Exercise - Use machine learning and standard deviation to create fictional game data

100 XP
  • 5 minutes

We've run into a problem. We can't get real-time game data from the Tune Squad playing. There's no way for us to watch a game of 15 Tune Squad characters playing basketball and test out our model in an actual app.

But, we can use the trick we learned about testing our model to create a new dataset that will simulate a game!

The web app that we'll build in the upcoming units will help us decide which player to give a water break to every 12 minutes of a standard 48-minute game. So, we should create a CSV file that will contain randomized player data over four iterations: 0 minutes (the start of the game), 12 minutes, 24 minutes, and 36 minutes:

Python 
# Initialize four empty DataFrames, one for each 12-minute period.
number_of_iterations = 4
df_list = [pd.DataFrame(columns=game_stat_cols, index=list(ts_df['player_name'])) for i in range(number_of_iterations)]

# For each period, generate randomized player data and predict the PER.
# Use the model fitted earlier.
for df in df_list:
    for stat in game_stat_cols:
        df[stat] = list(ts_df[stat] + randn(len(ts_df)) * stdev_s[stat])
    df['PER'] = lin_reg.predict(df)

# Concatenate the DataFrames and make the players' names the index.
game_df = pd.concat(df_list)
game_df.rename_axis('player_name', inplace=True)

# Create another index for the period in question.
minutes = [(x // len(ts_df)) * 12 for x in range(len(game_df))]
game_df['minutes'] = minutes
game_df.set_index('minutes', append=True, inplace=True)
game_df = game_df.swaplevel()

game_df

Here's the output:

minutes player_name TS% AST TO USG ORR DRR REBR PER
0 Sylvester 0.617897 31.998307 16.008589 39.097789 6.413572 15.286788 12.792845 31.761414
Marvin the Martian 0.605018 33.807167 13.666316 34.011445 6.441147 18.954713 15.582038 31.065373
Road Runner 0.606381 29.732153 15.027962 38.444237 4.532569 27.268949 10.968630 21.550397
Foghorn Leghorn 0.606442 31.879217 15.467578 36.674624 6.019145 20.964740 14.774713 29.283807
Bugs Bunny 0.636505 35.193108 15.708836 37.962839 6.116200 23.132887 15.677267 31.850749
... ... ... ... ... ... ... ... ... ...
36 Granny 0.608159 26.021605 14.814805 31.721274 6.422251 22.213857 16.454224 27.644534
Wile E. Coyote 0.601998 29.851305 14.137783 32.102558 5.819653 25.550118 15.818051 25.497379
Tweety 0.607711 25.210393 12.609053 34.014773 6.628928 27.041059 11.643892 20.439595
Penelope 0.613901 25.007772 14.394944 28.324923 7.564207 23.184055 9.919301 16.124275
Daffy Duck 0.634848 31.911011 14.694308 33.226023 6.308140 18.023737 16.305183 33.155391

60 rows × eight columns

The final DataFrame looks complete. We can save it as a CSV file, so we can use it in our web app. When saving this DataFrame as a CSV file, we'll want to keep the indices, because we made them the player's names.

Python 
# Export the finished DataFrame to CSV.
game_df.to_csv('game_stats.csv')

A new CSV file should appear in your Visual Studio Code folder:

Screenshot that shows the game stats C S V file in Visual Studio Code Explorer.

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