{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:01:29Z","timestamp":1774026089135,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Funds through the FCT Portuguese Foundation for Science and Technology","award":["UID\/CED\/04748\/2025"],"award-info":[{"award-number":["UID\/CED\/04748\/2025"]}]},{"name":"National Funds through the FCT Portuguese Foundation for Science and Technology","award":["UIDB04045\/2021"],"award-info":[{"award-number":["UIDB04045\/2021"]}]},{"name":"Life Quality Research Center (LQRC-CIEQV), Santar\u00e9m, Portugal"},{"name":"Research Center in Sports Sciences, Health Sciences and Human Development, Vila Real, Portugal"},{"name":"SPRINT\u2014Sport Physical Activity and Health Research and Innovation Center, Portugal"},{"name":"national funds from FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, I.P.","award":["UID\/6157\/2025"],"award-info":[{"award-number":["UID\/6157\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Healthcare"],"abstract":"<jats:p>Background: 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. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 \u00b1 0.7 years; height 1.43 \u00b1 0.08 m; body mass 38.6 \u00b1 6.2 kg; BMI 18.7 \u00b1 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\u2013cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms\u2014K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)\u2014were trained using a 70\/30 train\u2013test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: 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. Conclusions: 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.<\/jats:p>","DOI":"10.3390\/healthcare13243301","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T11:00:26Z","timestamp":1765882826000},"page":"3301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence in Sub-Elite Youth Football Players: Predicting Recovery Through Machine Learning Integration of Physical, Technical, Tactical and Maturational Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1077-7233","authenticated-orcid":false,"given":"Pedro","family":"Afonso","sequence":"first","affiliation":[{"name":"Biosciences Higher School of Elvas, Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal"},{"name":"Department of Sport, Exercise and Health Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Research Centre for Active Living and Wellbeing (Livewell), Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-253 Bragan\u00e7a, Portugal"},{"name":"Department of Sports, Higher Institute of Educational Sciences of the Douro, 4560-708 Penafiel, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-6780","authenticated-orcid":false,"given":"Pedro","family":"Forte","sequence":"additional","affiliation":[{"name":"Research Centre for Active Living and Wellbeing (Livewell), Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-253 Bragan\u00e7a, Portugal"},{"name":"CI-ISCE, ISCE Douro, 4560-547 Penafiel, Portugal"},{"name":"Department of Sports Sciences, Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-252 Bragan\u00e7a, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9000-5419","authenticated-orcid":false,"given":"Lu\u00eds","family":"Branquinho","sequence":"additional","affiliation":[{"name":"Biosciences Higher School of Elvas, Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal"},{"name":"Life Quality Research Center (LQRC-CIEQV), 2001-964 Santar\u00e9m, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7530-512X","authenticated-orcid":false,"given":"Ricardo","family":"Ferraz","sequence":"additional","affiliation":[{"name":"Department of Sport Sciences, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"},{"name":"Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8105-7580","authenticated-orcid":false,"given":"Nuno Domingues","family":"Garrido","sequence":"additional","affiliation":[{"name":"Department of Sport, Exercise and Health Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4612-3623","authenticated-orcid":false,"given":"Jos\u00e9 Eduardo","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Department of Sports Sciences, Polytechnic Institute of Guarda, 6300-559 Guarda, Portugal"},{"name":"Department of Sports Sciences, Polytechnic of C\u00e1vado and Ave, 4750-810 Guimar\u00e3es, Portugal"},{"name":"SPRINT\u2014Sport Physical Activity and Health Research & Innovation Center, 6300-559 Guarda, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Palmer, B.L., van der Ploeg, G.E., Bourdon, P.C., Butler, S.R., and Crowther, R.G. 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