An Empirical Analysis of Factors Influencing the On-Court Performance of Elite Badminton Players in Guangdong Province

This paper explores the key factors influencing the performance of badminton players in high-level competitions, with a particular focus on the cultivation and impact of psychological qualities. The study reveals that athletes typically require 13 to 15 years of systematic training encompassing physical fitness, technical skills, tactical strategies, and psychological conditioning. Psychological qualities, as a crucial component of athletic performance, directly affect athletes' emotions, cognition, and willpower, thereby playing a pivotal role in competitive scenarios. By analyzing data from elite badminton players in Guangdong Province, this study identifies critical factors affecting on-court performance, including age, family atmosphere, best previous achievements before championship titles, and self-regulation abilities. The findings indicate that strong psychological qualities and family support significantly enhance athletes' performance, laying a solid foundation for their professional careers. Lastly, the study integrates the Psychological Journey Theory to examine how pivotal events at different stages of an athlete's development impact their mental resilience and competitive performance.

Elite badminton players，On-court performance，Machine learning，Psychological factors，Talent identification

1. Aquilina, D., & Henry, I. (2010). Elite athletes and university education in Europe: A review of policy and practice in higher education in the European Union Member States. International Journal of Sport Policy and Politics. https://doi.org/10.1080/19406941003634024 [Google Scholar] [Crossref]

2. Birrer, D., & Morgan, G. (2010). Psychological skills training as a way to enhance an athlete’s performance in high-intensity sports. Scandinavian Journal of Medicine & Science in Sports, 20(2), 78–87. https://doi.org/10.1111/j.1600-0838.2010.01188.x [Google Scholar] [Crossref]

3. Burgess, D. J., & Naughton, G. A. (2010). Talent Development in Adolescent Team Sports: A Review. https://doi.org/10.1123/ijspp.5.1.103 [Google Scholar] [Crossref]

4. Casebolt, K., & Zhang, P. (2020). An Authentic Badminton Game Performance Assessment Rubric. Strategies. https://www.tandfonline.com/doi/abs/10.1080/08924562.2019.1680329 [Google Scholar] [Crossref]

5. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2012). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1 [Google Scholar] [Crossref]

6. Debois, N., Ledon, A., & Wylleman, P. (2012). A lifespan perspective on the dual career of elite male athletes. Psychology of Sport and Exercise, 13(5), 630–639. https://doi.org/10.1016/j.psychsport.2012.04.005 [Google Scholar] [Crossref]

7. Dionigi, R. A., Fraser-Thomas, J., & Logan, J. (2012). The nature of family influences on sport participation in Masters athletes. Annals of Leisure Research. https://www.tandfonline.com/doi/abs/10.1080/11745398.2012.744274 [Google Scholar] [Crossref]

8. Felton, L., & Jowett, S. (2013). “What do coaches do” and “how do they relate”: Their effects on athletes’ psychological needs and functioning. Scandinavian Journal of Medicine & Science in Sports, 23(2), e130–e139. https://doi.org/10.1111/sms.12029 [Google Scholar] [Crossref]

9. Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 3133–3181. [Google Scholar] [Crossref]

10. Fletcher, D., & Sarkar, M. (2012). A grounded theory of psychological resilience in Olympic champions. Psychology of Sport and Exercise, 13(5), 669–678. https://doi.org/10.1016/j.psychsport.2012.04.007 [Google Scholar] [Crossref]

11. Freund, Y., Schapire, R. E., & Singer, Y. (2013). A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5), 771–780. https://doi.org/10.1145/3319535.3354196 [Google Scholar] [Crossref]

12. Gould, D., & Maynard, I. (2012). Psychological preparation for the Olympic Games. Journal of Sports Sciences, 27(13), 1393–1408. https://doi.org/10.1080/02640410903081826 [Google Scholar] [Crossref]

13. Henriksen, K., Stambulova, N., & Roessler, K. K. (2010). Successful talent development in track and field: Considering the role of environment. Scandinavian Journal of Medicine & Science in Sports, 20(S2), 122–132. https://doi.org/10.1111/j.1600-0838.2010.01187.x [Google Scholar] [Crossref]

14. Hollings, S. C., Mallett, C. J., & Hume, P. A. (2014). The transition from elite junior track-and-field athlete to successful senior athlete: Why some do, why others don’t. International Journal of Sports Science & Coaching, 9(3), 457–471. https://doi.org/10.1260/1747-9541.9.3.457 [Google Scholar] [Crossref]

15. Kegelaers, J., & Wylleman, P. (2019). Mental toughness and elite sports performance: A critical review. Sport, Exercise, and Performance Psychology, 8(3), 232–247. https://doi.org/10.1037/spy0000151 [Google Scholar] [Crossref]

16. Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. https://doi.org/10.1007/978-1-4614-6849-3 [Google Scholar] [Crossref]

17. Liaw, A., & Wiener, M. (2018). Classification and regression by randomForest. R News, 2(3), 18–22. [Google Scholar] [Crossref]

18. Loh, W. Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329–348. https://doi.org/10.1111/insr.12016 [Google Scholar] [Crossref]

19. MacNamara, Á., & Collins, D. (2015). Profiling, exploiting, and countering psychological characteristics in talent identification and development. The Sport Psychologist, 29(3), 290–301. https://doi.org/10.1123/tsp.2014-0018 [Google Scholar] [Crossref]

20. Mandolesi, L., Polverino, A., Montuori, S., Foti, F., Ferraioli, G., Sorrentino, P., & Sorrentino, G. (2018). Effects of Physical Exercise on Cognitive Functioning and Wellbeing: Biological and Psychological Benefits. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00509 [Google Scholar] [Crossref]

21. Olmedilla, A., Rubio, V. J., Ortega, E., García-Mas, A., & Ríes, F. (2018). Psychological intervention program to control stress in young tennis players. Frontiers in Psychology, 9, 2410. https://doi.org/10.3389/fpsyg.2018.02410 [Google Scholar] [Crossref]

22. Phomsoupha, M., & Laffaye, G. (2015). The science of badminton: Game characteristics, anthropometry, physiology, visual fitness and biomechanics. Sports Medicine, 45(4), 473–495. https://doi.org/10.1007/s40279-014-0287-2 [Google Scholar] [Crossref]

23. Rokach, L., & Maimon, O. (2014). Data mining with decision trees: Theory and applications (2nd ed.). World Scientific Publishing. https://doi.org/10.1142/9097 [Google Scholar] [Crossref]

24. Sargent Megicks, B., Till, K., Rongen, F., Cowburn, I., Gledhill, A., Mitchell, T., Emmonds, S., & Lara-Bercial, S. (2022). Examining European Talent Development Environments: Athlete, Parent and Coach Perceptions. Journal of Sports Sciences, 40(22), 2533–2543. https://doi.org/10.1080/02640414.2023.2172800 [Google Scholar] [Crossref]

25. Schapire, R. E., & Freund, Y. (2012). Boosting: Foundations and algorithms. MIT Press. [Google Scholar] [Crossref]

26. Slimani, M., Taylor, L., Baker, J. S., & Chamari, K. (2016). Effects of mental training on muscular force, hormonal and physiological changes in athletes: A review. Journal of Sports Sciences, 34(23), 2189–2199. https://doi.org/10.1080/02640414.2016.1210196 [Google Scholar] [Crossref]

27. Stambulova, N., Engström, C., Franck, A., Linnér, L., & Lindahl, K. (2015). Searching for an optimal balance: Dual career experiences of Swedish adolescent athletes. Psychology of Sport and Exercise, 21, 4–14. https://doi.org/10.1016/j.psychsport.2014.08.009 [Google Scholar] [Crossref]

28. Strandjord, S. E., & Rome, E. S. (2016). Growth and Development in the Young Athlete. A. C. Colvin & J. N. Gladstone, The Young Tennis Player: Injury Prevention and Treatment (19–36). Springer International Publishing. https://doi.org/10.1007/978-3-319-27559-8_2 [Google Scholar] [Crossref]

29. Weinberg, R. S., & Gould, D. (2019). Foundations of sport and exercise psychology (7th ed.). Human Kinetics. [Google Scholar] [Crossref]

30. Wright, C., Atkins, S., & Jones, B. (2012). An analysis of elite coaches’ engagement with performance analysis services (match, notational analysis and technique analysis). International Journal of Performance Analysis in Sport. https://www.tandfonline.com/doi/abs/10.1080/24748668.2012.11868609 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study