A Context-Enhanced Deep Learning Approach to Predict Baseball Pitch Location from Ball Tracking Release Metrics
Research Square
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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 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 (DNN) to predict final pitch location using ball tracking release metrics and contextual ball flight information (i.e., naïve projectile motion estimates) from over two million pitches thrown during National Collegiate Athletic Association Division I games. Predictions from the DNN were compared to predictions made by 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 DNN were approximately 15 centimeters, outperforming linear regression models by 33% (6 centimeters). A post-hoc analysis revealed that a DNN trained without the projectile motion features performed 17% (2.8 centimeters) worse than the optimal model, suggesting the added context helped the model learn 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 have been tied to performance and injury outcomes in elite pitchers. 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.
Bibliographic Details
Springer Science and Business Media LLC
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