nba-stats-predictor

NBA Stats Predictor

A deep learning tool that predicts NBA player statistics using historical data. The model analyzes player career trajectories to forecast future performance on a season-by-season basis.

🏀 Features

• Data Collection: Automated scraping of NBA player statistics directly from the NBA
• Data Processing: Comprehensive preprocessing including normalization and variable-length sequence preparation
• Model Training: LSTM-based deep learning model for time-series prediction
• Statistics Prediction: Forecasts key player statistics for upcoming seasons
• Model Persistence: Save and load trained models for future use

Project Structure

nba-stats-predictor/
├── data/                           # Generated data files
│   ├── nba_player_stats_*.csv     # Historical player statistics
│   ├── predicted_*_season.csv     # Model predictions
│   └── ...                        
├── scraper.ipynb                  # Web scraping notebook
├── trainer.ipynb                  # Model training notebook
├── guesser.ipynb                  # Prediction interface
├── nba_stats_predictor_model.keras # Saved model (generated)
└── normalization_params.json      # Model parameters (generated)

How It Works

1. Data Collection (scraper.ipynb):
   • Scrapes historical NBA player statistics
   • Processes and cleans the raw data
   • Saves data to CSV format
2. Model Training (trainer.ipynb):
   • Preprocesses data for deep learning
   • Saves preprocessed data to CSV format
   • Implements and trains LSTM model
   • Saves trained model and normalization parameters
3. Predictions (guesser.ipynb):
   • Loads trained model and parameters
   • Makes predictions for future seasons
   • Exports predictions to CSV files

Getting Started

1. Clone the repository:

   git clone https://github.com/srhn45/nba-stats-predictor.git
   cd nba-stats-predictor
   

2. Install required packages:

   pip install tensorflow pandas numpy sklearn
   

3. Run notebooks in order:
   • First run scraper.ipynb to collect data
   • Then trainer.ipynb to train the model

   • Finally guesser.ipynb to make predictions

   • Can run guesser.ipynb directly with the provided data if not looking to make changes

Generated Files

The following files are generated through the notebooks:

• data/nba_player_stats_*.csv: Contains scraped NBA statistics
• data/predicted_*_season.csv: Contains model predictions
• nba_stats_predictor_model.keras: Trained model file
• normalization_params.json: Model normalization parameters

Note: All generated files can be recreated by running the notebooks in sequence.

Future Improvements and Additions

• Further optimize the base model
• Add player similarity analysis
• Implement confidence intervals for predictions
• Add injury history consideration

• Expand to team-level predictions

• Develop Fantasy Draft AI Bot using predictions
  • Implement reinforcement learning for optimal draft strategies
  • Consider league-specific scoring systems
  • Account for position scarcity and roster construction
  • Optimize for season-long performance using predicted statistics
  • Adapt strategy based on draft position

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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