Recurrent Neural Networks model for injury prevention within a professional rugby union club: a proof of concept over one season

Abstract

BACKGROUND: In professional rugby, injury prevention and player availability are major challenges. Sports analytics use data from trainings and matches to address these issues. This study leveraged comprehensive daily data from a professional rugby club to predict players' readiness for training. Using this metric helped assess its effectiveness in predicting intrinsic injuries and improving injury prevention strategies. METHODS: Models including logistic regression, decision trees, and Long Short-Term Memory-based neural networks, were evaluated for their predictive accuracy and ability to discern patterns indicative of injury risks or readiness for physical activities. FINDINGS: The study demonstrated that long-short term memory and convolutional one-dimension models outperform traditional machine learning methods in analyzing players' physical conditions. This approach may support earlier identification of injury risks and inform workload management. Using model evaluation and interpretability techniques, including Local Interpretable Model-Agnostic Explanations (LIME) module, the study provided a framework for sports scientists, coaches, and medical staff to mitigate injury risks and optimize training sessions. INTERPRETATION: As a preliminary exploration, this study paves the way for further research into the integration of machine learning and neural networks in sports science, promising transformative impacts on injury prevention strategies in rugby.

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