---
title: NBA Performance Predictor
emoji: 🏀
colorFrom: orange
colorTo: red
sdk: gradio
sdk_version: 3.40.0
app_file: app.py
pinned: false
license: mit
---


# 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

```python
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}}
}
```