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'There is more that unites us than divides us' : optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities

Jan Willem Teunissen (UGent) , Jelle De Bock, Dominique Schasfoort, Maarten Slembrouck (UGent) , Steven Verstockt (UGent) , Matthieu Lenoir (UGent) and Johan Pion (UGent)
(2024) FRONTIERS IN SPORTS AND ACTIVE LIVING. 6.
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Abstract
Sports are characterized by unique rules, environments, and tasks, but also share fundamental similarities with each other sport. Such between-sports parallels can be vital for optimizing talent transfer processes. This study aimed to explore similarities between sports to provide an objective basis for clustering sports into families by means of machine learning. An online survey was conducted, garnering responses from 1,247 coaches across 36 countries and 34 sports. The survey gauged the importance (0 = not important 10 = important) of 18 characteristics related to the sport and the athlete performing in that sport. These traits formed the basis for the categorization of a sport by means of machine learning, particularly unsupervised clustering, and the LIME feature explainer. Analysis grouped 34 sports into five clusters based on shared features. A similarity matrix illustrated the degree of overlap among sports. The application of unsupervised clustering emphasized the lack of a single overarching attribute across sports, marking a shift away from traditional clustering approaches that rely on a limited set of characteristics for talent transfer. The results highlight the importance of identifying common sports for talent transfer, which could prove advantageous in guiding athletes towards new sporting directions.
Keywords
machine learning (ML), talent transfer, sports, clustering, experiential knowledge, coaches, questionnaire, PSYCHOLOGICAL CHARACTERISTICS, IDENTIFICATION, PERFORMANCE

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MLA
Teunissen, Jan Willem, et al. “’There Is More That Unites Us than Divides Us’ : Optimizing Talent Transfer Processes by Clustering 34 Sports by Their Task, Individual and Environmental Similarities.” FRONTIERS IN SPORTS AND ACTIVE LIVING, vol. 6, 2024, doi:10.3389/fspor.2024.1445510.
APA
Teunissen, J. W., De Bock, J., Schasfoort, D., Slembrouck, M., Verstockt, S., Lenoir, M., & Pion, J. (2024). ’There is more that unites us than divides us’ : optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities. FRONTIERS IN SPORTS AND ACTIVE LIVING, 6. https://doi.org/10.3389/fspor.2024.1445510
Chicago author-date
Teunissen, Jan Willem, Jelle De Bock, Dominique Schasfoort, Maarten Slembrouck, Steven Verstockt, Matthieu Lenoir, and Johan Pion. 2024. “’There Is More That Unites Us than Divides Us’ : Optimizing Talent Transfer Processes by Clustering 34 Sports by Their Task, Individual and Environmental Similarities.” FRONTIERS IN SPORTS AND ACTIVE LIVING 6. https://doi.org/10.3389/fspor.2024.1445510.
Chicago author-date (all authors)
Teunissen, Jan Willem, Jelle De Bock, Dominique Schasfoort, Maarten Slembrouck, Steven Verstockt, Matthieu Lenoir, and Johan Pion. 2024. “’There Is More That Unites Us than Divides Us’ : Optimizing Talent Transfer Processes by Clustering 34 Sports by Their Task, Individual and Environmental Similarities.” FRONTIERS IN SPORTS AND ACTIVE LIVING 6. doi:10.3389/fspor.2024.1445510.
Vancouver
1.
Teunissen JW, De Bock J, Schasfoort D, Slembrouck M, Verstockt S, Lenoir M, et al. ’There is more that unites us than divides us’ : optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities. FRONTIERS IN SPORTS AND ACTIVE LIVING. 2024;6.
IEEE
[1]
J. W. Teunissen et al., “’There is more that unites us than divides us’ : optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities,” FRONTIERS IN SPORTS AND ACTIVE LIVING, vol. 6, 2024.
@article{01JDHGATASRSRJ05VJ8X3C1GCX,
  abstract     = {{Sports are characterized by unique rules, environments, and tasks, but also share fundamental similarities with each other sport. Such between-sports parallels can be vital for optimizing talent transfer processes. This study aimed to explore similarities between sports to provide an objective basis for clustering sports into families by means of machine learning. An online survey was conducted, garnering responses from 1,247 coaches across 36 countries and 34 sports. The survey gauged the importance (0 = not important 10 = important) of 18 characteristics related to the sport and the athlete performing in that sport. These traits formed the basis for the categorization of a sport by means of machine learning, particularly unsupervised clustering, and the LIME feature explainer. Analysis grouped 34 sports into five clusters based on shared features. A similarity matrix illustrated the degree of overlap among sports. The application of unsupervised clustering emphasized the lack of a single overarching attribute across sports, marking a shift away from traditional clustering approaches that rely on a limited set of characteristics for talent transfer. The results highlight the importance of identifying common sports for talent transfer, which could prove advantageous in guiding athletes towards new sporting directions.}},
  articleno    = {{1445510}},
  author       = {{Teunissen, Jan Willem and De Bock, Jelle and Schasfoort, Dominique and Slembrouck, Maarten and Verstockt, Steven and Lenoir, Matthieu and Pion, Johan}},
  issn         = {{2624-9367}},
  journal      = {{FRONTIERS IN SPORTS AND ACTIVE LIVING}},
  keywords     = {{machine learning (ML),talent transfer,sports,clustering,experiential knowledge,coaches,questionnaire,PSYCHOLOGICAL CHARACTERISTICS,IDENTIFICATION,PERFORMANCE}},
  language     = {{eng}},
  pages        = {{9}},
  title        = {{'There is more that unites us than divides us' : optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities}},
  url          = {{http://doi.org/10.3389/fspor.2024.1445510}},
  volume       = {{6}},
  year         = {{2024}},
}
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