Abstract
Injuries pose a significant challenge in professional football, affecting player availability, team performance, and club finances. Accurate prediction of injury risk is crucial for implementing effective prevention strategies. This study develops a deep learning model to predict the likelihood of injury in professional football players using data collected through Catapult Sports tracking devices. The research is carried out in collaboration with KKS Lech Poznań, a Polish professional football club. The proposed model architecture combines bidirectional long- and short-term memory (BiLSTM) networks with an attention mechanism to learn from the time series data and predict player injury risk. The model is trained on sequences of Catapult data spanning 14 days before each recorded injury or non-injury event. To address class imbalance, a custom loss function was implemented that balances focal loss and the F_beta score. The model's performance is evaluated on an independent test set, achieving a specificity of 0.90, an accuracy of 0.90, and a recall of 0.40.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.67
Leveraging GPS Data and Attention-based BiLSTM for Injury Prediction in Professional Football
Injuries pose a significant challenge in professional football, affecting player availability, team performance, and club finances. Accurate prediction of injury risk is crucial for implementing effective prevention strategies. This study develops a deep learning model to predict the likelihood of injury in professional football players using data collected through Catapult Sports tracking devices. The research is carried out in collaboration with KKS Lech Poznań, a Polish professional football club. The proposed model architecture combines bidirectional long- and short-term memory (BiLSTM) networks with an attention mechanism to learn from the time series data and predict player injury risk. The model is trained on sequences of Catapult data spanning 14 days before each recorded injury or non-injury event. To address class imbalance, a custom loss function was implemented that balances focal loss and the F_beta score. The model's performance is evaluated on an independent test set, achieving a specificity of 0.90, an accuracy of 0.90, and a recall of 0.40.
Recommended Citation
Sadurska, A., Zaręba, M., Piłka, T. & Górecki, T. (2025). Leveraging GPS Data and Attention-based BiLSTM for Injury Prediction in Professional FootballIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.67