Forecasting soccer injuries by combining screening, monitoring and machine learning - Hecksteden, A.; Schmartz, G. P.; Egyptien, Y.; aus der Fünten, K.; Keller, A.; Meyer, T. (27th Annual Congress of the European College of Sport Science (ECSS), Sevilla, 30. Aug - 2. Sep 2022, 2022)
INTRODUCTION: Acute, exercise related injuries and their negative consequences for player health and team performance are an important issue in football. Identifying players or circumstances associated with an increased risk of injury is fundamental for successful risk management. So far, a considerable number of risk factors have been identified. However, univariable predictive accuracy is low and from the few multivariable prediction models none has been scientifically verified outside the original setting. Of note, time-constant (screening) and volatile (monitoring) risk factors are so far generally considered separately resulting in a restricted set of explanatory variables. Consequently, improvements in predictive accuracy may be expected when screening- and monitoring-data are combined, especially when analysed with current machine learning (ML) techniques able to account for interactions within a "web of determinants". Expectably, screening parameters will set a personal level for injury risk while monitoring parameters account for short term variations around it.