Early Detection of Injuries in MLB Pitchers from Video
This addresses the costly issue of player injuries in professional baseball, though it is incremental as it applies existing video recognition methods to a new sports domain.
The paper tackled the problem of detecting and predicting injuries in MLB pitchers from video data, finding that convolutional neural networks can achieve this with varying performance across individual pitchers, injury types, and prediction timelines.
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.