| | --- |
| | title: NBA Performance Predictor |
| | emoji: 🏀 |
| | colorFrom: red |
| | colorTo: blue |
| | sdk: gradio |
| | sdk_version: 5.44.0 |
| | app_file: app.py |
| | pinned: false |
| | license: mit |
| | --- |
| | |
| | # NBA Player Performance Predictor |
| |
|
| | ## Model Description |
| |
|
| | This interactive web application predicts NBA player points per game (PPG) using machine learning. The model analyzes historical player statistics, lag features, and engineered metrics to make predictions. |
| |
|
| | ## Features |
| |
|
| | - **Interactive Interface**: User-friendly sliders and inputs for player statistics |
| | - **Example Players**: Pre-loaded NBA stars (LeBron James, Stephen Curry, etc.) |
| | - **Real-time Predictions**: Instant predictions as you adjust parameters |
| | - **Player Categories**: Automatic classification (Role Player → Superstar) |
| | - **Mobile Friendly**: Works on phones, tablets, and desktops |
| |
|
| | ## How to Use |
| |
|
| | 1. **Input Current Season Stats**: Use sliders to set age, games played, minutes, etc. |
| | 2. **Add Historical Data**: Enter previous season performance metrics |
| | 3. **Select Position**: Choose the player's primary position |
| | 4. **Get Prediction**: Click "🔮 Predict Performance" for instant results |
| | 5. **Try Examples**: Use the example player buttons for quick testing |
| |
|
| | ## Model Details |
| |
|
| | - **Task**: Regression (Predicting NBA player points per game) |
| | - **Method**: XGBoost with time-series features |
| | - **Features**: Age, games, minutes, shooting stats, historical performance |
| | - **Performance**: RMSE ~3-5 points per game, R² ~0.6-0.8 |
| |
|
| | ## Key Features Used |
| |
|
| | The model considers various factors: |
| | - **Basic Stats**: Age, Games, Minutes Played, Field Goals, etc. |
| | - **Historical Performance**: Previous season statistics |
| | - **Efficiency Metrics**: Points per minute, overall efficiency |
| | - **Position & Team**: Encoded categorical variables |
| | - **Trend Analysis**: Performance changes over time |
| |
|
| | ## Prediction Categories |
| |
|
| | Based on predicted PPG: |
| | - 🔵 **Role Player**: < 8 PPG |
| | - 🟢 **Solid Contributor**: 8-15 PPG |
| | - 🟡 **Good Scorer**: 15-20 PPG |
| | - 🟠 **Star Player**: 20-25 PPG |
| | - 🔴 **Superstar**: 25+ PPG |
| |
|
| | ## Example Players |
| |
|
| | Try these pre-loaded examples: |
| | - **LeBron James (Prime)**: All-around superstar stats |
| | - **Stephen Curry (Peak)**: Elite shooting guard numbers |
| | - **Rookie Player**: Typical first-year player stats |
| | - **Veteran Role Player**: Experienced bench contributor |
| |
|
| | ## Technical Implementation |
| |
|
| | - **Frontend**: Gradio for interactive web interface |
| | - **Backend**: Python with XGBoost, scikit-learn, pandas |
| | - **Deployment**: Hugging Face Spaces |
| | - **Fallback Mode**: Simple heuristic when ML model unavailable |
| |
|
| | ## Limitations |
| |
|
| | - Works best for players with NBA history (lag features required) |
| | - May be less accurate for rookies or players with significant role changes |
| | - Predictions based on historical patterns, may not account for injuries or major team changes |
| | - Current version runs in fallback mode (simplified predictions) |
| |
|
| | ## Future Improvements |
| |
|
| | - Full XGBoost model integration |
| | - Additional statistics (advanced metrics, team context) |
| | - Multi-target prediction (rebounds, assists, efficiency) |
| | - Player comparison features |
| | - Historical trend visualization |
| |
|
| | ## Usage Examples |
| |
|
| | ### Basic Prediction |
| | ```python |
| | # Example input for a typical NBA player |
| | player_stats = { |
| | 'age': 27, |
| | 'games': 75, |
| | 'minutes': 32.0, |
| | 'field_goal_pct': 45.0, |
| | 'position': 'Small Forward', |
| | 'pts_last_season': 18.5 |
| | } |
| | ``` |
| |
|
| | ### Star Player Example |
| | ```python |
| | # Example for elite player |
| | star_stats = { |
| | 'age': 28, |
| | 'games': 79, |
| | 'minutes': 36.0, |
| | 'field_goal_pct': 50.0, |
| | 'position': 'Point Guard', |
| | 'pts_last_season': 28.5 |
| | } |
| | ``` |
| |
|
| | ## Data Sources |
| |
|
| | The model was trained on historical NBA player statistics including: |
| | - Regular season performance data |
| | - Multiple seasons for trend analysis |
| | - Various player positions and team contexts |
| |
|
| | ## Ethical Considerations |
| |
|
| | This model is for educational and analytical purposes only. It should not be used for: |
| | - Player salary negotiations without additional context |
| | - Draft decisions as the sole determining factor |
| | - Any form of discrimination or bias in player evaluation |
| |
|
| | ## Contact & Feedback |
| |
|
| | Feel free to provide feedback or suggestions for improvements. This is an educational project demonstrating machine learning applications in sports analytics. |
| |
|
| | --- |
| |
|
| | **Live Demo**: Try the interactive interface above! |
| | **Status**: Currently running in fallback mode (simplified predictions) |
| | **Next Update**: Full XGBoost model integration for enhanced accuracy |
