<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Department of Movement, Human and Health Sciences

Research Center for Sport and Physical Activity, Faculty of Sport Science and Physical Education

Computer, Control and Management Engineering Department

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players"}

Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between...

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Pubblicato online: 28 nov 2021

Pagine: 147 - 163

DOI: https://doi.org/10.2478/ijcss-2021-0009

Parole chiave
INJURY, YOUTH SOCCER, DATA MINING, PREDICTION, TRAINING LOAD

© 2021 M. Mandorino et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Pubblicato online: 28 nov 2021

Pagine: 147 - 163

DOI: https://doi.org/10.2478/ijcss-2021-0009

Parole chiaveINJURY, YOUTH SOCCER, DATA MINING, PREDICTION, TRAINING LOAD

© 2021 M. Mandorino et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
INJURY, YOUTH SOCCER, DATA MINING, PREDICTION, TRAINING LOAD