Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/96924
DC Field | Value | Language |
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dc.contributor.author | Mandorino, M. | - |
dc.contributor.author | Figueiredo, A. J. | - |
dc.contributor.author | Cima, G. | - |
dc.contributor.author | Tessitore, A. | - |
dc.date.accessioned | 2022-01-07T11:10:41Z | - |
dc.date.available | 2022-01-07T11:10:41Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1684-4769 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/96924 | - |
dc.description.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. © 2021 M. Mandorino et al., published by Sciendo. | pt |
dc.language.iso | eng | pt |
dc.publisher | International Association of Computer Science in Sport | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt |
dc.subject | Data Mining | pt |
dc.subject | Injury | pt |
dc.subject | Prediction | pt |
dc.subject | Training load | pt |
dc.subject | Youth soccer | pt |
dc.title | A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players | pt |
dc.type | article | - |
degois.publication.firstPage | 147 | pt |
degois.publication.lastPage | 163 | pt |
degois.publication.issue | 2 | pt |
degois.publication.title | International Journal of Computer Science in Sport | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.2478/ijcss-2021-0009 | pt |
degois.publication.volume | 20 | pt |
dc.date.embargo | 2021-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
crisitem.author.researchunit | CIDAF - Research Unit for Sport and Physical Activity | - |
crisitem.author.orcid | 0000-0001-6956-0514 | - |
Appears in Collections: | FCDEF - Artigos em Revistas Internacionais I&D CIDAF - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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10.2478_ijcss-2021-0009.pdf | 1.65 MB | Adobe PDF | View/Open |
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