仅凭投手身体动作，就能提前预测棒球投球类型，准确率达80.4%。
Interpretable Pre-Release Baseball Pitch Type Anticipation from Broadcast 3D Kinematics
- 用扩散模型从单目视频提取3D身体姿态，自动检测投球事件
- 上半身贡献64.9%预测信号，手腕位置和躯干侧倾最关键
- 揭示了身体动作与球路差异的界限，适合体育分析与计算机视觉研究
投手的身体能透露多少即将投出的球种信息？我们通过分析229,561次职业比赛中的单目3D姿态序列，在无球飞行数据条件下对八类投球进行分类。该方法链式整合了基于扩散的3D姿态主干网络、自动投球事件检测、经真值验证的生物力学特征提取，以及基于梯度提升的229个运动学特征分类。在迄今为止最大的同类基准上，仅使用身体动作即达到80.4%的准确率。系统性重要性分析表明，上半身贡献64.9%的预测信号，下半身占35.1%，其中手腕位置（14.8%）和躯干侧倾为最有效的关节组与生物力学特征。此外，我们证明握法差异（如四缝球与二缝球）无法通过姿态区分，确立了近80%的性能上限，明确了运动学信息与球飞行信息的边界。
原文摘要 · Abstract (English)
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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