Sports analytics · machine learning

Pitch Classifier

Completed 0% Vibes

Neural network workflow for classifying MLB pitch types from 2024 Statcast data, with separate models for left- and right-handed pitchers.

I wrote it. AI was only used for research support.

Left-handed pitchers 94.67% best validation accuracy
Right-handed pitchers 92.78% best validation accuracy
30-day Statcast pulls 18 engineered inputs Separate LHP / RHP models
Context INSY 7130 · Auburn · 2024
Best validation 94.67% LHP · 92.78% RHP
Feature set 18 release, movement, trajectory inputs

A baseball analytics project that downloads Statcast pitch data, tunes a feed-forward neural network, and trains separate classifiers for left- and right-handed pitchers using release, movement, and trajectory features.

  • Python
  • TensorFlow
  • Keras
  • KerasTuner
  • scikit-learn
  • pandas
  • pybaseball
  • matplotlib
More detail
  • Downloads 2024 MLB Statcast pitch-level data in 30-day chunks.
  • Tunes the model with Hyperband before final training.
  • Trains separate classifiers for left-handed and right-handed pitchers.
  • Includes plots, saved histories, model artifacts, and the full report.
Training progress plot for Pitch Classifier. Left-handed pitcher model accuracy plot. Right-handed pitcher model accuracy plot.

Repository Project report

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View Project See on GitHub

Vibe Score tracks how much of the build was delegated.

0% means I wrote it.

100% means I drove and tested while the agent did the typing.