player-performance-ratings 5.27.0

Match Predictions based on Player Ratings

player-performance-ratings

Framework designed to predict outcomes in sports games using player-based ratings or other forms of engineered features such as rolling means. Ratings can be used to predict game-winner, but also other outcomes such as total points scored, total yards gained, etc.

Installation

pip install player-performance-ratings

Examples

Ensure you have a dataset where each row is a unique combination of game_ids and player_ids. There are multiple different use-cases for the framework, such as:

1. Creating ratings for players/teams.
2. Predicting the outcome.
3. Creating features or other types of data-transformations

Training a Rating Model

If you only desire to generate ratings this is simple:

from player_performance_ratings.ratings import UpdateRatingGenerator
from examples import get_sub_sample_nba_data
from player_performance_ratings.data_structures import ColumnNames

df = get_sub_sample_nba_data(as_pandas=True)

# Defines the column names as they appear in the dataframe
column_names = ColumnNames(
    team_id='team_id',
    match_id='game_id',
    start_date="start_date",
    player_id="player_name",
)
# Sorts the dataframe. The dataframe must always be sorted as below
df = df.sort_values(by=[column_names.start_date, column_names.match_id, column_names.team_id, column_names.player_id])


# Drops games with less or more than 2 teams
df = (
    df.assign(team_count=df.groupby(column_names.match_id)[column_names.team_id].transform('nunique'))
    .loc[lambda x: x.team_count == 2]
    .drop(columns=['team_count'])
)

# Pretends the last 10 games are future games. The most will be trained on everything before that.
most_recent_10_games = df[column_names.match_id].unique()[-10:]
historical_df = df[~df[column_names.match_id].isin(most_recent_10_games)]
future_df = df[df[column_names.match_id].isin(most_recent_10_games)]

# Defining a simple rating-generator. It will use the "won" column to update the ratings.
# In contrast to a typical Elo, ratings will follow players.
rating_generator = UpdateRatingGenerator(performance_column='won')

# Calculate Ratings on Historical data
historical_df_with_ratings = rating_generator.generate_historical(historical_df, column_names=column_names)

# Printing out the 10 highest rated teams and the ratings of the players for the team
team_ratings = rating_generator.team_ratings
print(team_ratings[:10])

#Calculating Ratings for Future Matches
future_df_with_ratings = rating_generator.generate_future(future_df)

Predicting Game-Winner

Ensure you have a dataset where each row is a unique combination of game_ids and player_ids. Even if the concept of a player doesn't exist in the dataset, you can use team_id instead of player_id.

Utilizing a rating model can be as simple as:

from examples import get_sub_sample_nba_data
from player_performance_ratings.pipeline import Pipeline
from player_performance_ratings.predictor import GameTeamPredictor

from player_performance_ratings.ratings import UpdateRatingGenerator

from player_performance_ratings.data_structures import ColumnNames

df = get_sub_sample_nba_data(as_pandas=True)

# Defines the column names as they appear in the dataframe
column_names = ColumnNames(
    team_id='team_id',
    match_id='game_id',
    start_date="start_date",
    player_id="player_name",
)
# Sorts the dataframe. The dataframe must always be sorted as below
df = df.sort_values(by=[column_names.start_date, column_names.match_id, column_names.team_id, column_names.player_id])



# Drops games with less or more than 2 teams
df = (
    df.assign(team_count=df.groupby(column_names.match_id)[column_names.team_id].transform('nunique'))
    .loc[lambda x: x.team_count == 2]
    .drop(columns=['team_count'])
)

# Pretends the last 10 games are future games. The most will be trained on everything before that.
most_recent_10_games = df[column_names.match_id].unique()[-10:]
historical_df = df[~df[column_names.match_id].isin(most_recent_10_games)]
future_df = df[df[column_names.match_id].isin(most_recent_10_games)].drop(columns=['won'])

# Defining a simple rating-generator. It will use the "won" column to update the ratings.
# In contrast to a typical Elo, ratings will follow players.
rating_generator = UpdateRatingGenerator(performance_column='won')

# Defines the predictor. A machine-learning model will be used to predict game winner on a game-team-level.
# Mean team-ratings will be calculated (from player-level) and rating-difference between the 2 teams calculated.
# It will also use the location of the game as a feature.
predictor = GameTeamPredictor(
    game_id_colum=column_names.match_id,
    team_id_column=column_names.team_id,
    estimator_features=['location'],
    target='won',
    one_hot_encode_cat_features=True
)

# Pipeline is whether we define all the steps. Other transformations can take place as well.
# However, in our simple example we only have a simple rating-generator and a predictor.
pipeline = Pipeline(
    rating_generators=rating_generator,
    predictor=predictor,
    column_names=column_names,
)

# Trains the model and returns historical predictions
historical_predictions = pipeline.train(df=historical_df)

# Future predictions on future results
future_predictions = pipeline.predict(df=future_df)

#Grouping predictions from game-player level to game-level.
team_grouped_predictions = future_predictions.groupby(column_names.match_id).first()[
    [column_names.start_date, column_names.team_id, 'team_id_opponent', predictor.pred_column]]

print(team_grouped_predictions)

Calculating Rolling Means, Lags and Ratings in the same Pipeline

If the user simply wants to calculate features without directly feeding into a prediction-model, this can be done using PipelineTransformer. The example below calculates rolling-means and lags for kills, deaths, the result and calculates a rating based on the result. It then outputs the dataframe with the new features.

from examples import get_sub_sample_lol_data
from player_performance_ratings import ColumnNames
from player_performance_ratings.pipeline_transformer import PipelineTransformer
from player_performance_ratings.ratings import UpdateRatingGenerator

from player_performance_ratings.transformers import LagTransformer
from player_performance_ratings.transformers.lag_generators import RollingMeanTransformer

column_names = ColumnNames(
    team_id='teamname',
    match_id='gameid',
    start_date="date",
    player_id="playername",
    league='league',
    position='position',
)
df = get_sub_sample_lol_data(as_pandas=True)
df = (
    df.loc[lambda x: x.position != 'team']
    .assign(team_count=df.groupby('gameid')['teamname'].transform('nunique'))
    .loc[lambda x: x.team_count == 2]
    .assign(player_count=df.groupby(['gameid', 'teamname'])['playername'].transform('nunique'))
    .loc[lambda x: x.player_count == 5]
)
df = (df
.assign(team_count=df.groupby('gameid')['teamname'].transform('nunique'))
.loc[lambda x: x.team_count == 2]
)


# Pretends the last 10 games are future games. The most will be trained on everything before that.
most_recent_10_games = df[column_names.match_id].unique()[-10:]
historical_df = df[~df[column_names.match_id].isin(most_recent_10_games)]
future_df = df[df[column_names.match_id].isin(most_recent_10_games)].drop(columns=['result'])

rating_generator = UpdateRatingGenerator(
    performance_column='result'
)

lag_generators = [
    LagTransformer(
        features=["kills", "deaths", "result"],
        lag_length=3,
        granularity=['playername']
    ),
    RollingMeanTransformer(
        features=["kills", "deaths", "result"],
        window=20,
        min_periods=1,
        granularity=['playername']
    )
]


transformer = PipelineTransformer(
    column_names=column_names,
    rating_generators=rating_generator,
    lag_generators=lag_generators
)

historical_df = transformer.fit_transform(historical_df)

future_df = transformer.transform(future_df)
print(future_df.head())

Hyperparameter tuning

TODO

## Advanced usecases

The listed examples above are quite simple. 
However, the framework is designed to be flexible and can easily be extended in order to create better models:
Examples:

* Create a better margin of victory bombine multiple columns to create a performance_column which ratings will be calculated based on.
* Combine rolling-means, lags with ratings to create a more complex model.
* Add other features such as weather, home/away, etc.
* Predict other outcomes than game-winner, such as total points scored, total yards gained, etc.
* Create custom transformations utilizing domain knowledge of the sport.