# Early Detection of Injuries in MLB Pitchers from Video

arXiv: https://arxiv.org/abs/1904.08916 · HTML: https://mlsanity.com/paper/1904.08916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Puck localization and multi-task event recognition in broadcast hockey videos](https://mlsanity.com/paper/2105.10563/) (2021)
- [AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability](https://mlsanity.com/paper/2507.12905/) (2025)
- [Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video](https://mlsanity.com/paper/2603.04864/) (2026)

## Abstract (arXiv)

Injuries are a major cost in sports. Teams spend millions of dollars every year on players who are hurt and unable to play, resulting in lost games, decreased fan interest and additional wages for replacement players. Modern convolutional neural networks have been successfully applied to many video recognition tasks. In this paper, we introduce the problem of injury detection/prediction in MLB pitchers and experimentally evaluate the ability of such convolutional models to detect and predict injuries in pitches only from video data. We conduct experiments on a large dataset of TV broadcast MLB videos of 20 different pitchers who were injured during the 2017 season. We experimentally evaluate the model's performance on each individual pitcher, how well it generalizes to new pitchers, how it performs for various injuries, and how early it can predict or detect an injury.