Key result
Comprehensive SVM monitoring detects ~96% of injury data points but yields many false positives.
Why the study?
Monitoring tools providing early indication of injury and illness could improve player protection, prompting evaluation of the feasibility of a comprehensive monitoring approach and identification of associated variables.
Does a comprehensive monitoring approach using machine learning predict illness and injury in elite youth soccer players?
Observational (n=25)
Does a comprehensive monitoring approach using machine learning predict illness and injury in elite youth soccer players?
A comprehensive monitoring approach using machine learning is feasible in elite youth soccer players but currently yields low precision for predicting injuries and illnesses, leading to false positives.
Low precision and high false positives limit clinical use in youth soccer; leaves open whether refined ML models can improve injury prediction.
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, VO2max: 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.
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Haller et al. (2023) conducted an observational in Elite youth soccer players (injury and illness prediction) (n=25). Comprehensive monitoring approach with machine learning (linear SVM) was evaluated on Feasibility, adherence, and prediction of illness and injury. A comprehensive monitoring approach using a linear support vector machine correctly detected 96.3% of all data points for injury prediction, though the model showed low precision with many false-positives.
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