Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months.

Autor: Haller N; Department of Sport and Exercise Science, University of Salzburg, Salzburg, Austria.; Department of Sports Medicine, Rehabilitation and Disease Prevention, University of Mainz, Mainz, Germany., Kranzinger S; Salzburg Research Forschungsgesellschaft m.b.H, Salzburg, Austria., Kranzinger C; Salzburg Research Forschungsgesellschaft m.b.H, Salzburg, Austria., Blumkaitis JC; Department of Sport and Exercise Science, University of Salzburg, Salzburg, Austria., Strepp T; Department of Sport and Exercise Science, University of Salzburg, Salzburg, Austria., Simon P; Department of Sports Medicine, Rehabilitation and Disease Prevention, University of Mainz, Mainz, Germany., Tomaskovic A; Department of Sports Medicine, Rehabilitation and Disease Prevention, University of Mainz, Mainz, Germany., O'Brien J; Red Bull Athlete Performance Center, Salzburg, Austria., Düring M; Red Bull Athlete Performance Center, Salzburg, Austria., Stöggl T; Department of Sport and Exercise Science, University of Salzburg, Salzburg, Austria.; Red Bull Athlete Performance Center, Salzburg, Austria.
Jazyk: angličtina
Zdroj: Journal of sports science & medicine [J Sports Sci Med] 2023 Sep 01; Vol. 22 (3), pp. 476-487. Date of Electronic Publication: 2023 Sep 01 (Print Publication: 2023).
DOI: 10.52082/jssm.2023.476
Abstrakt: The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO 2max : 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.
(© Journal of Sports Science and Medicine.)
Databáze: MEDLINE
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