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ORIGINAL RESEARCH article

Front. Sports Act. Living
Sec. Sports Coaching: Performance and Development
Volume 6 - 2024 | doi: 10.3389/fspor.2024.1445510

"There is more that unites us than divides us". Optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities

Provisionally accepted
Jan Willem Teunissen Jan Willem Teunissen 1,2*Jelle De Bock Jelle De Bock 2Dominique Schasfoort Dominique Schasfoort 1Maarten Slembrouck Maarten Slembrouck 2Steven Verstockt Steven Verstockt 2Matthieu E. Lenoir Matthieu E. Lenoir 2Johan Pion Johan Pion 2
  • 1 HAN University of Applied Sciences, Nijmegen, Netherlands
  • 2 Ghent University, Ghent, East Flanders, Belgium

The final, formatted version of the article will be published soon.

    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, based on the SportKompas I Need (Pion, 2015), was conducted, garnering responses from 1247 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

    Received: 07 Jun 2024; Accepted: 08 Oct 2024.

    Copyright: © 2024 Teunissen, De Bock, Schasfoort, Slembrouck, Verstockt, Lenoir and Pion. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jan Willem Teunissen, HAN University of Applied Sciences, Nijmegen, Netherlands

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.