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

arXiv: https://arxiv.org/abs/2603.04874 · HTML: https://mlsanity.com/paper/2603.04874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [ViTPose++: Vision Transformer for Generic Body Pose Estimation](https://mlsanity.com/paper/2212.04246/) (2022)
- [Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery](https://mlsanity.com/paper/2403.09063/) (2024)
- [PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics](https://mlsanity.com/paper/2405.07407/) (2024)
- [DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.09502/) (2025)

## Abstract (arXiv)

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.