Notebooks¶
Original exploratory Jupyter notebooks are preserved in notebooks/ and can be run via Jupyter or Google Colab. They now import from the pitch_sequencing package.
Available Notebooks¶
1. Baseball Pitch Sequence Simulator¶
File: notebooks/Baseball_Pitch_Sequence_Simulator.ipynb
Demonstrates the synthetic data generation pipeline, including pitcher archetypes, sequence strategies, count-dependent outcomes, fatigue modeling, and game context. Generates both the main pitch dataset and HMM training sequences.
2. HMM Pitch Predictor¶
File: notebooks/HMM_Pitch_Predictor.ipynb
Trains a Hidden Markov Model on synthetic pitch sequences. Sweeps the number of hidden states (1-8) and evaluates prediction accuracy. Shows transition matrices and emission probabilities.
3. AutoGluon Baseball Pitch Prediction¶
File: notebooks/AutoGluon_Baseball_Pitch_Prediction.ipynb
Uses AutoGluon's TabularPredictor to predict the next pitch type from tabular features. Demonstrates automated model selection and ensembling.
4. AutoGluon Baseball Pitch Outcome Prediction¶
File: notebooks/AutoGluon_Baseball_Pitch_Outcome_Prediction.ipynb
Predicts pitch outcomes (ball, strike, hit) rather than pitch types. Uses the same AutoGluon approach with outcome-specific features.
5. LSTM Pitch Predictor¶
File: notebooks/LSTM_Pitch_Predictor.ipynb
Trains a 2-layer LSTM on windowed pitch sequences. Includes data preprocessing, sequence creation, model training with early stopping, and evaluation with confusion matrices.
Running Notebooks¶
Local Jupyter¶
Google Colab¶
Upload any notebook to Google Colab and add this cell at the top: