# Early Detection of Injuries in MLB Pitchers from Video

## TL;DR

This paper explores using convolutional neural networks to detect and predict injuries in MLB pitchers from video data, aiming to reduce injury-related costs and improve player health management.

## Contribution

It introduces a novel approach applying CNNs to MLB pitcher injury detection and prediction solely from video footage, with comprehensive experimental evaluation.

## Key findings

- Model can detect injuries from video data
- Performance varies across pitchers and injury types
- Early prediction of injuries is feasible

## Abstract

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.