title: GBM handling factor variables, worried about too many factors
body: <p>I am working on a basketball model that predicts how well an NBA player will play in their next game, based on how well they have performed in all previous games of the season. There are roughly 10 players per NBA team, and each of 30 teams has played about 25 games this season, so my dataframe has about 10*30*25 = 7,500 observations at this point. I run my model each day, predicting how well players will play in the next day - therefore, for tomorrow I will make roughly 10*30 = 300 predictions. </p>
  
  <p>My question is this - currently i have about 50 columns / features / x-variables that I am using for prediction, all of which are numeric variables (average number of points scored, average number of rebounds, etc.). However, I think it may help  my model to know which player each row corresponds to. That is, I want to pass a 51st column, a factor variable including the players names. I read online that GBM can deal with factor variables as it will "dummify" them internally, however I am worried that "dummifying" 300 different players will not perform well. Will passing a factor variable with all of the player names backfire and ultimately hurt my model, due to the large number of dummy variables it will create internally, or is this okay?</p>
  
  <pre><code>my_df                        
                          PLAYER FG FGA X3P X3PA FT FTA
  1042            Andre Drummond  6  16   0    0  6  10
  17747            Marcus Morris  6  19   1    4  5   6
  14861 Kentavious Caldwell-Pope  7  14   4    7  3   3
  7976            Ersan Ilyasova  6  12   3    6  1   2
  22401           Reggie Jackson  4  10   2    4  5   5
  24475          Stanley Johnson  3  10   1    3  0   0
  24649              Steve Blake  1   6   1    5  0   0
  12489              Jodie Meeks  1   4   0    0  0   0
  1955               Aron Baynes  3   5   0    0  0   0
  21500             Paul Millsap  7  15   2    6  3   4
  </code></pre>