A machine learning approach for the classification of sports based on a coaches' perspective of environmental, individual and task requirements: A sports profile analysis.
Well-designed talent programmes in sports with a focus on talent identification, orientation, development, and transfer support the engagement of young individuals and the pursuit of elite performance. To facilitate these processes, an analysis of task, environmental and individual characteristics p...
| Publicado en: | Journal of Sports Sciences Vol. 43; no. 1; pp. 23 - 33 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183684282&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183684282 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640414 5BV jtl: Journal of Sports Sciences issn: 02640414 maglogo: Y pubinfo: dt: Jan2025 vid: 43 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 183684282 174285211 183684282 183684282 10.1080/02640414.2023.2271706 183684282 ppf: 23 ppct: 10 formats: tig: atl: A machine learning approach for the classification of sports based on a coaches' perspective of environmental, individual and task requirements: A sports profile analysis. aug: au: Teunissen, Jan Willem Faber, Irene R. De Bock, Jelle Slembrouck, Maarten Verstockt, Steven Lenoir, Matthieu Pion, Johan affil: Institute for Studies in Sports and Exercise, HAN University of Applied Sciences, Nijmegen, The Netherlands sug: subj: Athletic Performance Machine Learning Task Performance and Analysis Coaches, Athletic Athletes In Adolescence Talent Identification, Sports Classification Skill Acquisition Human Aptitude Sports Classification Surveys Discriminant Analysis Machine Learning Algorithms Evaluation Adolescence Environment Adolescent: 13-18 years ab: Well-designed talent programmes in sports with a focus on talent identification, orientation, development, and transfer support the engagement of young individuals and the pursuit of elite performance. To facilitate these processes, an analysis of task, environmental and individual characteristics per sport is much needed. The aims of this study were to 1) analyse whether unique profiles per sport could be established by generic characteristics and 2) to discuss similarities and differences for the potential application in talent development and transfer. By means of a validated survey, 1247 coaches from 34 sports ranked 18 characteristics on importance to their sports (0 = not important − 10 = very important). To discriminate the responses per sport a Discriminant Analysis (DA) was carried out. To refine the DA-classification, Uniform Manifold Approximation and Projection (UMAP) with CatBoost classifier was performed. To test the performance of the CatBoost classifier-algorithm, a confusion-matrix was generated. The cross-validated DA showed that 70.2% of the coaches were correctly classified to their sport. The UMAP/CatBoost technique revealed 75.1% accuracy with correctly predicted responses per sport ranging from 18.2% (sailing) to 98.2% (soccer). With varying precision, the algorithm was able to differentiate sports by importance of its characteristics indicating similarities and differences per sport. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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