[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118159-en":3,"doc-seo-118159-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118159,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review","Tennis has evolved toward power-driven play, increasing the need for a refined understanding of performance determinants. This systematic review synthesizes research applying machine learning to tennis performance analysis. Evidence indicates machine learning can support monitoring of psychological state, talent identification, match outcome prediction, spatial and tactical analysis, and injury prevention. It also highlights practical use of wearable technologies to enable data-driven coaching decisions. Conclusions support integrating these insights to optimize players’ outcomes as technology advances.","Systematic Review  \nApplications of Machine Learning to Optimize Tennis Performance: A Systematic Review  \nTatiana Sampaio 1,2,3,*, João P. Oliveira 1,2,3, Daniel A. Marinho 1,2, Henrique P. Neiva 1,2 and Jorge E. Morais 3,4  \n1 Department of Sports Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal; [jpco-2001@live.com.pt](jpco-2001@live.com.pt) (J.P.O.); [marinho.d@gmail.com](marinho.d@gmail.com) (D.A.M.); [hpn@ubi.pt](hpn@ubi.pt) (H.P.N.)  \n2 Research Centre in Sports, Health and Human Development (CIDESD), 6201-001 Covilhã, Portugal  \n3 Research Centre for Active Living and Wellbeing (LiveWell), Instituto Politécnico de Bragança, 5301-856 Bragança, Portugal; [morais.jorgestrela@ipb.pt](morais.jorgestrela@ipb.pt)  \n4 Department of Sports Sciences, Instituto Politécnico de Bragança, 5301-856 Bragança, Portugal  \n* Correspondence: [tatiana_sampaio30@hotmail.com](tatiana_sampaio30@hotmail.com)  \nAbstract: (1) Background: Tennis 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 ma  \nchine learning in tennis performance analysis. (2) 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 optimization.  \nKeywords: machine learning; performance; tennis; artificial intelligence (AI)  \nCitation: Sampaio, T.; Oliveira, J.P.; Marinho, D.A.; Neiva, H.P.; Morais, J.E. Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review. Appl. Sci. 2024, 14, 5517. [https://doi.org/10.3390/app14135517](https://doi.org/10.3390/app14135517)  \nAcademic Editor: Arkady Voloshin  \nReceived: 28 May 2024  \nRevised: 20 June 2024  \nAccepted: 21 June 2024  \nPublished: 25 June 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/license](https://creativecommons.org/license)s/by/4.0/) .  \n1. Introduction  \nTennis has undergone a transformation in recent decades [1]. Once a sport dominated by finesse and technical skill [2], it has become a lightning-fast, power-driven game where players regularly perform serves exceeding 210 km per hour [2] . Success hinges not on a single dominant physical attribute but rather on a complex interplay of various physical components [1] . To compete at the highest level, athletes now require a holistic combination of speed, agility, and power, coupled with moderate to high aerobic capacity [1]. Supporting these physical demands are critical cognitive and psychological processes [3,4] . Players must exhibit exceptional reactive abilities, anticipation skills, and decision-making skills while maintaining mental fortitude to cope with fatigue, the pressure of highstakes points, and the draw of significant extrinsic rewards, such as ranking and lucrative endorsements [5–7] . The stop-and-start nature of tennis competition further adds to the complexity [2] .  \nMatches are characterized by intermittent periods of high-intensity activity lasting 4–10 s, combined with brief recovery periods of 10–20 s and longer rest intervals of 60–90 s","cbCaiaCHzL6mxv01","https://ap.wps.com/l/cbCaiaCHzL6mxv01","pdf",709675,1,21,"English","en",105,"# Abstract\n# Introduction\n## Tennis performance challenges\n## Machine learning rationale and data preparation\n# Methods\n## Systematic search strategy\n# Results\n## Psychological monitoring\n## Talent identification and prediction\n## Spatial/tactical analysis and injury prevention\n# Conclusions","[{\"question\":\"What problem does the review address in tennis performance?\",\"answer\":\"The review addresses the complexity of modern tennis performance, where success depends on interacting physical, cognitive, and psychological factors, and coaches need better optimization methods.\"},{\"question\":\"What methods are used to gather studies for this review?\",\"answer\":\"A systematic search was conducted to identify articles that apply machine learning to tennis performance analysis.\"},{\"question\":\"How can machine learning support coaching decisions in tennis?\",\"answer\":\"Machine learning can aid psychological state monitoring, talent identification, match outcome prediction, and spatial and tactical analysis, enabling more informed, data-driven coaching and performance optimization.\"}]","Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review | 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problem does the review address in tennis performance?","Question",{"text":75,"@type":76},"The review addresses the complexity of modern tennis performance, where success depends on interacting physical, cognitive, and psychological factors, and coaches need better optimization methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What methods are used to gather studies for this review?",{"text":80,"@type":76},"A systematic search was conducted to identify articles that apply machine learning to tennis performance analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How can machine learning support coaching decisions in tennis?",{"text":84,"@type":76},"Machine learning can aid psychological state monitoring, talent identification, match outcome prediction, and spatial and tactical analysis, enabling more informed, data-driven coaching and performance 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