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

Teixeira, José E.; Encarnação, Samuel; Branquinho., L; Ferraz, Ricardo; Portella, Daniel L.; Monteiro, Diogo; Morgans, Ryland; M. Barbosa, Tiago; Monteiro, António Miguel; Forte, Pedro

Publication

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

2024-10-29Journal article

dc.contributor.author	Teixeira, José E.
dc.contributor.author	Encarnação, Samuel
dc.contributor.author	Branquinho., L
dc.contributor.author	Ferraz, Ricardo
dc.contributor.author	Portella, Daniel L.
dc.contributor.author	Monteiro, Diogo
dc.contributor.author	Morgans, Ryland
dc.contributor.author	M. Barbosa, Tiago
dc.contributor.author	Monteiro, António Miguel
dc.contributor.author	Forte, Pedro
dc.date.accessioned	2024-10-31T15:32:41Z
dc.date.available	2024-10-31T15:32:41Z
dc.date.issued	2024-10-29
dc.description.abstract	Introduction: A 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.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Teixeira JE, Encarnação S, Branquinho L, Ferraz R, Portella DL, Monteiro D, Morgans R, Barbosa TM, Monteiro AM and Forte P (2024) Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach. Front. Psychol. 15:1447968. doi: 10.3389/fpsyg.2024.1447968	pt_PT
dc.identifier.doi	10.3389/fpsyg.2024.1447968	pt_PT
dc.identifier.issn	1664-1078
dc.identifier.uri	http://hdl.handle.net/10400.8/10219
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Frontiers	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Youth soccer	pt_PT
dc.subject	Recovery	pt_PT
dc.subject	GPS	pt_PT
dc.subject	Perceived exertion	pt_PT
dc.subject	AI	pt_PT
dc.title	Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.conferencePlace	Suiça	pt_PT
oaire.citation.endPage	12	pt_PT
oaire.citation.startPage	1	pt_PT
oaire.citation.title	Frontiers in Psychology	pt_PT
oaire.citation.volume	15	pt_PT
person.familyName	Monteiro
person.givenName	Diogo
person.identifier	F-1202-2015
person.identifier.ciencia-id	ED1F-6228-E26F
person.identifier.orcid	0000-0002-7179-6814
person.identifier.scopus-author-id	56437945500
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
relation.isAuthorOfPublication	79dcae83-d54a-4acc-a9a1-8268c7776ab9
relation.isAuthorOfPublication.latestForDiscovery	79dcae83-d54a-4acc-a9a1-8268c7776ab9