Key result
Multidimensional random forest model accurately predicts overtraining risk in youth soccer players with 0.94 AUC.
Why the study?
Overtraining syndrome is a critical challenge in youth soccer during rapid physiological maturation and high training demands, prompting the development of a multidimensional prediction model.
Does a multidimensional prediction model accurately predict overtraining risk in youth soccer players?
Cohort (n=120)
Yes
Does a multidimensional prediction model accurately predict overtraining risk in youth soccer players?
A machine learning model integrating physiological and psychological markers accurately predicts overtraining risk in elite youth soccer players.
Answers are grounded in this paper and cite their sources.
May aid early overtraining detection in youth soccer; hypothesis-generating pending prospective validation.
Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods of rapid physiological maturation combined with high training demands. This study aimed to develop and validate a multidimensional prediction model for overtraining risk in youth soccer players by integrating physiological, psychological, and performance parameters through advanced machine learning. A longitudinal study tracked 120 male youth players (aged 12-18) from six elite South Korean academies over one competitive season (August 2023-May 2024). Data included bi-weekly blood sampling (testosterone, cortisol, creatine kinase, IL-6, TNF-α), weekly psychological assessments (RESTQ-Sport, POMS), continuous GPS-based training load monitoring, and monthly performance tests. A random forest model with SMOTE to address class imbalance achieved an AUC-ROC of 0.94 (internal validation), with sensitivity and specificity of 0.87 and 0.92, respectively. Key predictors included testosterone-to-cortisol ratio (0.89), RESTQ-Sport balance (0.83), and acute:chronic workload ratio (0.78). A simplified, non-invasive model excluding blood markers achieved an AUC-ROC of 0.89. A three-tier risk stratification system identified 85% of high-risk cases a week before performance declined. These findings underscore the model's superior predictive power and practical utility, offering a foundation for evidence-based, proactive overtraining risk management in elite youth soccer development.
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Qian et al. (2025) conducted a cohort in Overtraining syndrome (n=120). Multidimensional prediction model was evaluated on Prediction of overtraining risk (AUC-ROC 0.94). A multidimensional random forest model integrating physiological, psychological, and performance parameters accurately predicted overtraining risk in youth soccer players (AUC-ROC 0.94).
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