Key result
A classification tree model predicted non-contact injuries in young soccer players with acceptable discrimination (AUC=0.76), identifying recovery status and workload as key risk factors.
Why the study?
Predicting and avoiding injury is a challenging task, motivating the use of data mining to identify relationships between modifiable and non-modifiable risk factors to predict non-contact injuries.
Cohort (n=23)
No
A data mining approach using a classification tree model can predict non-contact injuries in young soccer players with acceptable accuracy (AUC=0.76) based on recovery status, training load, and maturity offset.
May aid risk stratification via workload monitoring in youth soccer; hypothesis-generating and requires prospective validation before practice change.
Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.
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Mandorino et al. (2021) conducted a cohort in Non-contact injuries (n=23). Classification and regression tree (CART) model was evaluated on Prediction of non-contact injuries (Area Under the Curve). A classification tree model predicted non-contact injuries in young soccer players with acceptable discrimination (AUC=0.76), identifying recovery status and workload as key risk factors.
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