Leveraging Machine Learning to Enhance Injury Prevention Strategies for Fast Bowlers
DOI:
https://doi.org/10.48001/978-81-966500-0-1-9Keywords:
Random Forest, Injury Prediction, Model Accuracy, Machine Learning, AIAbstract
Fast bowlers in cricket face a high risk of injury due to the immense physical strain associated with their role, often resulting in prolonged absences and performance declines. This study aims to develop a predictive model for fast bowler injuries using the Random Forest algorithm. Key parameters such as workload, biomechanics, fitness levels, injury history, and the critical factor of the last ball bowled before injury were analyzed to detect patterns linked to injury. The Random Forest model was applied, leveraging these variables to provide high predictive accuracy. Model performance was evaluated demonstrating the efficacy of this approach in predicting injuries before they occur. The results highlight the significance of precise workload management and the critical moments leading up to injury, offering valuable insights for coaching staff and medical teams.
Downloads
References
Amendolara, A., Pfister, D., Settelmayer, M., Shah, M., Wu, V., Donnelly, S., Johnston, B., Peterson, R., Sant, D., Kriak, J., & Bills, K. (2023). An Overview of Machine Learning Applications in Sports Injury Prediction. Cureus. https://doi.org/10.7759/cureus.46170
Asif, M., & McHale, I. G. (2016). In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model. International Journal of Forecasting, 32(1), 34–43. https://doi.org/10.1016/j.ijforecast.2015.02.005
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Dennis, R., Farhart, R., Goumas, C., & Orchard, J. (2003). Bowling workload and the risk of injury in elite cricket fast bowlers. Journal of Science and Medicine in Sport, 6(3), 359–367. https://doi.org/10.1016/S1440-2440(03)80031-2
Hickey, J., Shield, A. J., Williams, M. D., & Opar, D. A. (2014). The financial cost of hamstring strain injuries in the Australian Football League. British Journal of Sports Medicine, 48(8), 729–730. https://doi.org/10.1136/bjsports-2013-092884
Huxley, D. J., O’Connor, D., & Healey, P. A. (2014). An examination of the training profiles and injuries in elite youth track and field athletes. European Journal of Sport Science, 14(2), 185–192. https://doi.org/10.1080/17461391.2013.809153
Leddy, C., Bolger, R., Byrne, P. J., Kinsella, S., & Zambrano, L. (2024). The application of Machine and Deep Learning for technique and skill analysis in swing and team sport-specific movement: A systematic review. International Journal of Computer Science in Sport, 23(1), 110–145. https://doi.org/10.2478/ijcss-2024-0007
Orchard, J. W., Kountouris, A., & Sims, K. (2017). Risk factors for hamstring injuries in Australian male professional cricket players. Journal of Sport and Health Science, 6(3), 271–274. https://doi.org/10.1016/j.jshs.2017.05.004
Rommers, N., Rössler, R., Verhagen, E., Vandecasteele, F., Verstockt, S., Vaeyens, R., Lenoir, M., D’Hondt, E., & Witvrouw, E. (2020). A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players. Medicine and Science in Sports and Exercise, 52(8), 1745–1751. https://doi.org/10.1249/MSS.0000000000002305
