Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach Article Swipe
José E. Teixeira
,
Samuel Encarnação
,
Luís Branquinho
,
Ricardo Ferraz
,
Daniel Leite Portella
,
Diogo Monteiro
,
Ryland Morgans
,
Tiago M. Barbosa
,
António M. Monteiro
,
Pedro Forte
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2024.1447968
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2024.1447968
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyg.2024.1447968
- OA Status
- gold
- Cited By
- 4
- References
- 87
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403880681
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403880681Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpsyg.2024.1447968Digital Object Identifier
- Title
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Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-29Full publication date if available
- Authors
-
José E. Teixeira, Samuel Encarnação, Luís Branquinho, Ricardo Ferraz, Daniel Leite Portella, Diogo Monteiro, Ryland Morgans, Tiago M. Barbosa, António M. Monteiro, Pedro ForteList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyg.2024.1447968Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fpsyg.2024.1447968Direct OA link when available
- Concepts
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Football, Psychology, Elite, Cognitive psychology, Football players, Applied psychology, Elite athletes, Communication, Athletes, Physical therapy, Political science, Medicine, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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87Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.total | 112 |
| abstract_inverted_index.under | 42 |
| abstract_inverted_index.using | 84, 98, 141 |
| abstract_inverted_index.while | 92 |
| abstract_inverted_index.young | 38 |
| abstract_inverted_index.youth | 8 |
| abstract_inverted_index.(79%). | 246 |
| abstract_inverted_index.(KNN), | 155 |
| abstract_inverted_index.(SVM), | 163 |
| abstract_inverted_index.(U)15, | 43 |
| abstract_inverted_index.5-fold | 245 |
| abstract_inverted_index.K-fold | 175 |
| abstract_inverted_index.Weekly | 52 |
| abstract_inverted_index.across | 59, 244 |
| abstract_inverted_index.bands. | 103 |
| abstract_inverted_index.forest | 147 |
| abstract_inverted_index.global | 87 |
| abstract_inverted_index.groups | 62 |
| abstract_inverted_index.mental | 25 |
| abstract_inverted_index.method | 176 |
| abstract_inverted_index.models | 18, 34 |
| abstract_inverted_index.random | 146 |
| abstract_inverted_index.rating | 105 |
| abstract_inverted_index.scores | 117 |
| abstract_inverted_index.sprint | 222 |
| abstract_inverted_index.state, | 129 |
| abstract_inverted_index.state. | 51 |
| abstract_inverted_index.states | 22 |
| abstract_inverted_index.system | 89 |
| abstract_inverted_index.vector | 161 |
| abstract_inverted_index.weekly | 255 |
| abstract_inverted_index.weeks, | 221 |
| abstract_inverted_index.6–20) | 110, 116 |
| abstract_inverted_index.Results | 184 |
| abstract_inverted_index.extreme | 156 |
| abstract_inverted_index.feature | 139, 197 |
| abstract_inverted_index.initial | 65 |
| abstract_inverted_index.machine | 15, 162 |
| abstract_inverted_index.missing | 135 |
| abstract_inverted_index.models, | 251 |
| abstract_inverted_index.players | 40, 81 |
| abstract_inverted_index.predict | 20 |
| abstract_inverted_index.prevent | 24 |
| abstract_inverted_index.quality | 113 |
| abstract_inverted_index.results | 238 |
| abstract_inverted_index.season, | 71 |
| abstract_inverted_index.states. | 273 |
| abstract_inverted_index.support | 160 |
| abstract_inverted_index.tracked | 83 |
| abstract_inverted_index.values, | 136 |
| abstract_inverted_index.Outfield | 80 |
| abstract_inverted_index.accuracy | 187, 243 |
| abstract_inverted_index.approach | 3 |
| abstract_inverted_index.assessed | 170 |
| abstract_inverted_index.boosting | 158 |
| abstract_inverted_index.covering | 72 |
| abstract_inverted_index.critical | 200 |
| abstract_inverted_index.decision | 166 |
| abstract_inverted_index.devices, | 91 |
| abstract_inverted_index.efficacy | 259 |
| abstract_inverted_index.employed | 119, 178 |
| abstract_inverted_index.evaluate | 121 |
| abstract_inverted_index.exertion | 108 |
| abstract_inverted_index.fatigue. | 26 |
| abstract_inverted_index.features | 227, 263 |
| abstract_inverted_index.football | 9, 39, 270 |
| abstract_inverted_index.gradient | 157 |
| abstract_inverted_index.handling | 134 |
| abstract_inverted_index.included | 229 |
| abstract_inverted_index.internal | 124 |
| abstract_inverted_index.involved | 133 |
| abstract_inverted_index.learning | 16 |
| abstract_inverted_index.measured | 97 |
| abstract_inverted_index.outputs. | 183 |
| abstract_inverted_index.portable | 85 |
| abstract_inverted_index.recovery | 6, 21, 50, 114, 128, 272 |
| abstract_inverted_index.research | 28 |
| abstract_inverted_index.sessions | 75 |
| abstract_inverted_index.training | 53, 74, 125, 220 |
| abstract_inverted_index.wearable | 261 |
| abstract_inverted_index.according | 47, 230 |
| abstract_inverted_index.distance, | 223 |
| abstract_inverted_index.efficient | 266 |
| abstract_inverted_index.exertion, | 123 |
| abstract_inverted_index.monitored | 58 |
| abstract_inverted_index.neighbors | 154 |
| abstract_inverted_index.perceived | 107, 122 |
| abstract_inverted_index.promising | 2 |
| abstract_inverted_index.selection | 140, 198 |
| abstract_inverted_index.telemetry | 101 |
| abstract_inverted_index.variables | 213 |
| abstract_inverted_index.verified. | 195 |
| abstract_inverted_index.(3–18%). | 236 |
| abstract_inverted_index.(XGBoost), | 159 |
| abstract_inverted_index.Conclusion | 247 |
| abstract_inverted_index.[K-nearest | 153 |
| abstract_inverted_index.algorithms | 152, 207 |
| abstract_inverted_index.importance | 235 |
| abstract_inverted_index.instances. | 79 |
| abstract_inverted_index.optimizing | 5 |
| abstract_inverted_index.percentage | 233 |
| abstract_inverted_index.players’ | 271 |
| abstract_inverted_index.predicting | 269 |
| abstract_inverted_index.throughout | 63 |
| abstract_inverted_index.variables, | 201 |
| abstract_inverted_index.(73–100%) | 193 |
| abstract_inverted_index.2019–2020 | 69 |
| abstract_inverted_index.application | 31 |
| abstract_inverted_index.classifier. | 149 |
| abstract_inverted_index.classifying | 36 |
| abstract_inverted_index.combination | 253, 267 |
| abstract_inverted_index.competitive | 70 |
| abstract_inverted_index.considering | 208 |
| abstract_inverted_index.correlation | 142 |
| abstract_inverted_index.highlighted | 199 |
| abstract_inverted_index.implemented | 204 |
| abstract_inverted_index.observation | 78 |
| abstract_inverted_index.positioning | 88 |
| abstract_inverted_index.Introduction | 0 |
| abstract_inverted_index.coefficients | 143 |
| abstract_inverted_index.demonstrated | 257 |
| abstract_inverted_index.investigates | 29 |
| abstract_inverted_index.performance. | 173 |
| abstract_inverted_index.preprocessing | 132 |
| abstract_inverted_index.respectively. | 130 |
| abstract_inverted_index.accelerations, | 218 |
| abstract_inverted_index.classification | 172, 191 |
| abstract_inverted_index.cross-validate | 180 |
| abstract_inverted_index.decelerations, | 219 |
| abstract_inverted_index.normalization, | 137 |
| abstract_inverted_index.systematically | 57 |
| abstract_inverted_index.cross-validated | 240 |
| abstract_inverted_index.device-collected | 262 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5065703571 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I3129271201, https://openalex.org/I4210092303, https://openalex.org/I63634437 |
| citation_normalized_percentile.value | 0.85649762 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |