from mplsoccer import Pitch import streamlit as st import requests from bs4 import BeautifulSoup from fuzzywuzzy import fuzz, process import plotly.express as px import numpy as np import matplotlib.pyplot as plt import streamlit as st def display_player_names_and_positions_middlePitch(home_lineups, away_lineups, position_id_to_coordinates_home, position_id_to_coordinates_away): # Dictionaries to store coordinates and player names for both home and away teams coordinates_dict_home = {} player_names_dict_home = {} coordinates_dict_away = {} player_names_dict_away = {} # Process home team lineup for index, row in home_lineups.iterrows(): player_name = row['player_name'] position_info = row['positions'][0] if row['positions'] else {} position_id = position_info.get('position_id', 'Unknown') if position_id in position_id_to_coordinates_home: coordinates_dict_home[position_id] = position_id_to_coordinates_home[position_id] player_names_dict_home[position_id] = player_name # Process away team lineup for index, row in away_lineups.iterrows(): player_name = row['player_name'] position_info = row['positions'][0] if row['positions'] else {} position_id = position_info.get('position_id', 'Unknown') if position_id in position_id_to_coordinates_away: coordinates_dict_away[position_id] = position_id_to_coordinates_away[position_id] player_names_dict_away[position_id] = player_name # Plotting the pitch pitch = Pitch( # pitch_color='#1f77b4', pitch_color='#d2d2d2', line_color='white', stripe=False, pitch_type='statsbomb' ) fig, ax = pitch.draw() # Plotting home team positions if coordinates_dict_home: x_coords_home, y_coords_home = zip(*coordinates_dict_home.values()) ax.scatter(x_coords_home, y_coords_home, color='red', s=300, edgecolors='white', zorder=3) for position_id, (x, y) in coordinates_dict_home.items(): name = player_names_dict_home.get(position_id, "Unknown") ax.text(x, y - 4, f'{name.split()[-1]}', color='black', ha='center', va='center', fontsize=12, zorder=6) # Plotting away team positions if coordinates_dict_away: x_coords_away, y_coords_away = zip(*coordinates_dict_away.values()) ax.scatter(x_coords_away, y_coords_away, color='blue', s=300, edgecolors='white', zorder=3) for position_id, (x, y) in coordinates_dict_away.items(): name = player_names_dict_away.get(position_id, "Unknown") ax.text(x, y - 4, f'{name.split()[-1]}', color='black', ha='center', va='center', fontsize=12, zorder=6) # Display the plot in Streamlit st.pyplot(fig) def display_player_names_and_positions_twoTeam(home_lineups, away_lineups, position_id_to_coordinates_home, position_id_to_coordinates_away): # Dictionaries to store coordinates and player names for both home and away teams coordinates_dict_home = {} player_names_dict_home = {} coordinates_dict_away = {} player_names_dict_away = {} # Process home team lineup for index, row in home_lineups.iterrows(): player_name = row['player_name'] position_info = row['positions'][0] if row['positions'] else {} position_id = position_info.get('position_id', 'Unknown') if position_id in position_id_to_coordinates_home: coordinates_dict_home[position_id] = position_id_to_coordinates_home[position_id] player_names_dict_home[position_id] = player_name # Process away team lineup for index, row in away_lineups.iterrows(): player_name = row['player_name'] position_info = row['positions'][0] if row['positions'] else {} position_id = position_info.get('position_id', 'Unknown') if position_id in position_id_to_coordinates_away: coordinates_dict_away[position_id] = position_id_to_coordinates_away[position_id] player_names_dict_away[position_id] = player_name # Plotting the pitch pitch = Pitch( # pitch_color='#1f77b4', pitch_color='#d2d2d2', line_color='white', stripe=False, pitch_type='statsbomb' ) fig, ax = pitch.draw() # Plotting home team positions if coordinates_dict_home: x_coords_home, y_coords_home = zip(*coordinates_dict_home.values()) ax.scatter(x_coords_home, y_coords_home, color='red', s=300, edgecolors='white', zorder=3) for position_id, (x, y) in coordinates_dict_home.items(): name = player_names_dict_home.get(position_id, "Unknown") ax.text(x, y - 4, f'{name.split()[-1]}', color='black', ha='center', va='center', fontsize=12, zorder=6) # Plotting away team positions if coordinates_dict_away: x_coords_away, y_coords_away = zip(*coordinates_dict_away.values()) ax.scatter(x_coords_away, y_coords_away, color='blue', s=300, edgecolors='white', zorder=3) for position_id, (x, y) in coordinates_dict_away.items(): name = player_names_dict_away.get(position_id, "Unknown") ax.text(x, y - 4, f'{name.split()[-1]}', color='black', ha='center', va='center', fontsize=12, zorder=6) # Display the plot in Streamlit st.pyplot(fig) def display_player_names_and_positions_oneTeam(home_lineups, position_id_to_coordinates_home,color): # Dictionaries to store coordinates and player names for the home team coordinates_dict_home = {} player_names_dict_home = {} # Process home team lineup for index, row in home_lineups.iterrows(): player_name = row['player_name'] position_info = row['positions'][0] if row['positions'] else {} position_id = position_info.get('position_id', 'Unknown') if position_id in position_id_to_coordinates_home: coordinates_dict_home[position_id] = position_id_to_coordinates_home[position_id] player_names_dict_home[position_id] = player_name # Plotting the pitch pitch = Pitch( pitch_color='#d2d2d2', line_color='white', stripe=False, pitch_type='statsbomb' ) fig, ax = pitch.draw() # Plotting home team positions if coordinates_dict_home: x_coords_home, y_coords_home = zip(*coordinates_dict_home.values()) ax.scatter(x_coords_home, y_coords_home, color=color, s=300, edgecolors='white', zorder=3) for position_id, (x, y) in coordinates_dict_home.items(): name = player_names_dict_home.get(position_id, "Unknown") ax.text(x, y - 4, f'{name.split()[-1]}', color='black', ha='center', va='center', fontsize=12, zorder=6) # Display the plot in Streamlit st.pyplot(fig) # Function to find the best image match based on the team name def get_best_match_image(team_name, images_data): image_names = [image['image_name'] for image in images_data] best_match, match_score = process.extractOne(team_name, image_names, scorer=fuzz.token_sort_ratio) if match_score > 70: # Adjust the threshold as needed for image in images_data: if image['image_name'] == best_match: return image['image_link'] return None class StadiumImageFetcher: def __init__(self): self.headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3" } def get_stadium_image_url(self, stadium_name): # Formulate the Google Image search URL query = stadium_name.replace(' ', '+') url = f"https://www.google.com/search?hl=en&tbm=isch&q={query}" # Send the request response = requests.get(url, headers=self.headers) # Parse the HTML content using BeautifulSoup soup = BeautifulSoup(response.text, 'lxml') # Find the first image result images = soup.find_all('img') if images and len(images) > 1: # The first image might be the Google logo, so skip it return images[1]['src'] else: return None def display_stadium(self): st.title("Stadium Image Display") # Input the stadium name stadium_name = st.text_input("Enter the stadium name:") if stadium_name: # Get the image URL image_url = self.get_stadium_image_url(stadium_name) if image_url: st.image(image_url, caption=f'{stadium_name} Stadium') else: st.write("Image not found.") def main_stadium(): stadium = StadiumImageFetcher() stadium.display_stadium() def box_plot_pass(events,team): event_filterTeam = events[events['team']==team] event_pass = event_filterTeam.filter(regex='^(pass)', axis=1).dropna(how='all') event_pass_length = event_pass['pass_length'] fig = px.box(event_pass_length, y=event_pass_length, labels={'y':'Pass Length'}, title=f'{team} Distribution of Pass Length') fig.update_layout(width=300, height=600) # 200 pixels de largeur, 400 pixels de hauteur # Afficher le graphique dans Streamlit st.plotly_chart(fig) # def get_possession(events): # """ # Extracts and normalizes the possession counts for the two teams from the events DataFrame. # Parameters: # - events (pd.DataFrame): DataFrame containing an events column 'possession_team'. # Returns: # - home_team_possession (float): Normalized possession percentage for the home team, rounded to one decimal place. # - away_team_possession (float): Normalized possession percentage for the away team, rounded to one decimal place. # """ # # Get possession counts # possession_counts = events['possession_team'].value_counts() # # Get the top two possession teams # home_team_possession, away_team_possession = possession_counts.iloc[0], possession_counts.iloc[1] # # Calculate total possession # total_possession = home_team_possession + away_team_possession # # Normalize possession and round to one decimal place # home_team_possession = round((home_team_possession / total_possession) * 100, 1) # away_team_possession = round((away_team_possession / total_possession) * 100, 1) # return home_team_possession, away_team_possession # def get_total_xg(events): # """ # Calcule la somme totale des xG (expected goals) par équipe et retourne les valeurs arrondies à 2 décimales. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'shot_statsbomb_xg' et 'team'. # Returns: # - home_xg (float): Somme totale des xG pour l'équipe à domicile, arrondie à 2 décimales. # - away_xg (float): Somme totale des xG pour l'équipe à l'extérieur, arrondie à 2 décimales. # """ # # Filtrer les colonnes et supprimer les lignes où 'shot_statsbomb_xg' est NaN # data = events.filter(regex='^shot_statsbomb_xg|^team$').dropna(subset=['shot_statsbomb_xg']) # # Grouper par 'team' et calculer la somme des xG # data = data.groupby('team')['shot_statsbomb_xg'].sum() # # Arrondir les résultats à 2 décimales # home_xg, away_xg = round(data.iloc[0], 2), round(data.iloc[1], 2) # return home_xg, away_xg # def get_total_shots(events): # """ # Calcule le nombre total de tirs (shots) par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'shot_statsbomb_xg' et 'team'. # Returns: # - home_shots (int): Nombre total de tirs pour l'équipe à domicile. # - away_shots (int): Nombre total de tirs pour l'équipe à l'extérieur. # """ # # Filtrer les colonnes et supprimer les lignes où 'shot_statsbomb_xg' est NaN # data = events.filter(regex='^shot_statsbomb_xg|^team$').dropna(subset=['shot_statsbomb_xg']) # # Grouper par 'team' et compter le nombre de tirs # shot_counts = data.groupby('team').count()['shot_statsbomb_xg'] # # Retourner les comptes pour les deux équipes # home_shots, away_shots = shot_counts.iloc[0], shot_counts.iloc[1] # return home_shots, away_shots # def get_total_shots_off_target(events): # """ # Calcule le nombre total de tirs non cadrés (off target) par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'shot_outcome' et 'team'. # Returns: # - home_off_target (int): Nombre total de tirs non cadrés pour l'équipe à domicile. # - away_off_target (int): Nombre total de tirs non cadrés pour l'équipe à l'extérieur. # """ # # Define outcomes that indicate a shot was off target # off_target_outcomes = ['Off T', 'Blocked', 'Missed'] # # Filter the events DataFrame for shots off target # data = events[events['shot_outcome'].isin(off_target_outcomes)] # # Group by 'team' and count the number of off-target shots # off_target_counts = data.groupby('team').size() # # Return the counts for the two teams # home_off_target, away_off_target = off_target_counts.iloc[0], off_target_counts.iloc[1] # return home_off_target, away_off_target # def get_total_shots_on_target(events): # """ # Calcule le nombre total de tirs cadrés (on target) par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'shot_outcome' et 'team'. # Returns: # - home_on_target (int): Nombre total de tirs cadrés pour l'équipe à domicile. # - away_on_target (int): Nombre total de tirs cadrés pour l'équipe à l'extérieur. # """ # # Define outcomes that indicate a shot was on target # on_target_outcomes = ['Goal', 'Saved', 'Saved To Post', 'Shot Saved Off Target'] # # Filter the events DataFrame for shots on target # data = events[events['shot_outcome'].isin(on_target_outcomes)] # # Group by 'team' and count the number of on-target shots # on_target_counts = data.groupby('team').size() # # Return the counts for the two teams # home_on_target, away_on_target = on_target_counts.iloc[0], on_target_counts.iloc[1] # return home_on_target, away_on_target # def get_total_passes(events): # """ # Calcule le nombre total de passes par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'pass_outcome' et 'team'. # Returns: # - home_passes (int): Nombre total de passes pour l'équipe à domicile. # - away_passes (int): Nombre total de passes pour l'équipe à l'extérieur. # """ # # Filtrer les colonnes 'pass_outcome' et 'team', puis grouper par équipe et compter le nombre de passes # pass_counts = events.filter(regex='^pass_end_location|^team$').groupby('team').count()['pass_end_location'] # # Retourner les comptes pour les deux équipes # home_passes, away_passes = pass_counts.iloc[0], pass_counts.iloc[1] # return home_passes, away_passes # def get_successful_passes(events): # """ # Calculates the total number of successful passes for home and away teams. # Parameters: # - events (pd.DataFrame): DataFrame containing the columns for all passes and pass outcomes. # Returns: # - home_successful_passes (int): Successful passes for the home team. # - away_successful_passes (int): Successful passes for the away team. # """ # # Get the total passes using the get_total_passes function # home_passes, away_passes = get_total_passes(events) # # Identify unsuccessful passes based on 'type' or another column indicating unsuccessful outcomes # unsuccessful_passes = events.filter(regex='^pass_outcome|^team$').groupby('team').count().reset_index()['pass_outcome'] # # Calculate successful passes by subtracting unsuccessful passes from total passes # home_unsuccessful_passes = unsuccessful_passes.iloc[0] # away_unsuccessful_passes = unsuccessful_passes.iloc[1] # home_successful_passes = home_passes - home_unsuccessful_passes # away_successful_passes = away_passes - away_unsuccessful_passes # return home_successful_passes, away_successful_passes # def get_total_corners(events): # """ # Calcule le nombre total de corners par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'pass_type' et 'team'. # Returns: # - home_corners (int): Nombre total de corners pour l'équipe à domicile. # - away_corners (int): Nombre total de corners pour l'équipe à l'extérieur. # """ # # Filtrer les colonnes 'pass_type' et 'team', puis grouper par équipe et compter le nombre de corners # corner_counts = events.filter(regex='^pass_type|^team$').query('pass_type == "Corner"').groupby('team').count()['pass_type'] # # Extract the team names from the events DataFrame # teams = events['team'].unique() # # Handle cases where a team might not have any corners # home_corners = corner_counts.get(teams[0], 0) # away_corners = corner_counts.get(teams[1], 0) # return home_corners, away_corners # def get_total_fouls(events): # """ # Calcule le nombre total de fautes par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'team' et 'type'. # Returns: # - home_fouls (int): Nombre total de fautes pour l'équipe à domicile. # - away_fouls (int): Nombre total de fautes pour l'équipe à l'extérieur. # """ # fouls = events[events['type'] == 'Foul Committed'] # foul_counts = fouls.groupby('team').size() # home_fouls, away_fouls = foul_counts.iloc[0], foul_counts.iloc[1] # return home_fouls, away_fouls # def get_total_yellow_cards(events): # """ # Calcule le nombre total de cartons jaunes par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'bad_behaviour_card' et 'team'. # Returns: # - home_yellow_cards (int): Nombre total de cartons jaunes pour l'équipe à domicile. # - away_yellow_cards (int): Nombre total de cartons jaunes pour l'équipe à l'extérieur. # """ # # Filtrer les colonnes 'bad_behaviour_card' et 'team', puis grouper par équipe et compter le nombre de cartons jaunes # yellow_card_counts = events.filter(regex='^bad_behaviour_card|^team$').query('bad_behaviour_card == "Yellow Card"').groupby('team').count()['bad_behaviour_card'] # # Extraire les noms des équipes de l'événement DataFrame # teams = events['team'].unique() # # Gérer les cas où une équipe pourrait ne pas avoir de cartons jaunes # home_yellow_cards = yellow_card_counts.get(teams[0], 0) # away_yellow_cards = yellow_card_counts.get(teams[1], 0) # return home_yellow_cards, away_yellow_cards # def get_total_red_cards(events): # """ # Calcule le nombre total de cartons rouges par équipe. # Parameters: # - events (pd.DataFrame): DataFrame contenant les colonnes 'bad_behaviour_card' et 'team'. # Returns: # - home_red_cards (int): Nombre total de cartons rouges pour l'équipe à domicile. # - away_red_cards (int): Nombre total de cartons rouges pour l'équipe à l'extérieur. # """ # # Filtrer les colonnes 'bad_behaviour_card' et 'team', puis grouper par équipe et compter le nombre de cartons rouges # red_card_counts = events.filter(regex='^bad_behaviour_card|^team$').query('bad_behaviour_card == "Red Card"').groupby('team').count()['bad_behaviour_card'] # # Extraire les noms des équipes de l'événement DataFrame # teams = events['team'].unique() # # Gérer les cas où une équipe pourrait ne pas avoir de cartons rouges # home_red_cards = red_card_counts.get(teams[0], 0) # away_red_cards = red_card_counts.get(teams[1], 0) # return home_red_cards, away_red_cards # def display_normalized_scores(home_scores, away_scores, categories, home_color='blue', away_color='green', background_color='lightgray', bar_height=0.8, spacing_factor=2.5): # """ # Displays a horizontal bar chart with normalized scores for home and away teams. # Parameters: # - home_scores: List of scores for the home team. # - away_scores: List of scores for the away team. # - categories: List of category names for each score. # - home_color: Color of the bars representing the home team. # - away_color: Color of the bars representing the away team. # - background_color: Color of the background rectangles. # - bar_height: Height of the bars and background rectangles. # - spacing_factor: Factor to adjust the spacing between bars. # """ # # Internal container size variables # container_width = '50%' # Adjust width as needed # container_height = 'auto' # Adjust height as needed # # Normalizing the scores # home_normalized = [] # away_normalized = [] # for home, away in zip(home_scores, away_scores): # total = home + away # if total != 0: # home_normalized.append((home / total) * 100) # away_normalized.append((away / total) * 100) # else: # home_normalized.append(0) # away_normalized.append(0) # # Augmenting the spacing between the bars by multiplying y_pos by a factor # y_pos = np.arange(len(categories)) * spacing_factor # # Plot # fig, ax = plt.subplots(figsize=(10, 8)) # # Adding light gray backgrounds for each category with the same height as the bars # for i in range(len(categories)): # ax.add_patch(plt.Rectangle((-100, y_pos[i] - bar_height / 2), 200, bar_height, color=background_color, alpha=0.3, linewidth=0)) # # Plotting normalized scores for both teams # ax.barh(y_pos, home_normalized, height=bar_height, color=home_color, align='center', label='Home Team') # ax.barh(y_pos, [-score for score in away_normalized], height=bar_height, color=away_color, align='center', label='Away Team') # # Positioning the category names above the bars to avoid overlap # for i in range(len(categories)): # ax.text(0, y_pos[i] + bar_height / 2 + 0.1, categories[i], ha='center', va='bottom', fontsize=10) # # Adding non-normalized values to the end of the bars # for i in range(len(categories)): # ax.text(home_normalized[i] / 2, y_pos[i], f'{home_scores[i]}', va='center', color='white', fontweight='bold') # ax.text(-away_normalized[i] / 2, y_pos[i], f'{away_scores[i]}', va='center', color='white', fontweight='bold') # # Adjusting the axis limits # ax.set_xlim(-100, 100) # ax.set_ylim(-1, max(y_pos) + spacing_factor) # # Hiding the spines # for spine in ax.spines.values(): # spine.set_visible(False) # # Removing y-ticks and x-ticks # ax.set_yticks([]) # ax.set_xticks([]) # # Custom HTML/CSS to control the size of the container # st.markdown( # f""" # #
# """, # unsafe_allow_html=True # ) # # Displaying the plot in Streamlit # st.pyplot(fig) # # Closing the custom container # st.markdown('
', unsafe_allow_html=True) def display_normalized_scores(home_scores, away_scores, categories, home_color='blue', away_color='green', background_color='lightgray', bar_height=0.8, spacing_factor=2.5): """ Displays a horizontal bar chart with normalized scores for home and away teams. Parameters: - home_scores: List of scores for the home team. - away_scores: List of scores for the away team. - categories: List of category names for each score. - home_color: Color of the bars representing the home team. - away_color: Color of the bars representing the away team. - background_color: Color of the background rectangles. - bar_height: Height of the bars and background rectangles. - spacing_factor: Factor to adjust the spacing between bars. """ # Normalizing the scores home_normalized = [] away_normalized = [] for home, away in zip(home_scores, away_scores): # Remplacer les None par 0 pour éviter les erreurs home = 0 if home is None else home away = 0 if away is None else away total = home + away if total != 0: home_normalized.append((home / total) * 100) away_normalized.append((away / total) * 100) else: home_normalized.append(0) away_normalized.append(0) # Augmenting the spacing between the bars by multiplying y_pos by a factor y_pos = np.arange(len(categories)) * spacing_factor # Plot fig, ax = plt.subplots(figsize=(10, 8)) # Adding light gray backgrounds for each category with the same height as the bars for i in range(len(categories)): ax.add_patch(plt.Rectangle((-100, y_pos[i] - bar_height / 2), 200, bar_height, color=background_color, alpha=0.3, linewidth=0)) # Plotting normalized scores for both teams ax.barh(y_pos, home_normalized, height=bar_height, color=home_color, align='center', label='Home Team') ax.barh(y_pos, [-score for score in away_normalized], height=bar_height, color=away_color, align='center', label='Away Team') # Positioning the category names above the bars to avoid overlap for i in range(len(categories)): ax.text(0, y_pos[i] + bar_height / 2 + 0.1, categories[i], ha='center', va='bottom', fontsize=10) # Adding non-normalized values to the end of the bars for i in range(len(categories)): ax.text(home_normalized[i] / 2, y_pos[i], f'{home_scores[i]}', va='center', color='white', fontweight='bold') ax.text(-away_normalized[i] / 2, y_pos[i], f'{away_scores[i]}', va='center', color='white', fontweight='bold') # Adjusting the axis limits ax.set_xlim(-100, 100) ax.set_ylim(-1, max(y_pos) + spacing_factor) # Hiding the spines for spine in ax.spines.values(): spine.set_visible(False) # Removing y-ticks and x-ticks ax.set_yticks([]) ax.set_xticks([]) # Displaying the plot in Streamlit st.pyplot(fig)