1. bookVolume 20 (2021): Issue 2 (December 2021)
Journal Details
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Journal
eISSN
1684-4769
First Published
16 Apr 2016
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2 times per year
Languages
English
access type Open Access

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

Published Online: 28 Nov 2021
Volume & Issue: Volume 20 (2021) - Issue 2 (December 2021)
Page range: 147 - 163
Journal Details
License
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English
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.

Keywords

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