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Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach

2024Journal article Open access
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dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorEncarnação, Samuel
dc.contributor.authorBranquinho, Luís
dc.contributor.authorFerraz, Ricardo
dc.contributor.authorPortella, Daniel Leite
dc.contributor.authorMonteiro, Diogo
dc.contributor.authorMorgans, Ryland
dc.contributor.authorBarbosa, Tiago M.
dc.contributor.authorMonteiro, A.M.
dc.contributor.authorForte, Pedro
dc.date.accessioned2024-11-19T09:38:23Z
dc.date.available2024-11-19T09:38:23Z
dc.date.issued2024
dc.description.abstractA promising approach to optimizing recovery in youth football has been the use of machine learning (ML) models to predict recovery states and prevent mental fatigue. This research investigates the application of ML models in classifying male young football players aged under (U)15, U17, and U19 according to their recovery state. Weekly training load data were systematically monitored across three age groups throughout the initial month of the 2019–2020 competitive season, covering 18 training sessions and 120 observation instances. Outfield players were tracked using portable 18-Hz global positioning system (GPS) devices, while heart rate (HR) was measured using 1 Hz telemetry HR bands. The rating of perceived exertion (RPE 6–20) and total quality recovery (TQR 6–20) scores were employed to evaluate perceived exertion, internal training load, and recovery state, respectively. Data preprocessing involved handling missing values, normalization, and feature selection using correlation coefficients and a random forest (RF) classifier. Five ML algorithms [K-nearest neighbors (KNN), extreme gradient boosting (XGBoost), support vector machine (SVM), RF, and decision tree (DT)] were assessed for classification performance. The K-fold method was employed to cross-validate the ML outputs. Results: A high accuracy for this ML classification model (73–100%) was verified. The feature selection highlighted critical variables, and we implemented the ML algorithms considering a panel of 9 variables (U15, U19, body mass, accelerations, decelerations, training weeks, sprint distance, and RPE). These features were included according to their percentage of importance (3–18%). The results were cross-validated with good accuracy across 5-fold (79%). Conclusion: The five ML models, in combination with weekly data, demonstrated the efficacy of wearable device-collected features as an efficient combination in predicting football players’ recovery states.pt_PT
dc.description.sponsorshipThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Portuguese Foundation for Science and Technology, I.P., under grant number UID/CED/04748/2020; SPRINT\u2014Sport Physical Activity and Health Research & Innovation Center, Portugal; Life Quality Research Center (LQRC-CIEQV), Santar\u00E9m, Portugal; Research Center for Active Living and Wellbeing (Livewell), Bragan\u00E7a, Portugal; and Research Centre in Sports Sciences, Health Sciences and Human Development, Vila Real, Portugal.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationTeixeira, José Eduardo; Encarnação, Samuel; Branquinho, Luís; Ferraz, Ricardo; Portella, Daniel Leite; Monteiro, Diogo; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, A.M.; Forte, Pedro (2024). Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach. Frontiers in Psychology. ISSN 1664-1078. 15, p. 1-12pt_PT
dc.identifier.doi10.3389/fpsyg.2024.1447968pt_PT
dc.identifier.issn1664-1078
dc.identifier.urihttp://hdl.handle.net/10198/30626
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherFrontiers in Psychologypt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectYouth soccerpt_PT
dc.subjectRecoverypt_PT
dc.subjectGPSpt_PT
dc.subjectPerceived exertionpt_PT
dc.subjectAIpt_PT
dc.titleClassification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage12pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleFrontiers in Psychologypt_PT
oaire.citation.volume15pt_PT
person.familyNameTeixeira
person.familyNameEncarnação
person.familyNameBarbosa
person.familyNameMonteiro
person.familyNameForte
person.givenNameJosé Eduardo
person.givenNameSamuel
person.givenNameTiago M.
person.givenNameAntónio M.
person.givenNamePedro
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-id9416-E2F5-E660
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.orcid0000-0003-2965-2777
person.identifier.orcid0000-0001-7071-2116
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0003-0184-6780
person.identifier.scopus-author-id10044856400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication79042f92-ce53-4b79-a33f-76ac63c55b8d
relation.isAuthorOfPublicationd38d4c9f-84d5-4562-9482-5322ded17d3d
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