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Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review

2024Artigo científico Acesso aberto
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dc.contributor.authorSampaio, Tatiana
dc.contributor.authorOliveira, João P.
dc.contributor.authorMarinho, D.A.
dc.contributor.authorNeiva, Henrique P.
dc.contributor.authorMorais, J.E.
dc.date.accessioned2024-08-21T09:26:17Z
dc.date.available2024-08-21T09:26:17Z
dc.date.issued2024
dc.description.abstractTennis has changed toward power-driven gameplay, demanding a nuanced understanding of performance factors. This review explores the role of machine learning in enhancing tennis performance. (2) Methods: A systematic search identified articles utilizing machine learning in tennis performance analysis. (3) Results: Machine learning applications show promise in psychological state monitoring, talent identification, match outcome prediction, spatial and tactical analysis, and injury prevention. Coaches can leverage wearable technologies for personalized psychological state monitoring, data-driven talent identification, and tactical insights for informed decision-making. (4) Conclusions: Machine learning offers coaches insights to refine coaching methodologies and optimize player performance in tennis. By integrating these insights, coaches can adapt to the demands of the sport by improving the players’ outcomes. As technology progresses, continued exploration of machine learning’s potential in tennis is warranted for further advancements in performance optimizationpt_PT
dc.description.sponsorshipThis work is supported by national funds (FCT\u2014Portuguese Foundation for Science and Technology) under the project UIDB/DTP/04045/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSampaio, Tatiana; Oliveira, João P.; Marinho, D.A.; Neiva, Henrique P.; Morais, J.E. (2024). Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review. Applied Sciences. ISSN 2076-3417. 4:13, p. 1-21pt_PT
dc.identifier.doi10.3390/app14135517pt_PT
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10198/30186
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationUIDB/DTP/04045/2020pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial Intelligence (AI)pt_PT
dc.subjectMachine learningpt_PT
dc.subjectPerformancept_PT
dc.subjectTennispt_PT
dc.titleApplications of Machine Learning to Optimize Tennis Performance: A Systematic Reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage21pt_PT
oaire.citation.issue13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume4pt_PT
person.familyNameSampaio
person.familyNameMorais
person.givenNameTatiana
person.givenNameJ.E.
person.identifier867179
person.identifier.ciencia-idAA12-BF58-EE60
person.identifier.orcid0000-0001-8548-2907
person.identifier.orcid0000-0002-6885-0648
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication04e290ae-a93f-4d0b-a577-badc8e1067af
relation.isAuthorOfPublication80b13e62-254d-4d46-ad90-8b509ab523a8
relation.isAuthorOfPublication.latestForDiscovery04e290ae-a93f-4d0b-a577-badc8e1067af

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