Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics

• URL: http://arxiv.org/abs/2603.04874v1
• Date: Thu, 05 Mar 2026 07:04:35 GMT
• Title: Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics
• Authors: Jerrin Bright, Michelle Lu, John Zelek,
• Abstract summary: We classify eight pitch types from monocular 3D pose sequences without access to ball-flight data.<n>Our pipeline chains a diffusion-based 3D pose backbone with automatic pitching-event detection.<n>We achieve 80.4% accuracy using body kinematics alone on 119,561 professional pitches.
• Score: 3.1224081969539714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How much can a pitcher's body reveal about the upcoming pitch? We study this question at scale by classifying eight pitch types from monocular 3D pose sequences, without access to ball-flight data. Our pipeline chains a diffusion-based 3D pose backbone with automatic pitching-event detection, groundtruth-validated biomechanical feature extraction, and gradient-boosted classification over 229 kinematic features. Evaluated on 119,561 professional pitches, the largest such benchmark to date, we achieve 80.4\% accuracy using body kinematics alone. A systematic importance analysis reveals that upper-body mechanics contribute 64.9\% of the predictive signal versus 35.1\% for the lower body, with wrist position (14.8\%) and trunk lateral tilt emerging as the most informative joint group and biomechanical feature, respectively. We further show that grip-defined variants (four-seam vs.\ two-seam fastball) are not separable from pose, establishing an empirical ceiling near 80\% and delineating where kinematic information ends and ball-flight information begins.

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