  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.
