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Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach

2025Journal article Open access
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datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorAfonso, Pedro
dc.contributor.authorSchneider, André
dc.contributor.authorBranquinho, Luís
dc.contributor.authorMaio, Eduardo
dc.contributor.authorFerraz, Ricardo
dc.contributor.authorNascimento, Rafael
dc.contributor.authorMorgans, Ryland
dc.contributor.authorBarbosa, Tiago M.
dc.contributor.authorMonteiro, António M.
dc.contributor.authorForte, Pedro
dc.date.accessioned2025-06-11T11:18:46Z
dc.date.available2025-06-11T11:18:46Z
dc.date.issued2025
dc.description.abstractOptimizing recovery is crucial for maintaining performance and reducing fatigue and injury risk in youth football players. This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological features. Over six weeks, training loads were monitored via GPS, psychophysiological scales, and heart rate sensors, analyzing variables such as total distance, high-speed running, recovery state, and perceived exertion. The data preparation process involved managing absent values, applying normalization techniques, and selecting relevant features. A total of five ML models were evaluated: K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). XGBoost, RF, and DT achieved high accuracy, while KNN underperformed. Using a correlation matrix, average speed (AvS) was the only variable significantly correlated with the rating of perceived exertion (RPE) (r = 0.142; p = 0.010). After dimensionality reduction, ML models were re-evaluated, with RF and DT performing best, followed by XGBoost and SVM. These findings confirm that tracking and wearable-derived data are effectively useful for predicting RPE, providing valuable insights for workload management and personalized recovery strategies. Future research should integrate psychological and interpersonal factors to enhance predictive modeling in the individual long-term health and performance of young football players.eng
dc.description.sponsorshipThis research received no external funding.
dc.identifier.citationTeixeira, José Eduardo; Afonso, Pedro; Schneider, André; Branquinho, Luís; Maio, Eduardo; Ferraz, Ricardo; Nascimento, Rafael; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, António M.; Forte, Pedro (2025) Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach. Applied Sciences. ISSN 2076-3417. 15:7, p. 1-15
dc.identifier.doi10.3390/app15073718
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10198/34575
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.ispartofApplied Sciences
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectYouth
dc.subjectMonitoring
dc.subjectTechnology
dc.subjectAI
dc.subjectPsychophysiology
dc.titlePlayer Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approacheng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage15
oaire.citation.issue7
oaire.citation.startPage1
oaire.citation.titleApplied Sciences
oaire.citation.volume15
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameTeixeira
person.familyNameBarbosa
person.familyNameMonteiro
person.familyNameForte
person.givenNameJosé Eduardo
person.givenNameTiago M.
person.givenNameAntónio M.
person.givenNamePedro
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-id8B11-BDC4-F6FF
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0001-7071-2116
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0003-0184-6780
person.identifier.scopus-author-id10044856400
relation.isAuthorOfPublication79042f92-ce53-4b79-a33f-76ac63c55b8d
relation.isAuthorOfPublication941a6f14-cfba-458a-a2e3-0cbd1846cd42
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relation.isAuthorOfPublication.latestForDiscovery79042f92-ce53-4b79-a33f-76ac63c55b8d

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