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- W4288366169 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." @default.
- W4288366169 created "2022-07-29" @default.
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- W4288366169 date "2019-04-18" @default.
- W4288366169 modified "2023-09-29" @default.
- W4288366169 title "Early Detection of Injuries in MLB Pitchers from Video" @default.
- W4288366169 doi "https://doi.org/10.48550/arxiv.1904.08916" @default.
- W4288366169 hasPublicationYear "2019" @default.
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