Zero-Sum Baseball

This project demonstrates how a zero-sum stochastic game model of baseball can be used to approach more complex baseball problems. We explore some strategies for optimizing batter lineups and achieve some interesting results.

Read the write-up here for the full details.

Getting Started

1. Install the requirements with pip install -r requirements.txt
   • Or manually install PyTorch, CVXPY, Pandas, Matplotlib, blosc2, and tqdm
2. Fetch the raw data with raw_data/fetch_data.py
3. Process the data with src/data/data_loading.py
4. Try out the zero-sum stochastic game model with src/policy/optimal_policy.py
5. Try the batting lineup optimization scripts with src/policy/batting_order_optimization.py
6. Check out some visualizations with src/statistics.ipynb and src/policy/batting_order.ipnyb
7. Feel free to load the data with bd = BaseballData() and experiment!

Project Structure

• model_weights/ contains pre-trained models for the distributions
• presentation/ contains the research poster and write-up
• src/ contains the made codebase
  • src/data/ contains the data processing scripts and Pytorch datasets
  • src/distributions/ contains the Pytorch models for learning the distributions
  • src/model/ contains the object classes for the game model, like players, zones, pitches, etc.
  • src/policy/ contains the zero-sum stochastic game model and work on batting lineup optimization