Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics
#baseball #pitch anticipation #3D kinematics #interpretable AI #broadcast analysis #sports technology #pre-release prediction
📌 Key Takeaways
- Researchers developed a method to predict baseball pitch types before release using broadcast 3D kinematics.
- The approach focuses on interpretability, allowing insights into which kinematic features influence predictions.
- It utilizes pre-release data from broadcast footage, enhancing real-time anticipation for viewers and analysts.
- The model aims to improve fan engagement and strategic analysis by providing early, understandable pitch insights.
📖 Full Retelling
arXiv:2603.04874v1 Announce Type: cross
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
🏷️ Themes
Sports Analytics, Machine Learning
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--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04874 [Submitted on 5 Mar 2026] Title: Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics Authors: Jerrin Bright , Michelle Lu , John Zelek View a PDF of the paper titled Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics, by Jerrin Bright and 2 other authors View PDF HTML 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. Comments: Submitted to CVPRW'26 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.04874 [cs.CV] (or arXiv:2603.04874v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.04874 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jerrin Bright [ view email ] [v1] Thu, 5 Mar 2026 07:04:35 UTC (1,179 KB) Full-text links: Access Paper: View a PDF of the pape...
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