Predicting athlete injury risk has been a holy grail in sports medicine with little progress to date due to a variety of factors such as small sample sizes, significantly imbalanced data, and inadequate statistical approaches. Modeling approaches which are not able to account for the multiple interactions across factors can be misleading. We address the small sample size by collecting longitudinal data of NBA player injuries using publicly available data sources and develop a state of the art deep learning model, METIC, to predict future injuries based on past injuries, game activity, and player statistics. We evaluate model performance using metrics appropriate for imbalanced data and find that METIC performs significantly better than other traditional machine learning approaches. METIC uses feature learning to create interactive features which become meaningful in combination with each other. METIC can be used by practitioners and front offices to improve athlete management and reduce injury incidence, potentially saving sports teams millions in revenue due to reduced athlete injuries.
The transfer of training of the hang power clean to skating starts in men's ice hockey was evaluated applying Verkhoshansky and Siff's proposed criteria of dynamic correspondence to the existing research. A relatively high degree of dynamic correspondence was evidenced throughout the criteria, although with a consideration of the sagittal plane alone. Based on this evaluation, the use of the hang power clean in training increases ice hockey players' capacity to express their skill through an improvement in peak force output and an increase in rate of force development in lower limb extension and is therefore recommended to practitioners training ice hockey players.
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