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https://hdl.handle.net/10316/96924
Título: | A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players | Autor: | Mandorino, M. Figueiredo, A. J. Cima, G. Tessitore, A. |
Palavras-chave: | Data Mining; Injury; Prediction; Training load; Youth soccer | Data: | 2021 | Editora: | International Association of Computer Science in Sport | Título da revista, periódico, livro ou evento: | International Journal of Computer Science in Sport | Volume: | 20 | Número: | 2 | Resumo: | 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. © 2021 M. Mandorino et al., published by Sciendo. | URI: | https://hdl.handle.net/10316/96924 | ISSN: | 1684-4769 | DOI: | 10.2478/ijcss-2021-0009 | Direitos: | openAccess |
Aparece nas coleções: | FCDEF - Artigos em Revistas Internacionais I&D CIDAF - Artigos em Revistas Internacionais |
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Ficheiro | Descrição | Tamanho | Formato | |
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10.2478_ijcss-2021-0009.pdf | 1.65 MB | Adobe PDF | Ver/Abrir |
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