Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video

• URL: http://arxiv.org/abs/2603.04864v1
• Date: Thu, 05 Mar 2026 06:38:32 GMT
• Title: Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video
• Authors: Jerrin Bright, Justin Mende, John Zelek,
• Abstract summary: We present a monocular video pipeline that recovers 18 clinically relevant biomechanics metrics from broadcast footage.<n>Built on DreamPose3D, our approach introduces a drift-controlled global lifting module that recovers pelvis trajectory.<n>Using these metrics for injury prediction, an automated screening model achieves AUC 0.811 for Tommy John surgery and 0.825 for significant arm injuries on 7,348 pitchers.
• Score: 2.994962964425238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Injury prediction in pitching depends on precise biomechanical signals, yet gold-standard measurements come from expensive, stadium-installed multi-camera systems that are unavailable outside professional venues. We present a monocular video pipeline that recovers 18 clinically relevant biomechanics metrics from broadcast footage, positioning pose-derived kinematics as a scalable source for injury-risk modeling. Built on DreamPose3D, our approach introduces a drift-controlled global lifting module that recovers pelvis trajectory via velocity-based parameterization and sliding-window inference, lifting pelvis-rooted poses into global space. To address motion blur, compression artifacts, and extreme pitching poses, we incorporate a kinematics refinement pipeline with bone-length constraints, joint-limited inverse kinematics, smoothing, and symmetry constraints to ensure temporally stable and physically plausible kinematics. On 13 professional pitchers (156 paired pitches), 16/18 metrics achieve sub-degree agreement (MAE $< 1^{\circ}$). Using these metrics for injury prediction, an automated screening model achieves AUC 0.811 for Tommy John surgery and 0.825 for significant arm injuries on 7,348 pitchers. The resulting pose-derived metrics support scalable injury-risk screening, establishing monocular broadcast video as a viable alternative to stadium-scale motion capture for biomechanics.

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