From Pose to Pitch: Classifying Baseball Pitch Types with Projection-Gated ST-GCN

We present a skeleton-based approach to baseball pitch type classification from broadcast video. We leverage Human Pose Estimation and an ST-GCN architecture, improved with a projection-gated temporal downsampler, to learn kinematic signatures of the pitcher’s body, adaptively selecting the most informative frames, enabling pitch type classification without the need for ball tracking. On the MLB-YouTube dataset, our proposed method reaches ~62% six-class accuracy, highlighting body mechanics as a practical biometric cue.