Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. However, few studies adopt more advanced techniques such as deep learning to conduct these analyses. We aimed to fill this gap by developing a multi-output deep neural network to predict final pitch location using ball tracking release metrics and contextual ball flight information (i.e., projectile motion predictions) from over 2 million pitches thrown during the National Collegiate Athletic Association Division I games. Predictions from the deep neural network were compared to previously reported machine learning models, and permutation-based feature importance was used to investigate the most important features for predicting pitch location. Euclidean distance errors with the deep neural network were approximately 15 cm, outperforming linear regression models by 33% (6 cm). A post hoc analysis revealed that a deep neural network trained without projectile motion predictions performed 17% (2.8 cm) worse than the optimal model, suggesting the context helped the model learn the underlying physics principles that govern ball flight. Moreover, the most important ball tracking metrics for predicting pitch location were lateral release position and spin rate, which are under direct control of the pitcher and have been tied to performance and injury outcomes. Thus, this model provides an enhanced framework to analyze pitcher performance, and future applications may use additional context to predict other performance metrics from ball tracking data, such as throwing arm biomechanics.
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