+31,Kai'Sa,266,2.71,47.37,9.69,0.91,208.07,11989
+32,Rell,265,3.24,49.06,9.65,3.18,23.9,7443
+33,Jax,264,2.07,56.82,9.62,11.66,183.78,11208
+34,Poppy,262,2.46,51.15,9.54,1.52,56.66,8261
+35,Taliyah,257,2.69,50.58,9.36,2.23,199.33,11689
+36,Bard,254,3.67,53.54,9.25,1.43,26.46,7628
+37,Talon,249,2.9,54.62,9.07,7.57,202.84,12227
+38,Vi,228,2.97,46.93,0.83,0.11,165.43,10646
+39,Galio,222,3.01,55.41,8.09,4.06,161.87,9825
+40,Lux,222,3.17,5.36,8.09,1.74,132.95,9845
+41,Elise,222,2.77,52.25,8.09,10.29,122.33,10790
+42,Camille,218,2.14,54.59,7.94,1.99,169.03,11136
+43,Zed,212,2.77,0.5,7.72,13.73,200.28,12429
+44,Syndra,211,2.59,53.08,7.69,4.57,208.25,11529
+45,Nidalee,210,3.45,0.5,7.65,8.84,170.11,11411
+46,Kindred,210,2.57,47.14,7.65,8.86,174.7,11719
+47,Maokai,208,2.16,45.19,7.58,0.18,110.61,8332
+48,Gragas,205,2.4,49.27,7.47,4.85,169.43,10303
+49,Ahri,202,3.17,52.97,7.36,0.22,196.68,11003
+50,Draven,199,2.14,48.74,7.25,33.07,200.71,13185
+51,Jarvan IV,195,3.69,51.79,0.71,1.72,161.92,10821
+52,Zyra,192,3.16,55.21,6.99,5.48,188.71,11473
+53,Neeko,190,2.81,52.63,6.92,1.18,63.94,8500
+54,Ekko,185,3.05,50.27,6.83,2.22,197.31,11848
+55,Sona,184,3.19,53.26,6.79,1.36,26.69,7799
+56,Diana,177,2.59,57.63,6.45,1.45,192.18,11840
+57,Garen,169,2.25,52.66,6.16,2.41,205.02,11777
+58,Riven,160,2.22,51.25,5.83,8.06,200.78,11609
+59,Pantheon,160,2.16,40.63,5.83,1.42,147.55,10426
+60,Braum,159,3.08,50.94,5.79,5.33,23.71,7133
+61,Aatrox,158,2.08,4.81,5.76,4.89,177.53,10395
+62,Janna,155,3.88,53.55,5.72,3.86,24.06,7465
+63,Tristana,153,2.63,55.56,5.64,0.54,207.52,12824
+64,Gwen,153,1.81,40.52,5.57,2.68,191.23,11212
+65,Zac,150,3.37,54.67,5.46,5.14,144.34,9863
+66,Swain,148,2.87,51.35,5.46,1.34,180.99,10870
+67,Orianna,144,3.18,56.25,5.25,1.01,197.33,10605
+68,Blitzcrank,144,2.66,52.08,5.25,0.85,23.44,7164
+69,Nami,144,3.87,49.31,5.25,0.56,23.22,7603
+70,Kha'Zix,142,2.85,45.77,5.17,0.35,170.93,11456
+71,Senna,141,2.96,54.61,5.14,0.78,31.61,8331
+72,Leona,141,2.55,47.52,5.14,2.01,25.09,7187
+73,Thresh,139,3.11,49.64,5.06,3.31,26.56,7183
+74,Rumble,136,2.24,44.85,4.95,1.45,186.17,10817
+75,Kalista,134,2.39,43.28,4.88,3.01,202.46,12187
+76,Smolder,133,2.36,45.11,4.84,1.27,223.16,12163
+77,Lucian,127,2.51,52.76,4.63,0.28,208.8,11887
+78,Ziggs,127,2.99,50.39,4.63,0.67,215.27,11706
+79,LeBlanc,124,2.48,41.94,4.52,22.71,156.94,9891
+80,Fiora,123,1.92,57.72,4.48,7.97,207.29,12135
+81,Qiyana,120,2.75,61.67,4.37,4.05,180.3,11951
+82,Azir,119,2.34,53.78,4.33,1.37,212.93,11549
+83,Vladimir,119,2.65,44.54,4.33,5.57,201.12,11094
+84,Aphelios,118,2.29,49.15,4.35,0.52,202.92,11660
+85,Xerath,117,3.64,53.85,4.26,0.02,154.71,10485
+86,Kennen,113,2.28,57.52,4.12,3.15,188.5,11130
+87,Volibear,110,2.08,47.27,4.06,1.47,181.27,10689
+88,Seraphine,109,3.09,54.13,4.02,2.01,90.32,8859
+89,Bel'Veth,109,3.04,48.62,3.97,1.06,176.42,11627
+90,Xin Zhao,105,2.92,54.29,3.82,0.33,164.17,10876
+91,Zoe,102,2.97,52.94,3.72,4.39,106.28,9690
+92,Dr. Mundo,95,2.52,44.21,3.46,0.94,195.67,11028
+93,Darius,94,1.99,51.06,3.42,8.42,199.72,11408
+94,Ryze,92,2.35,54.35,3.35,0.86,214.32,11409
+95,Teemo,91,2.28,57.14,3.31,2.42,206.65,11535
+96,Lissandra,91,2.71,50.55,3.31,0.95,179.89,10267
+97,Nocturne,91,3.11,46.15,3.31,0.16,177.43,11299
+98,Karthus,90,2.39,44.44,3.28,12.85,197.17,12214
+99,Lillia,89,3.35,58.43,3.24,1.38,190.69,11350
+100,Rengar,89,2.38,44.94,3.24,20.09,176.18,11359
+101,Alistar,89,2.79,4.27,3.24,1.57,22.94,6940
+102,Miss Fortune,88,2.79,46.59,3.21,0.36,213.86,11953
+103,Kayn,86,2.67,55.81,3.13,4.19,184.67,11786
+104,Trundle,86,1.51,0.5,3.17,0.18,204.58,12245
+105,Zilean,85,3.14,47.06,0.31,0.17,92.91,7722
+106,Zeri,84,2.4,40.48,3.06,0.37,224.45,12286
+107,Xayah,83,2.44,45.78,3.02,0.35,221.17,12281
+108,Samira,80,2.29,51.25,2.91,2.67,183.21,12175
+109,Soraka,80,3.09,46.25,2.91,1.87,39.89,7706
+110,Hecarim,79,2.9,43.04,2.88,4.64,193.19,11339
+111,Nunu & Willump,78,2.43,33.33,2.84,3.54,142.21,9073
+112,Kayle,77,2.25,49.35,0.28,0.83,225.47,12285
+113,Udyr,74,2.82,5.27,2.73,4.76,179.85,10322
+114,Fiddlesticks,73,2.83,57.53,2.66,5.58,74.84,9529
+115,Shaco,73,2.5,49.32,2.66,9.15,121.48,10403
+116,Shen,72,2.93,52.78,2.66,0.55,131.83,8504
+117,Briar,71,2.54,56.34,2.62,1.65,154.52,11786
+118,Gangplank,70,2.24,61.43,2.58,0.38,206.49,12713
+119,Akshan,69,2.94,49.28,2.51,4.47,190.16,12380
+120,Renata Glasc,67,2.98,52.24,2.44,0.52,28.27,7606
+121,Milio,67,4.03,47.76,2.44,2.31,24.31,7352
+122,Veigar,65,2.52,58.46,0.24,0.26,204.69,12145
+123,Yuumi,65,4.28,43.08,2.37,5.03,15.05,7028
+124,Kassadin,64,2.87,54.69,2.33,3.22,192.22,11845
+125,Katarina,63,2.73,55.56,2.29,5.67,186.57,12147
+126,Cho'Gath,63,2.45,53.97,2.29,0.27,189.78,10459
+127,Taric,63,2.39,46.03,2.29,0.48,43.49,7062
+128,Sivir,61,2.58,50.82,2.22,0.28,231.49,12104
+129,Ivern,61,4.63,49.18,2.22,1.33,137.18,9604
+130,Olaf,58,2.06,53.45,2.14,1.34,195.36,11604
+131,Malphite,58,2.29,53.45,2.11,5.63,169.38,9948
+132,Rammus,58,3.44,51.72,2.11,1.77,158.72,10681
+133,Vayne,58,2.15,0.5,2.11,1.87,189.76,11569
+134,Warwick,58,1.89,37.93,2.11,0.89,168.67,11066
+135,Evelynn,57,3.79,63.16,2.08,3.45,156.91,10804
+136,Aurelion Sol,57,3.56,57.89,2.08,0.46,206.32,11735
+137,Rek'Sai,57,3.68,52.63,0.21,0.77,179.51,12298
+138,Singed,57,2.03,42.11,2.08,0.88,172.35,9992
+139,Sejuani,55,3,49.09,0.02,0.06,139.36,9399
+140,Sett,55,1.84,47.27,2.03,0.12,179.38,11512
+141,Urgot,54,2.02,0.5,1.97,0.99,193.78,11833
+142,Tahm Kench,51,2.43,4.51,1.88,0.21,54.86,8513
+143,Tryndamere,50,1.95,0.58,1.82,0.25,240.58,13679
+144,Cassiopeia,50,1.95,0.4,1.82,1.88,197.78,11455
+145,Sion,48,1.54,47.92,1.75,0.24,211.04,12066
+146,Ornn,46,2.83,5.87,0.17,0.18,169.59,9579
+147,Twitch,46,2.84,54.35,1.68,0.09,162.09,12261
+148,Master Yi,45,2.25,51.11,1.66,0.75,177.09,12508
+149,Kog'Maw,45,1.93,28.89,1.66,0.14,195.02,11113
+150,Vex,44,2.63,56.82,0.16,1.66,189.75,11082
+151,Anivia,41,2.61,41.46,1.51,1.42,195.22,11045
+152,Twisted Fate,39,2.2,51.28,1.44,0.29,187.26,11260
+153,Yorick,39,1.43,43.59,1.42,0.49,186.97,10508
+154,Fizz,39,2.51,43.59,1.44,0.55,154.77,10855
+155,Vel'Koz,35,3.03,0.6,1.29,0.31,167.91,11602
+156,Mordekaiser,32,2.22,59.38,1.18,0.91,174.25,10702
+157,Nilah,31,2.14,58.06,1.13,3.41,201.23,12587
+158,Heimerdinger,31,1.99,41.94,1.14,0.68,169.45,9619
+159,Kled,30,2.26,66.67,1.11,0.27,188.8,12340
+160,Morgana,27,3.25,55.56,0.98,1.99,114.81,9350
+161,Naafiri,26,1.9,42.31,0.95,0.27,161.38,9767
+162,Brand,25,2.16,0.44,0.91,0.27,138.92,9463
+163,Annie,24,2.44,58.33,0.89,0.37,167,10856
+164,Malzahar,23,2.23,47.83,0.84,0.87,203.39,10805
+165,Shyvana,23,2.89,47.83,0.85,0.12,209.39,12167
+166,Quinn,21,1.95,52.38,0.76,0.73,157.57,11228
+167,Nasus,20,2.08,0.5,0.73,1.62,184.05,10645
+168,Amumu,17,1.72,29.41,0.62,1.15,125.41,8739
+169,Illaoi,16,1.95,31.25,0.59,1.11,211.13,12318

util/data_type_checking.py

@@ -0,0 +1,67 @@
+import os
+import pandas as pd
+
+def check_data_loading(file_path):
+    """
+    Debug function to check how the data is being loaded with proper encoding
+    """
+    # Try different encodings
+    encodings = ['utf-8', 'utf-8-sig', 'latin1', 'ISO-8859-1']
+    
+    for encoding in encodings:
+        try:
+            print(f"\nTrying encoding: {encoding}")
+            
+            # Read raw data first
+            with open(file_path, 'r', encoding=encoding) as f:
+                raw_lines = f.readlines()[:5]  # First 5 lines
+            print("Raw data first 5 lines:")
+            for line in raw_lines:
+                print(line.strip())
+
+            # Now read with pandas
+            df = pd.read_csv(file_path, encoding=encoding)
+            
+            # Check champion-related columns
+            champ_cols = [col for col in df.columns if '7d_champ' in col]
+            print("\nChampion columns:")
+            for col in champ_cols:
+                print(f"\n{col} unique values:")
+                print(df[col].value_counts().head())
+                print(f"Data type: {df[col].dtype}")
+
+            # Check specific row data
+            print("\nSample row check:")
+            sample_row = df.iloc[0]
+            for col in ['7d_champ_1', '7d_champ_2', '7d_champ_3']:
+                print(f"{col}: {sample_row[col]} (type: {type(sample_row[col])})")
+
+            print(f"\nSuccessfully read file with {encoding} encoding")
+            return df
+            
+        except UnicodeDecodeError:
+            print(f"Failed to read with {encoding} encoding")
+            continue
+        except Exception as e:
+            print(f"Error with {encoding} encoding: {str(e)}")
+            continue
+    
+    raise ValueError("Could not read file with any of the attempted encodings")
+
+# Use this function in your main code
+try:
+    #input_file = os.path.join("util", "data", f"player_stats_merged_2025-01-05_1.csv")
+    input_file = os.path.join("util", "data", f"training_feature.csv")
+    print(f"Checking file: {input_file}")
+    df_check = check_data_loading(input_file)
+    
+    # Additional validation
+    print("\nDataFrame Info:")
+    print(df_check.info())
+    
+    print("\nSample of champion columns:")
+    champ_cols = [col for col in df_check.columns if 'champ' in col.lower()]
+    print(df_check[champ_cols].head())
+    
+except Exception as e:
+    print(f"Error: {str(e)}")

util/feature_eng.py

@@ -0,0 +1,370 @@
+import os
+import pandas as pd
+import numpy as np
+from helper import ChampionConverter, process_kda_perfect
+
+def create_champion_features(merged_player_stats=None, meta_stats=None, weekly_meta=None, debug=None, consider_team_comp=True, test_mode=False):
+    """
+    Create features for champion prediction using player data.
+    Champion names will be used as column headers.
+    Uses pd.concat to avoid DataFrame fragmentation.
+    """
+    try:
+        if merged_player_stats is None:
+            print("Loading merged player stats...")
+            input_file = os.path.join("util", "data", "player_stats_merged.csv")
+            merged_player_stats = pd.read_csv(input_file, low_memory=False)
+            
+        #processing kda value
+        merged_player_stats = process_kda_perfect(merged_player_stats)
+   
+
+        if test_mode:
+            print("Test mode: Using only first 100 rows")
+            merged_player_stats = merged_player_stats.head(100)
+        
+        if meta_stats is None:
+            print("Loading meta stats...")
+            meta_file = os.path.join("util", "data", "meta_stats.csv")
+            meta_stats = pd.read_csv(meta_file, low_memory=False)
+
+        if weekly_meta is None:
+            print("Loading weekly meta stats...")
+            weekly_file = os.path.join("util", "data", "weekly_meta_stats.csv")
+            weekly_meta = pd.read_csv(weekly_file, low_memory=False)
+        
+        
+        # Initialize variables
+        debug_data = []
+        original_columns = merged_player_stats.columns.tolist()
+        feature_dict = {}
+
+        # Copy original columns
+        for col in merged_player_stats.columns:
+            feature_dict[col] = merged_player_stats[col].values.copy()
+
+
+        # Initialize the champion converter
+        converter = ChampionConverter()
+        all_champions = converter.champions
+        #total_champions = len(converter.champions)
+        
+            
+
+        # Get low tier champions and counter information
+        tier_penalties = {3: 0.9, 4: 0.85, 5: 0.8}
+
+        # Create tier_map as a dictionary of lists
+        tier_map = {}
+        for _, row in meta_stats.iterrows():
+            champ = row['champion']
+            tier = row['tier']
+            if pd.notna(tier):
+                if champ in tier_map:
+                    tier_map[champ].append(tier)
+                else:
+                    tier_map[champ] = [tier]
+
+        counter_map = {}
+        for _, row in meta_stats.iterrows():
+            if pd.notna(row['counter1']):
+                champ = row['champion']
+                counters = [row['counter1'], row['counter2'], row['counter3']]
+                if champ in counter_map:
+                    counter_map[champ].extend([c for c in counters if pd.notna(c)])
+                else:
+                    counter_map[champ] = [c for c in counters if pd.notna(c)]
+
+        # Ensure unique counters and remove duplicates
+        for champ, counters in counter_map.items():
+            counter_map[champ] = list(set(counters))
+
+        # Move 'champion' column to the first position
+        cols = ['champion'] + [col for col in merged_player_stats if col != 'champion']
+        merged_player_stats = merged_player_stats[cols]
+
+        # Define importance weights
+        weights = {
+            'recent': 0.3,    # Last 20 games
+            'weekly': 0.4,    # Last 7 days
+            'meta': 0.2,      # Only from weekly_stats
+            'season': 0.06,   # Current season
+            'mastery': 0.04   # All-time mastery
+        }
+
+        # Process rows in batches
+        batch_size = 100
+        total_rows = len(merged_player_stats)
+        
+        print(f"Total rows: {total_rows}")
+
+        for batch_start in range(0, total_rows, batch_size):
+            batch_end = min(batch_start + batch_size, total_rows)
+            batch_rows = merged_player_stats.iloc[batch_start:batch_end]
+            print(f"\nProcessing rows {batch_start} to {batch_end} ({batch_start/total_rows*100:.2f}% complete)")
+
+            # Initialize batch scores dictionary
+            batch_scores = {champion: np.zeros(len(batch_rows)) for champion in all_champions}
+            
+            # Process each row in this batch
+            for batch_idx, (idx, row) in enumerate(batch_rows.iterrows()):
+                # Process each champion for this row
+                for champion in all_champions:
+                    # Initialize scores for this champion and row
+                    champion_scores = {
+                        'recent_score': 0,
+                        'weekly_score': 0,
+                        'meta_score': 0,
+                        'season_score': 0,
+                        'mastery_score': 0
+                    }
+
+                    # Store debug info if needed
+                    base_score_before_penalty = 0
+                    counter_penalty = 0
+                    counter_debug = []
+
+                    # 1. Recent Performance
+                    for i in range(1, 4):
+                        if row.get(f'most_champ_{i}') == champion:
+                            wr = float(row[f'WR_{i}']) if pd.notna(row[f'WR_{i}']) else 0
+                            kda = float(row[f'KDA_{i}']) if pd.notna(row[f'KDA_{i}']) else 0
+                            wins = float(row[f'W_{i}']) if pd.notna(row[f'W_{i}']) else 0
+                            losses = float(row[f'L_{i}']) if pd.notna(row[f'L_{i}']) else 0
+                            games = wins + losses
+                            total_games = float(row['total_games']) if pd.notna(row['total_games']) else 20
+                            
+                            performance_quality = (
+                                (wr * 0.7) +
+                                (min(kda, 10) / 10 * 0.3)
+                            )
+                            
+                            games_factor = min(games / 5, 1.0)
+                            games_ratio = games / total_games
+                            
+                            if games >= 5:
+                                if performance_quality < 0.4:
+                                    performance_quality *= 0.8
+                                elif performance_quality > 0.7:
+                                    performance_quality *= 1.2
+                            
+                            champion_scores['recent_score'] = (
+                                performance_quality * (0.7 + (0.3 * games_factor))
+                            ) * (1 + games_ratio * 0.2)
+                            break  # Exit loop once found
+                    
+                    # 2. Weekly Performance
+                    for i in range(1, 4):
+                        if row.get(f'7d_champ_{i}') == champion:
+                            weekly_wins = float(row[f'7d_W_{i}']) if pd.notna(row[f'7d_W_{i}']) else 0
+                            weekly_losses = float(row[f'7d_L_{i}']) if pd.notna(row[f'7d_L_{i}']) else 0
+                            weekly_games = float(row[f'7d_total_{i}']) if pd.notna(row[f'7d_total_{i}']) else 0
+                            weekly_wr = float(row[f'7d_WR_{i}']) if pd.notna(row[f'7d_WR_{i}']) else 0
+                            profile_wr = float(row['win_rate']) if pd.notna(row['win_rate']) else 0.5
+                            
+                            if weekly_games > 0:
+                                wr_trend = (weekly_wr - profile_wr) / profile_wr if profile_wr > 0 else 0
+                                weekly_intensity = min(weekly_games / 10, 1.0)
+                                win_ratio = weekly_wins / weekly_games if weekly_games > 0 else 0
+                                
+                                weekly_performance = (
+                                    (weekly_wr * 0.4) +
+                                    (max(min(wr_trend, 1), -1) * 0.2) +
+                                    (weekly_intensity * 0.2) +
+                                    (win_ratio * 0.2)
+                                )
+                                
+                                if weekly_games >= 5:
+                                    if weekly_performance < 0.4:
+                                        weekly_performance *= 0.8
+                                    elif weekly_performance > 0.7:
+                                        weekly_performance *= 1.2
+                                
+                                champion_scores['weekly_score'] = weekly_performance * (
+                                    0.7 + (0.3 * min(weekly_games / 5, 1.0))
+                                )
+                                break  # Exit loop once found
+
+                    # 3. Meta Score
+                    if champion in weekly_meta['champion'].values:
+                        weekly_row = weekly_meta[weekly_meta['champion'] == champion].iloc[0]
+                        rank = weekly_row['rank']
+                        games = weekly_row['games']
+                        pick_rate = weekly_row['pick']
+                        ban_rate = weekly_row['ban']
+                        
+                        weight = (
+                            1 / rank * 0.5 +
+                            games / 100 * 0.3 +
+                            pick_rate * 0.1 -
+                            ban_rate * 0.1
+                        )
+                        
+                        champion_scores['meta_score'] = weight
+
+                    # 4. Season Performance
+                    for i in range(1, 8):
+                        if row.get(f'season_champ_{i}') == champion:
+                            wr = float(row[f'wr_ssn_{i}']) if pd.notna(row[f'wr_ssn_{i}']) else 0
+                            games = float(row[f'games_ssn_{i}']) if pd.notna(row[f'games_ssn_{i}']) else 0
+                            kda = float(row[f'kda_ssn_{i}']) if pd.notna(row[f'kda_ssn_{i}']) else 0
+                            
+                            champion_scores['season_score'] = (
+                                wr * 0.7 +
+                                (kda / 10) * 0.3 
+                            ) * (games / 100)
+                            break  # Exit loop once found
+                    
+                    # 5. Mastery Score
+                    for i in range(1, 17):
+                        if row.get(f'mastery_champ_{i}') == champion:
+                            mastery = float(row[f'm_lv_{i}']) if pd.notna(row[f'm_lv_{i}']) else 0            
+                            champion_scores['mastery_score'] = mastery / 7
+                            break  # Exit loop once found
+
+                    # Calculate base score for this champion and row
+                    base_score = (
+                        champion_scores['recent_score'] * weights['recent'] +
+                        champion_scores['weekly_score'] * weights['weekly'] +
+                        champion_scores['meta_score'] * weights['meta'] +
+                        champion_scores['season_score'] * weights['season'] +
+                        champion_scores['mastery_score'] * weights['mastery']
+                    )
+
+                    
+                    # Store the pre-penalty score for debugging
+                    base_score_before_penalty = base_score
+
+                    # Apply tier penalties
+                    if champion in tier_map:
+                        highest_tier = min(tier_map[champion])
+                        if highest_tier in tier_penalties:
+                            base_score *= tier_penalties[highest_tier]
+
+                    # Process team composition and counter penalties
+                    if consider_team_comp:
+                        # Check team champions
+                        for i in range(1, 5):
+                            team_col = f'team_champ{i}'
+                            if team_col in row and pd.notna(row[team_col]):
+                                if row[team_col] == champion:
+                                    base_score = 0
+                                    break
+                        
+                        # Only check opponents if base_score isn't already 0
+                        if base_score != 0:
+                            counter_penalty = 0
+                            counter_debug = []  # For debug information
+                            
+                            for i in range(1, 6):
+                                opp_col = f'opp_champ{i}'
+                                if opp_col in row and pd.notna(row[opp_col]):
+                                    opp_champ = row[opp_col]
+                                    if opp_champ == champion:
+                                        base_score = 0
+                                        break
+                                    if champion in counter_map and opp_champ in counter_map[champion]:
+                                        counter_penalty += 0.1
+                                        counter_debug.append(opp_champ)
+                            
+                            if counter_penalty > 0:
+                                base_score = base_score * (1 - counter_penalty)
+
+                    # Store the final score for this champion and row
+                    batch_scores[champion][batch_idx] = max(base_score, 0)
+
+                    # Collect debug data if this is the debug champion
+                    if debug == champion:
+                        counter_list = []
+                        for i in range(1, 6):
+                            opp_col = f'opp_champ{i}'
+                            if opp_col in row and pd.notna(row[opp_col]):
+                                if champion in counter_map and row[opp_col] in counter_map[champion]:
+                                    counter_list.append(row[opp_col])
+
+                        debug_row = {
+                            'champion': row['champion'],
+                            'recent_score': champion_scores['recent_score'],
+                            'weekly_score': champion_scores['weekly_score'],
+                            'meta_score': champion_scores['meta_score'],
+                            'base_score': base_score_before_penalty,
+                            'final_score': base_score,
+                            'counter_penalty': counter_penalty if consider_team_comp else 0,
+                            'final_score_actual': feature_dict[row['champion']][idx] if row['champion'] in feature_dict else base_score,
+                            'counter_list_debug': counter_list
+                        }
+                        debug_data.append(debug_row)
+
+            # Update feature_dict with batch results
+            for champion in batch_scores:
+                if champion not in feature_dict:
+                    feature_dict[champion] = np.zeros(total_rows)
+                feature_dict[champion][batch_start:batch_end] = batch_scores[champion]
+
+            # Save after each batch with timestamp
+            temp_df = pd.DataFrame({
+                **{col: feature_dict[col] for col in original_columns},  # Original columns first
+                **{champion: feature_dict[champion] for champion in all_champions}  # Then champion columns
+            })
+            
+            batch_save_file = os.path.join("util", "data", f"feature_eng_stats.csv")
+            temp_df.to_csv(batch_save_file, index=False)
+            print(f"Saved batch progress to {batch_save_file}")
+
+            if debug:
+                print(f"{debug} is countered by: {counter_map[debug]}")
+
+        # Process debug data if any
+        if debug:
+            debug_df = pd.DataFrame(debug_data)
+            print("\nDebug Data:")
+            print(debug_df)
+
+        # Create final DataFrame
+        champion_features = pd.DataFrame(feature_dict)
+
+        # Create the final DataFrame by combining original data with new features
+        features = pd.concat([
+            merged_player_stats[original_columns],  # Keep all original columns
+            champion_features[[col for col in champion_features.columns if col not in original_columns]]  # Only new champion columns
+        ], axis=1)
+
+        # Move the champion column to be the first column
+        if 'champion' in features.columns:
+            columns = ['champion'] + [col for col in features.columns if col != 'champion']
+            features = features[columns]
+        
+        # Save to CSV with current date in filename
+        output_file = os.path.join("util", "data", f"feature_eng_stats.csv")
+        os.makedirs(os.path.dirname(output_file), exist_ok=True)
+        features.to_csv(output_file, index=False)
+        
+        # Print confirmation message
+        print(f"Saved features to {output_file}")
+            
+        return features
+
+    except Exception as e:
+        print(f"\nError occurred: {str(e)}")
+        return None
+
+if __name__ == "__main__":
+    try:
+        input_file = os.path.join("util", "data", f"player_stats_merged_2025-01-05.csv")              
+        merged_stats = pd.read_csv(input_file)
+
+        features = create_champion_features(
+            merged_player_stats=merged_stats,
+            debug='Viktor',
+            consider_team_comp=True,
+            test_mode=True
+        )
+        
+        if features is not None:
+            print("\nProcessing completed successfully!")
+            print(f"Generated features for {len(features)} rows")
+        else:
+            print("\nProcessing failed or was interrupted.")
+            
+    except Exception as e:
+        print(f"\nFatal error: {str(e)}")

util/helper.py

@@ -0,0 +1,1205 @@
+import pandas as pd
+from datetime import datetime
+import os
+import numpy as np
+from urllib.parse import quote, unquote
+
+class ChampionConverter:
+    def __init__(self):
+        self.champions = [
+            "Aatrox", "Ahri", "Akali", "Akshan", "Alistar", "Ambessa", "Amumu", "Anivia", "Annie", "Aphelios", "Ashe", "Aurelion Sol",
+            "Aurora", "Azir", "Bard", "Bel'Veth", "Blitzcrank", "Brand", "Braum", "Briar", "Caitlyn", "Camille", "Cassiopeia", "Cho'Gath",
+            "Corki", "Darius", "Diana", "Dr. Mundo", "Draven", "Ekko", "Elise", "Evelynn", "Ezreal", "Fiddlesticks", "Fiora", "Fizz", "Galio",
+            "Gangplank", "Garen", "Gnar", "Gragas", "Graves", "Gwen", "Hecarim", "Heimerdinger", "Hwei", "Illaoi", "Irelia", "Ivern", "Janna",
+            "Jarvan IV", "Jax", "Jayce", "Jhin", "Jinx", "K'Sante", "Kai'Sa", "Kalista", "Karma", "Karthus", "Kassadin", "Katarina", "Kayle",
+            "Kayn", "Kennen", "Kha'Zix", "Kindred", "Kled", "Kog'Maw", "LeBlanc", "Lee Sin", "Leona", "Lillia", "Lissandra", "Lucian", "Lulu",
+            "Lux", "Malphite", "Malzahar", "Maokai", "Master Yi", "Milio", "Miss Fortune", "Mordekaiser", "Morgana", "Naafiri", "Nami", "Nasus",
+            "Nautilus", "Neeko", "Nidalee", "Nilah", "Nocturne", "Nunu & Willump", "Olaf", "Orianna", "Ornn", "Pantheon", "Poppy", "Pyke",
+            "Qiyana", "Quinn", "Rakan", "Rammus", "Rek'Sai", "Rell", "Renata Glasc", "Renekton", "Rengar", "Riven", "Rumble", "Ryze", "Samira",
+            "Sejuani", "Senna", "Seraphine", "Sett", "Shaco", "Shen", "Shyvana", "Singed", "Sion", "Sivir", "Skarner", "Smolder", "Sona",
+            "Soraka", "Swain", "Sylas", "Syndra", "Tahm Kench", "Taliyah", "Talon", "Taric", "Teemo", "Thresh", "Tristana", "Trundle",
+            "Tryndamere", "Twisted Fate", "Twitch", "Udyr", "Urgot", "Varus", "Vayne", "Veigar", "Vel'Koz", "Vex", "Vi", "Viego", "Viktor",
+            "Vladimir", "Volibear", "Warwick", "Wukong", "Xayah", "Xerath", "Xin Zhao", "Yasuo", "Yone", "Yorick", "Yuumi", "Zac", "Zed",
+            "Zeri", "Ziggs", "Zilean", "Zoe", "Zyra"
+        ]
+
+        self.champion_to_number = {champion: i for i, champion in enumerate(self.champions, start=1)}
+        self.number_to_champion = {i: champion for i, champion in enumerate(self.champions, start=1)}
+
+    def champion_to_num(self, champion_name):
+        return self.champion_to_number.get(champion_name, None)
+
+    def num_to_champion(self, number):
+        return self.number_to_champion.get(number, None)
+    
+def convert_date(date_str):
+    """Convert datetime string to Unix timestamp"""
+    try:
+        if pd.isna(date_str):
+            return None
+        return pd.to_datetime(date_str).timestamp()
+    except:
+        return None
+    
+
+def convert_to_minutes(time_str):
+    """Convert time string (e.g., '15m 10s') to minutes (float)"""
+    try:
+        minutes = seconds = 0
+        parts = time_str.lower().split()
+        for part in parts:
+            if 'm' in part:
+                minutes = float(part.replace('m', ''))
+            elif 's' in part:
+                seconds = float(part.replace('s', ''))
+        return round(minutes + seconds/60, 2)
+    except:
+        return 0.0
+    
+def convert_percentage_to_decimal(percentage_str):
+    """Convert percentage string (e.g., 'P/Kill 43%') to decimal (0.43)"""
+    try:
+        # Extract number from string and convert to decimal
+        num = float(''.join(filter(str.isdigit, percentage_str))) / 100
+        return round(num, 2)
+    except:
+        return 0.0
+    
+def convert_tier_to_number(tier_str):
+    """
+    Convert tier string to number:
+    Challenger -> 1
+    Grandmaster -> 2
+    Master -> 3
+    Others -> 4
+    """
+    tier_map = {
+        'challenger': 1,
+        'grandmaster': 2,
+        'master': 3
+    }
+    # Convert to lowercase and return mapped value or 4 for any other tier
+    return tier_map.get(tier_str.lower().strip(), 4)
+
+def convert_result_to_binary(result_str):
+    """
+    Convert match result to binary:
+    Victory -> 1
+    Defeat -> 0
+    """
+    return 1 if result_str.lower().strip() == 'victory' else 0
+
+def merge_stats(recent_stats, player_stats, current_time =None):
+    """
+    Merge recent match stats with player profile stats and save to CSV.
+    Only keeps rows where matches exist in both DataFrames.
+    
+    Args:
+        recent_stats (DataFrame/dict): Recent match statistics
+        player_stats (DataFrame/tuple): Player profile statistics
+        
+    Returns:
+        DataFrame: Combined statistics
+    """
+    try:
+        if current_time is None:
+            current_time = datetime.utcnow().strftime("%Y-%m-%d")
+
+        # Convert recent_stats to DataFrame if it's not already
+        if not isinstance(recent_stats, pd.DataFrame):
+            recent_df = pd.DataFrame(recent_stats)
+        else:
+            recent_df = recent_stats
+        
+        # Handle player_stats based on its type
+        if isinstance(player_stats, tuple):
+            # If it's a tuple (merged_df, dfs), use the merged_df
+            player_df = player_stats[0]
+        elif isinstance(player_stats, pd.DataFrame):
+            player_df = player_stats
+        else:
+            raise ValueError("Invalid player_stats format")
+
+        # Ensure player_id exists in both DataFrames
+        if 'player_id' not in recent_df.columns:
+            recent_df['player_id'] = player_df['player_id'].iloc[0]
+
+        # Print info before merge
+        print(f"\nBefore merge:")
+        print(f"Recent stats rows: {len(recent_df)}")
+        print(f"Player stats rows: {len(player_df)}")
+        print(f"Unique players in recent stats: {recent_df['player_id'].nunique()}")
+        print(f"Unique players in player stats: {player_df['player_id'].nunique()}")
+
+        # Merge DataFrames with inner join
+        merged_df = pd.merge(
+            recent_df,
+            player_df,
+            on='player_id',
+            how='inner',  # Changed from 'left' to 'inner'
+            suffixes=('', '_profile')
+        )
+
+        # Print info after merge
+        print(f"\nAfter merge:")
+        print(f"Merged stats rows: {len(merged_df)}")
+        print(f"Unique players in merged stats: {merged_df['player_id'].nunique()}")
+
+        # Reorder columns to ensure player_id and region are first
+        cols = merged_df.columns.tolist()
+        cols = ['player_id'] + [col for col in cols if col != 'player_id']
+        if 'region' in cols:
+            cols.remove('region')
+            cols.insert(1, 'region')
+        merged_df = merged_df[cols]
+
+        # Create directory if it doesn't exist
+        save_dir = "util/data"
+        os.makedirs(save_dir, exist_ok=True)
+
+        # Save to CSV
+        filename = f"player_stats_merged_{current_time}.csv"
+        filepath = os.path.join(save_dir, filename)
+        merged_df.to_csv(filepath, index=False)
+        print(f"\nSuccessfully saved merged stats to {filepath}")
+
+        return merged_df
+
+    except Exception as e:
+        print(f"Error in merge_stats: {e}")
+        return None
+    
+
+def filter_leaderboard(df, tiers=None):
+    """
+    Filter leaderboard DataFrame to keep only specific tiers.
+    
+    Args:
+        df (pandas.DataFrame): Input leaderboard DataFrame
+        tiers (list): List of tiers to keep. Defaults to ["CHALLENGER", "GRANDMASTER"]
+        timestamp (str): Current timestamp in UTC
+        scraper_user (str): Current user's login
+    
+    Returns:
+        pandas.DataFrame: Filtered leaderboard data
+    """
+    try:
+        # Set default tiers if none provided
+        if tiers is None:
+            tiers = ["CHALLENGER", "GRANDMASTER"]
+        
+        # Convert tiers to uppercase for consistency
+        tiers = [tier.upper() for tier in tiers]
+        
+        # Validate input DataFrame
+        required_cols = ["tier", "summoner", "region"]
+        if not all(col in df.columns for col in required_cols):
+            raise ValueError(f"DataFrame must contain columns: {required_cols}")
+        
+        # Create copy to avoid modifying original DataFrame
+        filtered_df = df.copy()
+        
+        # Convert tier column to uppercase for consistent filtering
+        filtered_df['tier'] = filtered_df['tier'].str.upper()
+        
+        # Filter by specified tiers
+        filtered_df = filtered_df[filtered_df['tier'].isin(tiers)]
+        
+        
+        # Sort by region and tier
+        filtered_df = filtered_df.sort_values(['region', 'tier', 'rank'])
+        
+        # Reset index
+        filtered_df = filtered_df.reset_index(drop=True)
+        
+        # Save to CSV
+        output_file = os.path.join("util", "data", "lb_filtered.csv")
+        os.makedirs(os.path.dirname(output_file), exist_ok=True)
+        filtered_df.to_csv(output_file, index=False)
+        
+        print(f"\nFiltered leaderboard to {len(tiers)} tiers: {', '.join(tiers)}")
+        print(f"Remaining entries: {len(filtered_df)}")
+        print(f"Saved filtered leaderboard to {output_file}")
+        
+        # Print summary statistics
+        print("\nSummary by region and tier:")
+        summary = filtered_df.groupby(['region', 'tier']).size().unstack(fill_value=0)
+        print(summary)
+        
+        return filtered_df
+        
+    except Exception as e:
+        print(f"Error filtering leaderboard: {e}")
+        return None
+
+def format_summoner_name(summoner):
+    """
+    Format summoner name for URL usage
+    
+    Parameters:
+    summoner: str - Original summoner name
+    
+    Returns:
+    str - Formatted summoner name
+    """
+    if not summoner:
+        raise ValueError("Summoner name cannot be empty")
+        
+    # Remove leading/trailing whitespace
+    summoner = summoner.strip()
+    
+    # Replace spaces and special characters
+    formatted_summoner = summoner.replace(" ", "-").replace("#", "-")
+    
+    # Handle other special characters through URL encoding
+    formatted_summoner = quote(formatted_summoner)
+    
+    return formatted_summoner
+
+def convert_to_displayname(name):
+    """
+    Convert a summoner name to display format
+    Examples:
+    marthinsurya-NA -> marthinsurya #NA
+    toplane%20kid-EUW77 -> toplane kid #EUW77
+    Walid-Georgey-EUW -> Walid Georgey #EUW
+    Current%20User-KR -> Current User #KR
+    """
+    try:
+        if not name:
+            return ""
+            
+        # First decode URL encoding
+        decoded = unquote(name)
+        
+        # Remove any trailing hyphens
+        decoded = decoded.rstrip('-')
+        
+        # Split by last hyphen to separate name and region
+        if '-' in decoded:
+            parts = decoded.rsplit('-', 1)
+            base_name = parts[0]  # Everything before last hyphen
+            region = parts[1]
+            
+            # Replace remaining hyphens in base_name with spaces
+            base_name = base_name.replace('-', ' ')
+            
+            # Clean up any double spaces
+            base_name = ' '.join(filter(None, base_name.split()))
+            
+            return f"{base_name} #{region}"
+        
+        return decoded.replace('-', ' ')
+    except Exception as e:
+        print(f"Error converting name '{name}': {e}")
+        return name
+
+
+
+def get_player_list(leaderboard=None):
+    """
+    Convert leaderboard data into proper player list format for API calls.
+    
+    Args:
+        leaderboard (DataFrame): Input leaderboard DataFrame containing summoner and region
+    
+    Returns:
+        DataFrame: Formatted player list with region and username columns
+    """
+    try:
+        
+        if leaderboard is None:
+            leaderboard_file = os.path.join("util", "data", "lb_filtered.csv")
+            leaderboard = pd.read_csv(leaderboard_file)
+            
+        # Rename summoner column to username
+        leaderboard = leaderboard.rename(columns={'summoner': 'username'})
+        
+        # Select only region and username columns in correct order
+        player_list = leaderboard[['region', 'username']]
+        
+        print(f"Successfully processed {len(player_list)} players")
+        return player_list
+        
+    except Exception as e:
+        print(f"Error processing leaderboard: {e}")
+        return None
+
+def process_kda_perfect(df):
+    """
+    Process KDA values in the DataFrame, replacing 'Perfect' with appropriate values.
+    """
+    try:
+        # Create a copy to avoid modifying the original dataframe
+        df = df.copy()
+        
+        # Function to safely convert to numeric
+        def safe_convert(x):
+            if isinstance(x, (int, float)):
+                return x
+            if isinstance(x, str) and x.lower() == 'perfect':
+                return 6
+            try:
+                return float(x)
+            except:
+                return None
+
+        # 1. Process KDA_1, KDA_2, KDA_3
+        for col in ['KDA_1', 'KDA_2', 'KDA_3']:
+            if col in df.columns:
+                df[col] = df[col].apply(safe_convert)
+
+        # 2. Process kda_ssn_1 to kda_ssn_7
+        for i in range(1, 8):
+            col = f'kda_ssn_{i}'
+            if col in df.columns:
+                perfect_mask = df[col].astype(str).str.contains('perfect', case=False)
+                if perfect_mask.any():
+                    kills_col, assists_col = f'k_ssn_{i}', f'a_ssn_{i}'
+                    if kills_col in df.columns and assists_col in df.columns:
+                        df.loc[perfect_mask, col] = df.loc[perfect_mask].apply(
+                            lambda row: pd.to_numeric(row[kills_col], errors='coerce') + 
+                                      pd.to_numeric(row[assists_col], errors='coerce'), 
+                            axis=1
+                        )
+                    else:
+                        df.loc[perfect_mask, col] = 6
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        # 3. Process kda_ratio_profile
+        if 'kda_ratio_profile' in df.columns:
+            perfect_mask = df['kda_ratio_profile'].astype(str).str.contains('perfect', case=False)
+            if perfect_mask.any():
+                df.loc[perfect_mask, 'kda_ratio_profile'] = df.loc[perfect_mask].apply(
+                    lambda row: pd.to_numeric(row['avg_kills'], errors='coerce') + 
+                              pd.to_numeric(row['avg_assists'], errors='coerce'),
+                    axis=1
+                )
+            df['kda_ratio_profile'] = pd.to_numeric(df['kda_ratio_profile'], errors='coerce')
+
+        # 4. Process remaining kda_ratio columns
+        other_cols = [col for col in df.columns if 'kda_ratio' in col.lower() 
+                     and col != 'kda_ratio_profile' 
+                     and col not in [f'kda_ssn_{i}' for i in range(1, 8)]]
+        
+        for col in other_cols:
+            perfect_mask = df[col].astype(str).str.contains('perfect', case=False)
+            if perfect_mask.any():
+                prefix = col.split('kda_ratio')[0]
+                kills_col, assists_col = f"{prefix}kills", f"{prefix}assists"
+                if kills_col in df.columns and assists_col in df.columns:
+                    df.loc[perfect_mask, col] = df.loc[perfect_mask].apply(
+                        lambda row: pd.to_numeric(row[kills_col], errors='coerce') + 
+                                  pd.to_numeric(row[assists_col], errors='coerce'),
+                        axis=1
+                    )
+                else:
+                    df.loc[perfect_mask, col] = 6
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        return df
+
+    except Exception as e:
+        print(f"Error in process_kda_perfect: {str(e)}")
+        return df
+
+
+def check_mixed_types(df):
+    """
+    Check and print dataframe column types, inconsistencies, and basic statistics
+    """
+    # Get type information
+    dtype_info = pd.DataFrame({
+        'dtype': df.dtypes,
+        'non_null': df.count(),
+        'null_count': df.isnull().sum(),
+        'unique_values': [df[col].nunique() for col in df.columns]
+    })
+    
+    # Add sample of unique values for each column
+    dtype_info['sample_values'] = [df[col].dropna().sample(min(3, len(df[col].dropna()))).tolist() 
+                                 if len(df[col].dropna()) > 0 else [] 
+                                 for col in df.columns]
+    
+    # Check for mixed types in object columns
+    mixed_type_cols = []
+    for col in df.select_dtypes(include=['object']):
+        types = df[col].apply(type).unique()
+        if len(types) > 1:
+            mixed_type_cols.append({
+                'column': col,
+                'types': [t.__name__ for t in types],
+                'samples': df[col].dropna().sample(min(3, len(df[col].dropna()))).tolist()
+            })
+    
+    print("=== DataFrame Overview ===")
+    print(f"Shape: {df.shape}")
+    print("\n=== Data Types Summary ===")
+    print(df.dtypes.value_counts())
+    
+    if mixed_type_cols:
+        print("\n=== Mixed Type Columns ===")
+        for col_info in mixed_type_cols:
+            print(f"\nColumn: {col_info['column']}")
+            print(f"Types found: {col_info['types']}")
+            print(f"Sample values: {col_info['samples']}")
+    
+    return dtype_info
+
+def check_nan_float(df, column_name):
+    float_mask = df[column_name].apply(lambda x: isinstance(x, float))
+    is_nan_mask = df[column_name].isna()
+
+    # Check if all float values are NaN
+    all_floats_are_nan = (float_mask == is_nan_mask).all()
+    print(f"Are all float values NaN? {all_floats_are_nan}")
+
+    # Double check by comparing counts
+    print(f"Number of float values: {float_mask.sum()}")
+    print(f"Number of NaN values: {is_nan_mask.sum()}")
+
+def convert_team_colors(df):
+    """
+    Convert 'team' column values from 'blue'/'red' to 1/2
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame with 'team' column
+    
+    Returns:
+    pandas.DataFrame: DataFrame with converted team values
+    """
+    df = df.copy()
+    
+    if 'team' not in df.columns:
+        raise ValueError("Column 'team' not found in DataFrame")
+    
+    # Create mapping dictionary
+    team_mapping = {
+        'blue': 1,
+        'red': 2
+    }
+    
+    # Convert team colors to numbers
+    df['team'] = df['team'].map(team_mapping, na_action='ignore')
+    
+    return df
+
+def convert_region(df):
+    """
+    Convert 'region' column values to numeric:
+    kr -> 1
+    euw -> 2
+    vn -> 3
+    na -> 4
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame with 'region' column
+    
+    Returns:
+    pandas.DataFrame: DataFrame with converted region values
+    """
+    df = df.copy()
+    
+    if 'region' not in df.columns:
+        raise ValueError("Column 'region' not found in DataFrame")
+    
+    # Create mapping dictionary
+    region_mapping = {
+        'kr': 1,
+        'euw': 2,
+        'vn': 3,
+        'na': 4
+    }
+    
+    # Convert regions to numbers, keeping NA as NA
+    df['region'] = df['region'].map(region_mapping, na_action='ignore')
+    
+    return df
+
+def convert_champion_columns(df):
+    """
+    Convert all champion-related columns to numbers using ChampionConverter
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame
+    
+    Returns:
+    pandas.DataFrame: DataFrame with converted champion values
+    """
+    df = df.copy()
+    
+    # Initialize champion converter
+    converter = ChampionConverter()
+    
+    # Get all champion-related columns
+    champion_columns = [col for col in df.columns if 'champ' in col.lower()]
+    
+    for col in champion_columns:       
+        # Convert champion names to numbers
+        df[col] = df[col].map(converter.champion_to_num, na_action='ignore')
+    
+    return df
+
+def convert_date_column(df):
+    """
+    Convert date column from string format to Unix timestamp
+    Handles missing values (NaT, None, NaN)
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame with 'date' column
+    
+    Returns:
+    pandas.DataFrame: DataFrame with converted date values
+    """
+    df = df.copy()
+    
+    if 'date' not in df.columns:
+        raise ValueError("Column 'date' not found in DataFrame")
+    
+    # Convert dates to timestamps
+    df['date'] = df['date'].apply(convert_date)
+    
+    return df
+
+def convert_role_columns(df):
+    """
+    Convert role columns to numbers:
+    TOP -> 1, MID -> 2, ADC -> 3, JUNGLE -> 4, SUPPORT -> 5
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame
+    
+    Returns:
+    pandas.DataFrame: DataFrame with converted role values
+    """
+    df = df.copy()
+    
+    # Define role mapping
+    role_mapping = {
+        'TOP': 1,
+        'MID': 2,
+        'ADC': 3,
+        'JUNGLE': 4,
+        'SUPPORT': 5
+    }
+    
+    # Role columns to convert
+    role_columns = ['most_role_1', 'most_role_2']
+    
+    
+    for col in role_columns:
+        if col in df.columns:       
+            # Convert roles to numbers
+            df[col] = df[col].map(role_mapping, na_action='ignore')
+            
+        else:
+            print(f"Warning: Column {col} not found in DataFrame")
+    
+    return df
+
+def convert_id_columns(df):
+    """
+    Drop ID-related columns (player_id, teammates1-4, oppmates1-5)
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame
+    
+    Returns:
+    pandas.DataFrame: DataFrame with ID columns dropped
+    """
+    df = df.copy()
+    
+    # Specific ID columns to drop
+    id_columns = (
+        ['player_id', 'region_profile'] + 
+        [f'teammates{i}' for i in range(1, 5)] +  # teammates1 to teammates4
+        [f'oppmates{i}' for i in range(1, 6)]     # oppmates1 to oppmates5
+    )
+    
+    
+    # Verify columns exist and drop them
+    existing_columns = [col for col in id_columns if col in df.columns]
+    if len(existing_columns) != len(id_columns):
+        missing = set(id_columns) - set(existing_columns)
+        print(f"Note: Some columns were not found in DataFrame: {missing}")
+    
+    # Drop the columns
+    df = df.drop(columns=existing_columns)
+    
+    return df
+
+def remove_match_stats(df):
+    """
+    Remove match-specific statistics to prevent future data leakage.
+    
+    Parameters:
+    df (pandas.DataFrame): Input DataFrame
+    
+    Returns:
+    pandas.DataFrame: DataFrame with match-specific columns removed
+    """
+    # List of columns that contain match-specific information
+    match_stat_columns = [
+        'level',            # Champion level
+        'result',           # Match outcome (target variable)
+        'match_length_mins',# Match duration
+        'kill',            # Kills in the match
+        'death',           # Deaths in the match
+        'assist',          # Assists in the match
+        'kda_ratio',       # KDA ratio for the match
+        'kill_participation',# Kill participation in the match
+        'laning',          # Laning phase performance
+        'cs',              # Creep score in the match
+        'cs_per_min'       # CS per minute in the match
+    ]
+    
+    # Create a copy of the dataframe
+    df_clean = df.copy()
+    
+    # Remove match-specific columns
+    columns_to_drop = [col for col in match_stat_columns if col in df_clean.columns]
+    df_clean = df_clean.drop(columns=columns_to_drop)
+    
+    return df_clean
+
+def convert_df(df):
+    """
+    Master function to handle all conversions for training DataFrame
+    
+    Includes:
+    - Team color conversion (blue/red to 1/2)
+    - Region conversion (kr/euw/vn/na to 1/2/3/4)
+    - Champion conversion (champion names to numbers)
+    - Date conversion (string to Unix timestamp)
+    - Role conversion (TOP/MID/ADC/JUNGLE/SUPPORT to 1/2/3/4/5)
+    - Drop ID columns (player_id, teammates1-4, oppmates1-5, region_profile)
+    
+    Parameters:
+    df (pandas.DataFrame): Input training DataFrame
+    
+    Returns:
+    pandas.DataFrame: Processed DataFrame with all conversions
+    """
+    df = df.copy()
+    
+    # Drop rows where champion is NA
+    initial_rows = len(df)
+    df = df.dropna(subset=['champion'])
+    rows_dropped = initial_rows - len(df)
+    print(f"Dropped {rows_dropped} rows with NA champion values")
+
+    # Sequential conversions
+    conversions = [
+        convert_team_colors,      # Convert blue/red to 1/2
+        convert_region,          # Convert kr/euw/vn/na to 1/2/3/4
+        convert_champion_columns, # Convert champion names to numbers
+        convert_date_column,     # Convert dates to timestamps
+        convert_role_columns,    # Convert roles to 1-5
+        convert_id_columns,       # Drop ID-related columns
+        remove_match_stats        # Remove match-specific columns
+    ]
+    
+    ## Apply each conversion function in sequence
+    for convert_func in conversions:
+        try:
+            print(f"Applying {convert_func.__name__}...")
+            df = convert_func(df)
+        except Exception as e:
+            print(f"Error in {convert_func.__name__}: {str(e)}")
+            raise
+    
+    return df
+
+
+def get_top_champion_scores(df, n=5):
+    """
+    Get top n champion scores from a DataFrame
+    
+    Parameters:
+    df: pandas DataFrame containing champion scores
+    n: number of top champions to return (default 5)
+    
+    Returns:
+    pandas DataFrame with original data plus top n champion scores and their names
+    """
+    try:
+        converter = ChampionConverter()
+        df = df.copy()
+        
+        # Get all champion columns (from Aatrox to Zyra)
+        champion_start = df.columns.get_loc('Aatrox')
+        champion_end = df.columns.get_loc('Zyra') + 1
+        champion_cols = df.columns[champion_start:champion_end]
+        
+        # Convert scores to numeric, replacing non-numeric values with 0
+        champion_scores = df[champion_cols].apply(pd.to_numeric, errors='coerce').fillna(0)
+        
+        # Get indices of top n values for each row
+        top_n_indices = champion_scores.apply(lambda x: pd.Series(x.nlargest(n).index), axis=1)
+        top_n_values = champion_scores.apply(lambda x: pd.Series(x.nlargest(n).values), axis=1)
+        
+        # Create new columns for champion names and scores
+        for i in range(n):
+            # Champion scores
+            df[f'{i+1}_champ_score'] = top_n_values.iloc[:, i].astype(float)
+            
+            # Champion names (converted to numbers)
+            champ_names = top_n_indices.iloc[:, i]
+            df[f'{i+1}_champ_name'] = champ_names.map(
+                lambda x: int(converter.champion_to_num(x)) if pd.notnull(x) else -1
+            )
+        
+        return df
+    
+    except Exception as e:
+        print(f"Error in get_top_champion_scores: {str(e)}")
+        # Return original DataFrame with default values in case of error
+        for i in range(1, n + 1):
+            df[f'{i}_champ_score'] = 0.0
+            df[f'{i}_champ_name'] = -1
+        return df
+    
+def check_datatypes(df):
+    datatype= pd.DataFrame({
+        'dtype': df.dtypes,
+        'unique_values': df.nunique()
+    })
+
+    print(datatype)
+    return datatype
+
+def calculate_champ_variety_score(df):
+    df = df.copy()  # Create a copy to avoid warnings
+    
+    # Create a list of champion columns we want to check
+    champ_columns = [
+        'most_champ_1', 'most_champ_2', 'most_champ_3',
+        '7d_champ_1', '7d_champ_2', '7d_champ_3'
+    ]
+    
+    # Filter to only include columns that exist in the DataFrame
+    existing_columns = [col for col in champ_columns if col in df.columns]
+    
+    # Function to count unique non-NaN values
+    def count_unique_champions(row):
+        # Get all values that are not NaN
+        valid_champions = row[existing_columns].dropna()
+        # Count unique values
+        return len(set(valid_champions))
+    
+    # Calculate the score for each row
+    df['champ_variety_score'] = df.apply(count_unique_champions, axis=1)
+    
+    return df
+
+def calculate_playstyle(df):
+    df = df.copy()
+    
+    # Playstyle categorization (0-5)
+    conditions = [
+        # 0: Assassin/Carry (high kills, high KDA, high kill participation)
+        (df['avg_kills'] > df['avg_assists']) & 
+        (df['kda_ratio_profile'] > 3) & 
+        (df['kill_participation_profile'] > 0.6),
+        
+        # 1: Support/Utility (high assists, good KDA, high kill participation)
+        (df['avg_assists'] > df['avg_kills']) & 
+        (df['kda_ratio_profile'] > 2.5) & 
+        (df['kill_participation_profile'] > 0.55),
+        
+        # 2: Tank/Initiator (moderate deaths, high assists, high kill participation)
+        (df['avg_deaths'] > 3) & 
+        (df['avg_assists'] > df['avg_kills']) & 
+        (df['kill_participation_profile'] > 0.5),
+        
+        # 3: Split-pusher (lower kill participation, good KDA)
+        (df['kill_participation_profile'] < 0.5) & 
+        (df['kda_ratio_profile'] > 2),
+        
+        # 4: Aggressive/Fighter (high kills and deaths, high kill participation)
+        (df['avg_kills'] > 3) & 
+        (df['avg_deaths'] > 4) & 
+        (df['kill_participation_profile'] > 0.55)
+    ]
+    
+    values = [0, 1, 2, 3, 4]  # Numeric values for each playstyle
+    df['playstyle'] = np.select(conditions, values, default=5)
+    
+    return df
+
+def get_most_role_3(df):
+    df = df.copy()
+    
+    # Role mapping
+    role_mapping = {
+        'TOP': 1,
+        'MID': 2,
+        'ADC': 3,
+        'JUNGLE': 4,
+        'SUPPORT': 5
+    }
+    
+    def get_third_role_info(row):
+        # Create dictionary of role values excluding most_role_1 and most_role_2
+        role_values = {
+            'TOP': row['TOP'],
+            'JUNGLE': row['JUNGLE'],
+            'MID': row['MID'],
+            'ADC': row['ADC'],
+            'SUPPORT': row['SUPPORT']
+        }
+        
+        # Remove most_role_1 and most_role_2 from consideration
+        role_values.pop(row['most_role_1'], None)
+        role_values.pop(row['most_role_2'], None)
+        
+        # Find highest remaining role and its value
+        if role_values:
+            third_role, third_value = max(role_values.items(), key=lambda x: x[1])
+            return role_mapping[third_role], third_value
+        return 0, 0.0  # Default values if no third role found
+
+    # Add both most_role_3 and most_role_3_value
+    df[['most_role_3', 'most_role_3_value']] = df.apply(get_third_role_info, axis=1, result_type='expand')
+    
+    return df
+
+def calculate_role_specialization(df):
+    df = df.copy()
+    
+    # Define conditions for role specialization
+    conditions = [
+        # 0: Pure Specialist (one dominant role)
+        (df['most_role_1_value'] > 0.6),
+        
+        # 1: Strong Dual Role (two significant roles)
+        (df['most_role_1_value'] <= 0.6) & 
+        (df['most_role_2_value'] >= 0.3),
+        
+        # 2: Primary Role with Backups (moderate first role, has backups)
+        (df['most_role_1_value'] <= 0.6) & 
+        (df['most_role_2_value'] < 0.3) &
+        (df['most_role_1_value'] > 0.3) &
+        (df['most_role_3_value'] > 0.1),  # Has a viable third role
+        
+        # 3: Role Swapper (moderate first role, low others)
+        (df['most_role_1_value'] <= 0.6) & 
+        (df['most_role_2_value'] < 0.3) &
+        (df['most_role_1_value'] > 0.3) &
+        (df['most_role_3_value'] <= 0.1),  # No viable third role
+        
+        # 4: True Flex (plays multiple roles evenly)
+        (df['most_role_1_value'] <= 0.3) & 
+        (df['most_role_1_value'] > 0) &
+        (df['most_role_3_value'] >= 0.15)  # Significant third role
+    ]
+    
+    # 5 will be No Preference/Undefined (very low values or missing data)
+    values = [0, 1, 2, 3, 4]  # Numeric values for each category
+    df['role_specialization'] = np.select(conditions, values, default=5)
+    
+    return df
+
+def calculate_champion_loyalty(df):
+    df = df.copy()
+    
+    def get_loyalty_scores(row):
+        try:
+            # Get champions lists, handle potential NaN/None values (only top 2)
+            recent_champs = [
+                row['most_champ_1'] if pd.notna(row['most_champ_1']) else None,
+                row['most_champ_2'] if pd.notna(row['most_champ_2']) else None
+            ]
+            
+            # Include all 7 season champions
+            season_champs = []
+            season_games = []
+            for i in range(1, 8):
+                champ = row[f'season_champ_{i}'] if pd.notna(row[f'season_champ_{i}']) else None
+                games = row[f'games_ssn_{i}'] if pd.notna(row[f'games_ssn_{i}']) else 0
+                if champ is not None:
+                    season_champs.append(champ)
+                    season_games.append(games)
+            
+            # Add individual champion loyalty flags (only top 2)
+            champ_loyalty_flags = {
+                'recent_champ_1_loyal': 1 if (pd.notna(row['most_champ_1']) and 
+                                            row['most_champ_1'] in season_champs) else 0,
+                'recent_champ_2_loyal': 1 if (pd.notna(row['most_champ_2']) and 
+                                            row['most_champ_2'] in season_champs) else 0
+            }
+            
+            # Remove None values from recent champions
+            recent_champs = [c for c in recent_champs if c is not None]
+            
+            # If no valid champions, return defaults
+            if not recent_champs or not season_champs:
+                return {
+                    'loyalty_score': 0,
+                    'confidence_score': 0,
+                    **champ_loyalty_flags
+                }
+            
+            # Calculate games played for recent champions (only top 2)
+            recent_games = [
+                (row['W_1'] + row['L_1']) if pd.notna(row['most_champ_1']) else 0,
+                (row['W_2'] + row['L_2']) if pd.notna(row['most_champ_2']) else 0
+            ]
+            
+            total_recent_games = sum(recent_games)
+            total_season_games = sum(season_games)
+            
+            if total_recent_games == 0:
+                return {
+                    'loyalty_score': 0,
+                    'confidence_score': 0,
+                    **champ_loyalty_flags
+                }
+                
+            # Calculate overlap score with enhanced weights
+            loyalty_score = 0
+            for idx, champ in enumerate(recent_champs):
+                if champ in season_champs:
+                    season_idx = season_champs.index(champ)
+                    
+                    recent_weight = recent_games[idx] / total_recent_games
+                    season_weight = season_games[season_idx] / total_season_games
+                    position_weight = 1.7 if idx == 0 else 1.3  # Adjusted weights for 2 champions
+                    seasonal_position_weight = 1.3 if season_idx < 3 else 1.0
+                    
+                    combined_weight = (
+                        recent_weight * 0.6 +
+                        season_weight * 0.4
+                    ) * position_weight * seasonal_position_weight
+                    
+                    loyalty_score += combined_weight
+            
+            # Calculate confidence score (adjusted for 2 champions)
+            confidence_score = 0
+            confidence_score += 0.5 if pd.notna(row['most_champ_1']) else 0  # Increased weight for main
+            confidence_score += 0.2 if pd.notna(row['most_champ_2']) else 0  # Increased weight for second
+            confidence_score += sum(0.1 for i in range(1, 4) if pd.notna(row[f'season_champ_{i}']))
+            confidence_score += sum(0.05 for i in range(4, 8) if pd.notna(row[f'season_champ_{i}']))
+            
+            recent_games = sum((row[f'W_{i}'] + row[f'L_{i}']) if pd.notna(row[f'most_champ_{i}']) else 0 
+                             for i in range(1, 3))  # Only top 2
+            confidence_score += min(0.1, recent_games / 100)
+            
+            return {
+                'loyalty_score': round(min(loyalty_score, 1.0), 3),
+                'confidence_score': round(min(confidence_score, 1.0), 3),
+                **champ_loyalty_flags
+            }
+            
+        except Exception as e:
+            print(f"Error calculating loyalty scores: {e}")
+            return {
+                'loyalty_score': 0,
+                'confidence_score': 0,
+                'recent_champ_1_loyal': 0,
+                'recent_champ_2_loyal': 0
+            }
+    
+    # Apply calculations and expand results to columns
+    results = df.apply(get_loyalty_scores, axis=1)
+    
+    # Convert results to new columns
+    df['champion_loyalty_score'] = results.apply(lambda x: x['loyalty_score'])
+    df['loyalty_confidence_score'] = results.apply(lambda x: x['confidence_score'])
+    df['recent_champ_1_loyal'] = results.apply(lambda x: x['recent_champ_1_loyal'])
+    df['recent_champ_2_loyal'] = results.apply(lambda x: x['recent_champ_2_loyal'])
+    
+    return df
+
+def optimize_feature_dtypes(df):
+    """
+    Optimize data types for feature columns using unsigned integers for non-negative values
+    """
+    df = df.copy()
+    
+    # Very small range integers (< 10 unique values) to uint8 (0 to 255)
+    category_cols = {
+        'region': 4,              # 4 unique values
+        'team': 2,               # 2 unique values
+        'champ_variety_score': 6, # 6 unique values
+        'playstyle': 6,          # 6 unique values
+        'most_role_1': 5,        # 5 unique values
+        'most_role_2': 5,        # 5 unique values
+        'most_role_3': 5,        # 5 unique values
+        'role_specialization': 5,  # 5 unique values
+        'recent_champ_1_loyal':2,  # 2 unique values
+        'recent_champ_2_loyal':2   # 2 unique values
+    }
+    
+    for col, n_unique in category_cols.items():
+        if col in df.columns:
+            if df[col].isna().any():
+                # For columns with NaN, ensure proper handling
+                df[col] = df[col].astype('category')
+                # Fill NaN with a new category if needed
+                df[col] = df[col].cat.add_categories(['Unknown']).fillna('Unknown')
+            else:
+                df[col] = df[col].astype('category')  # Regular unsigned integer
+    
+    # Medium range integers (< 200 unique values) to UInt8 (0 to 255)
+    champion_cols = [
+        'champion',         # 168 unique
+        'team_champ1',     # 149 unique
+        'team_champ2',     # 154 unique
+        'team_champ3',     # 143 unique
+        'team_champ4',     # 140 unique
+        'opp_champ1',      # 144 unique
+        'opp_champ2',      # 82 unique
+        'opp_champ3',      # 145 unique
+        'opp_champ4',      # 119 unique
+        'opp_champ5',      # 110 unique
+        'most_champ_1',    # 138 unique
+        'most_champ_2',    # 134 unique
+        'season_champ1',   # 139 unique
+        'season_champ2',   # 129 unique
+        'season_champ3',   # 132 unique
+        '1_champ_name',    # 114 unique
+        '2_champ_name',    # 114 unique
+        '3_champ_name',    # 112 unique
+        '4_champ_name',    # 111 unique
+        '5_champ_name'     # 113 unique
+    ]
+    
+    for col in champion_cols:
+        if col in df.columns:
+            df[col] = df[col].astype('UInt8')  # All champion IDs can fit in UInt8
+    
+    # Float32 columns (performance metrics and ratios)
+    float32_cols = [
+        'most_role_1_value',         # 15 unique
+        'most_role_2_value',         # 11 unique
+        'most_role_3_value',         # 15 unique
+        'avg_kills',                 # 92 unique
+        'avg_deaths',                # 58 unique
+        'avg_assists',               # 132 unique
+        'kda_ratio_profile',         # 286 unique
+        'kill_participation_profile', # 37 unique
+        'WR_1',                      # 64 unique
+        'WR_2',                      # 23 unique
+        'WR_3',                      # 10 unique
+        'champion_loyalty_score',     # 156 unique
+        'loyalty_confidence_score'    # 5 unique
+    ]
+    
+    for col in float32_cols:
+        if col in df.columns:
+            df[col] = df[col].astype('float32')
+    
+    return df
+
+def remove_unwanted_columns(df):
+    """
+    Removes specified columns from the DataFrame
+    
+    Args:
+        df (pd.DataFrame): Input DataFrame
+    
+    Returns:
+        pd.DataFrame: DataFrame with specified columns removed
+    """
+    df = df.copy()
+    
+    # Define columns to remove
+    columns_to_remove = (
+        # Time and basic stats
+        ['date'] +
+        ['total_games', 'wins', 'losses', 'win_rate'] +
+        ['WR_1', 'WR_2', 'WR_3'] +
+        ['most_champ_3'] +
+        ['W_1', 'L_1', 'KDA_1', 'W_2', 'L_2', 'KDA_2', 'W_3', 'L_3', 'KDA_3'] +
+        
+        # Roles
+        ['TOP', 'JUNGLE', 'MID', 'ADC', 'SUPPORT'] +
+        
+        # Season and weekly stats
+        ['cs_ssn_1', 'cpm_ssn_1', 'kda_ssn_1', 'k_ssn_1', 'd_ssn_1', 'a_ssn_1', 'wr_ssn_1', 'games_ssn_1',
+         'cs_ssn_2', 'cpm_ssn_2', 'kda_ssn_2', 'k_ssn_2', 'd_ssn_2', 'a_ssn_2', 'wr_ssn_2', 'games_ssn_2',
+         'cs_ssn_3', 'cpm_ssn_3', 'kda_ssn_3', 'k_ssn_3', 'd_ssn_3', 'a_ssn_3', 'wr_ssn_3', 'games_ssn_3',
+         'season_champ_4', 'cs_ssn_4', 'cpm_ssn_4', 'kda_ssn_4', 'k_ssn_4', 'd_ssn_4', 'a_ssn_4', 'wr_ssn_4', 'games_ssn_4',
+         'season_champ_5', 'cs_ssn_5', 'cpm_ssn_5', 'kda_ssn_5', 'k_ssn_5', 'd_ssn_5', 'a_ssn_5', 'wr_ssn_5', 'games_ssn_5',
+         'season_champ_6', 'cs_ssn_6', 'cpm_ssn_6', 'kda_ssn_6', 'k_ssn_6', 'd_ssn_6', 'a_ssn_6', 'wr_ssn_6', 'games_ssn_6',
+         'season_champ_7', 'cs_ssn_7', 'cpm_ssn_7', 'kda_ssn_7', 'k_ssn_7', 'd_ssn_7', 'a_ssn_7', 'wr_ssn_7', 'games_ssn_7'] +
+        
+        # Weekly stats
+        ['7d_champ_1', '7d_total_1', '7d_WR_1', '7d_champ_2', '7d_total_2', '7d_WR_2', 
+         '7d_champ_3', '7d_total_3', '7d_WR_3'] +
+        ['7d_W_1', '7d_L_1', '7d_W_2', '7d_L_2', '7d_W_3', '7d_L_3'] +
+        
+        # Mastery stats
+        ['mastery_champ_1', 'm_lv_1', 'mastery_champ_2', 'm_lv_2', 'mastery_champ_3', 'm_lv_3',
+         'mastery_champ_4', 'm_lv_4', 'mastery_champ_5', 'm_lv_5', 'mastery_champ_6', 'm_lv_6',
+         'mastery_champ_7', 'm_lv_7', 'mastery_champ_8', 'm_lv_8', 'mastery_champ_9', 'm_lv_9',
+         'mastery_champ_10', 'm_lv_10', 'mastery_champ_11', 'm_lv_11', 'mastery_champ_12', 'm_lv_12',
+         'mastery_champ_13', 'm_lv_13', 'mastery_champ_14', 'm_lv_14', 'mastery_champ_15', 'm_lv_15',
+         'mastery_champ_16', 'm_lv_16'] +
+        
+        # Champion scores and others
+        ['1_champ_score', '2_champ_score', '3_champ_score', '4_champ_score', '5_champ_score'] +
+        ['avg_tier', 'team'] +
+        
+        # Champions individual score
+        ["Aatrox", "Ahri", "Akali", "Akshan", "Alistar", "Ambessa", "Amumu", "Anivia", "Annie", "Aphelios", 
+         "Ashe", "Aurelion Sol", "Aurora", "Azir", "Bard", "Bel'Veth", "Blitzcrank", "Brand", "Braum", 
+         "Briar", "Caitlyn", "Camille", "Cassiopeia", "Cho'Gath", "Corki", "Darius", "Diana", "Dr. Mundo", 
+         "Draven", "Ekko", "Elise", "Evelynn", "Ezreal", "Fiddlesticks", "Fiora", "Fizz", "Galio", 
+         "Gangplank", "Garen", "Gnar", "Gragas", "Graves", "Gwen", "Hecarim", "Heimerdinger", "Hwei", 
+         "Illaoi", "Irelia", "Ivern", "Janna", "Jarvan IV", "Jax", "Jayce", "Jhin", "Jinx", "K'Sante", 
+         "Kai'Sa", "Kalista", "Karma", "Karthus", "Kassadin", "Katarina", "Kayle", "Kayn", "Kennen", 
+         "Kha'Zix", "Kindred", "Kled", "Kog'Maw", "LeBlanc", "Lee Sin", "Leona", "Lillia", "Lissandra", 
+         "Lucian", "Lulu", "Lux", "Malphite", "Malzahar", "Maokai", "Master Yi", "Milio", "Miss Fortune", 
+         "Mordekaiser", "Morgana", "Naafiri", "Nami", "Nasus", "Nautilus", "Neeko", "Nidalee", "Nilah", 
+         "Nocturne", "Nunu & Willump", "Olaf", "Orianna", "Ornn", "Pantheon", "Poppy", "Pyke", "Qiyana", 
+         "Quinn", "Rakan", "Rammus", "Rek'Sai", "Rell", "Renata Glasc", "Renekton", "Rengar", "Riven", 
+         "Rumble", "Ryze", "Samira", "Sejuani", "Senna", "Seraphine", "Sett", "Shaco", "Shen", "Shyvana", 
+         "Singed", "Sion", "Sivir", "Skarner", "Smolder", "Sona", "Soraka", "Swain", "Sylas", "Syndra", 
+         "Tahm Kench", "Taliyah", "Talon", "Taric", "Teemo", "Thresh", "Tristana", "Trundle", "Tryndamere", 
+         "Twisted Fate", "Twitch", "Udyr", "Urgot", "Varus", "Vayne", "Veigar", "Vel'Koz", "Vex", "Vi", 
+         "Viego", "Viktor", "Vladimir", "Volibear", "Warwick", "Wukong", "Xayah", "Xerath", "Xin Zhao", 
+         "Yasuo", "Yone", "Yorick", "Yuumi", "Zac", "Zed", "Zeri", "Ziggs", "Zilean", "Zoe", "Zyra"]
+    )
+    
+    # Remove columns that exist in the DataFrame
+    columns_to_remove = [col for col in columns_to_remove if col in df.columns]
+    
+    # Drop the columns
+    df = df.drop(columns=columns_to_remove)
+    
+    # Print info about removed columns
+    print(f"Removed {len(columns_to_remove)} columns")
+    print(f"Remaining columns: {len(df.columns)}")
+    
+    return df
+
+
+def apply_feature_engineering(df, n=5):
+    """
+    Performs feature engineering pipeline
+    """
+    df = df.copy()
+    
+    # Engineering pipeline
+    transformations = [
+        calculate_champ_variety_score,
+        calculate_playstyle,
+        get_most_role_3,
+        calculate_role_specialization,
+        calculate_champion_loyalty,
+        lambda x: get_top_champion_scores(x, n),  # Add top 5 champions
+        remove_unwanted_columns,
+        optimize_feature_dtypes 
+    ]
+    
+    for transform in transformations:
+        try:
+            print(f"Applying {transform.__name__}...")
+            df = transform(df)
+        except Exception as e:
+            print(f"Error in {transform.__name__}: {str(e)}")
+            raise
+    
+    return df

util/main_scrapper.py

@@ -0,0 +1,36 @@
+from Recent_match_scrapper import get_multiple_matches_stats
+from Meta_scrapper import get_meta_stats
+from Leaderboard_scrapper import scrape_leaderboards
+from connection_check import check_connection
+from helper import merge_stats, filter_leaderboard, get_player_list
+from Player_scrapper import get_multiple_player_stats
+from feature_eng import create_champion_features
+from Weekly_meta_scrapper import get_weekly_meta
+
+
+#check_connection(region="euw", summoner="Szygenda #EUW")
+
+meta_stats = get_meta_stats()                       #save to meta_stats.csv
+weekly_meta_stats = get_weekly_meta()               #save to weekly_meta_stats.csv
+
+leaderboard = scrape_leaderboards(                #save to leaderboard_data.csv
+    regions=["kr", "euw", "vn", "na"],  
+    pages_per_region=5
+)
+
+filtered_lb = filter_leaderboard(                 #save to lb_filtered.csv   
+        df=leaderboard,
+        tiers=["CHALLENGER"]
+    )
+
+#player_list = get_player_list(filtered_lb)             
+#player_list = get_player_list()               # without arg, it will read from lb_filtered.csv
+
+player_stats = get_multiple_player_stats(player_list)    #save to player_stats.csv
+recent_stats = get_multiple_matches_stats(player_list)   #save to recent_stats.csv
+
+
+merged_stats = merge_stats(recent_stats, player_stats)          #save to player_stats_merged.csv
+
+#feature engineering
+training_features = create_champion_features(merged_stats, meta_stats, weekly_meta_stats, consider_team_comp=True)   #save to feature_eng_stats.csv

util/main_scrapper2.py

@@ -0,0 +1,46 @@
+from Recent_match_scrapper import get_multiple_matches_stats
+from Meta_scrapper import get_meta_stats
+from Leaderboard_scrapper import scrape_leaderboards
+from connection_check import check_connection
+from helper import merge_stats, filter_leaderboard, get_player_list
+from Player_scrapper import get_multiple_player_stats, get_player_stats
+from feature_eng import create_champion_features
+from Weekly_meta_scrapper import get_weekly_meta
+import pandas as pd
+
+
+#check_connection(region="euw", summoner="Szygenda #EUW")
+
+meta_stats = get_meta_stats()                       #save to meta_stats.csv
+
+#weekly_meta_stats = get_weekly_meta()               #save to weekly_meta_stats.csv
+
+#leaderboard = scrape_leaderboards(                #save to leaderboard_data.csv
+#    regions=["kr", "euw", "vn", "na"],  
+#    pages_per_region=5
+#)
+
+#filtered_lb = filter_leaderboard(                 #save to lb_filtered.csv   
+#        df=leaderboard,
+#        tiers=["CHALLENGER"]
+#    )
+
+#filtered_lb = pd.read_csv("util/data/lb_filtered_custom_2.csv")
+#player_list = get_player_list(filtered_lb)             
+#player_list = get_player_list()               # without arg, it will read from lb_filtered.csv
+
+
+##single player test
+lb_for_single = pd.read_csv("util/data/lb_filtered_for_single.csv")
+player_list = get_player_list(lb_for_single)
+
+player_stats = get_multiple_player_stats(player_list)    #save to player_stats.csv
+recent_stats = get_multiple_matches_stats(player_list)   #save to recent_stats.csv
+
+recent_stats = pd.read_csv("util/data/recent_matches.csv")
+player_stats = pd.read_csv("util/data/player_stats.csv")
+merged_stats = merge_stats(recent_stats, player_stats)          #save to player_stats_merged.csv
+
+#feature engineering
+merged_stats = pd.read_csv("util/data/player_stats_merged_2025-01-07.csv")
+training_features = create_champion_features(merged_player_stats=merged_stats, debug=None, consider_team_comp=True, test_mode=False)   #save to feature_eng_stats.csv