NBA Player Performance Predictor

Model Description

This model predicts NBA player points per game (PPG) using XGBoost regression with time-series features. The model uses historical player statistics, lag features, and engineered metrics to make predictions.

Model Details

Model Type: XGBoost Regressor
Task: Regression (Predicting NBA player points per game)
Framework: scikit-learn, XGBoost
Performance: RMSE ~3-5 points per game, R² ~0.6-0.8

Features

The model uses various features including:

Basic stats: Age, Games, Minutes Played, Field Goals, etc.
Lag features: Previous season performance metrics
Rolling averages: 2-3 year performance averages
Efficiency metrics: Points per minute, overall efficiency
Categorical encodings: Position, Team, Age category

Usage

from huggingface_model import NBAPerformancePredictorHF

# Load the model
model = NBAPerformancePredictorHF("path/to/model")

# Example prediction
player_stats = {
    'Age': 27,
    'G': 75,
    'GS': 70,
    'MP': 35.0,
    'FG': 8.5,
    'FGA': 18.0,
    'FG_1': 0.472,
    'Pos_encoded': 2,
    'Team_encoded': 15,
    'Age_category_encoded': 1,
    'PTS_lag_1': 22.5,
    'PTS_lag_2': 21.0,
    'TRB_lag_1': 7.2,
    'AST_lag_1': 4.8
}

predicted_points = model.predict(player_stats)
print(f"Predicted PPG: {predicted_points:.2f}")

Training Data

The model was trained on NBA player statistics from multiple seasons, including:

Regular season statistics
Playoff performance data
Historical player performance trends

Limitations

Requires historical data (lag features) for accurate predictions
Performance may vary for rookie players or players with limited history
Model is trained on specific NBA eras and may need retraining for different time periods

Ethical Considerations

This model is for educational and analytical purposes. It should not be used for:

Player salary negotiations
Draft decisions without additional context
Any form of discrimination or bias

Citation

@misc{nba_performance_predictor,
  title={NBA Player Performance Predictor using XGBoost},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/your-username/nba-performance-predictor}}
}