Publicação
Artificial Intelligence in Sub-Elite Youth Football Players: Predicting Recovery Through Machine Learning Integration of Physical, Technical, Tactical and Maturational Data
| datacite.subject.fos | Ciências Sociais::Ciências da Educação | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Afonso, Pedro Miguel Vaz | |
| dc.contributor.author | Forte, Pedro | |
| dc.contributor.author | Branquinho, Luís | |
| dc.contributor.author | Ferraz, Ricardo | |
| dc.contributor.author | Garrido, Nuno D. | |
| dc.contributor.author | Teixeira, José Eduardo | |
| dc.date.accessioned | 2026-01-12T11:25:26Z | |
| dc.date.available | 2026-01-12T11:25:26Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Forty male sub-elite U11 and U13 football players (age 10.3 +/- 0.7 years; height 1.43 +/- 0.08 m; body mass 38.6 +/- 6.2 kg; BMI 18.7 +/- 2.1 kg/m2) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical-cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)-were trained using a 70/30 train-test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. | eng |
| dc.description.sponsorship | The authors declare that financial support was received for the research and/or publication of this article. This project was supported by the National Funds through the FCT Portuguese Foundation for Science and Technology (projects UID/CED/04748/2025 and UIDB04045/2021), Life Quality Research Center (LQRC-CIEQV), Santarém, Portugal; Research Center in Sports Sciences, Health Sciences and Human Development, Vila Real, Portugal; SPRINT—Sport Physical Activity and Health Research and Innovation Center, Portugal; and This work is supported by national funds from FCT—Fundação para a Ciência e a Tecnologia, I.P., under the project/support UID/6157/2025. | |
| dc.identifier.citation | Afonso, Pedro Miguel Vaz; Forte, Pedro; Branquinho, Luís; Ferraz, Ricardo; Garrido, Nuno D.; Teixeira, José Eduardo (2025). Artificial Intelligence in Sub-Elite Youth Football Players: Predicting Recovery Through Machine Learning Integration of Physical, Technical, Tactical and Maturational Data. Healthcare. EISSN 2227-9032. 13:24, p. 1-20 | |
| dc.identifier.doi | 10.3390/healthcare13243301 | |
| dc.identifier.eissn | 2227-9032 | |
| dc.identifier.uri | http://hdl.handle.net/10198/35451 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation | Life Quality Research Centre | |
| dc.relation.ispartof | Healthcare | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Machine learning | |
| dc.subject | Youth soccer | |
| dc.subject | Recovery | |
| dc.subject | Maturation | |
| dc.subject | Performance | |
| dc.title | Artificial Intelligence in Sub-Elite Youth Football Players: Predicting Recovery Through Machine Learning Integration of Physical, Technical, Tactical and Maturational Data | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Life Quality Research Centre | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04748%2F2020/PT | |
| oaire.citation.endPage | 20 | |
| oaire.citation.issue | 24 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Healthcare | |
| oaire.citation.volume | 13 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Afonso | |
| person.familyName | Forte | |
| person.givenName | Pedro Miguel Vaz | |
| person.givenName | Pedro | |
| person.identifier.ciencia-id | 351B-B16B-79C7 | |
| person.identifier.orcid | 0009-0000-1077-7233 | |
| person.identifier.orcid | 0000-0003-0184-6780 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
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