Nejvíce citovaný článek - PubMed ID 38033090
Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach
This study explores the intricate dynamics of the Junior-to-Senior (JTS) transition phase in elite tennis. Focusing on challenges faced by young talents, the research aims to unveil factors influencing successful transitions and the role of elite junior tournaments. In a retrospective analysis, male tennis players (n = 240) from national teams in the ITF World Junior Tennis Finals tournament (2012-2016) were analyzed using Chi-square tests, Cramer's V, Bayesian statistics, and Multinomial Logistic Regression (MLR). Results revealed that 62.1% of elite junior participants were found in the Association of Tennis Professionals database, emphasizing the significance of team nominations and tournament results as important variables to monitor. Inferential and Bayesian statistics confirmed robustness, with MLR highlighting tournament results' importance. The findings highlight the complexities of the JTS transition and underscore the pivotal roles of participation, national team nominations, and tournament results. The study recommends the implementation of comprehensive player development programs, urging strategic team selections by national federations and academies. Coaches, stakeholders, and organizations should prioritize monitoring these variables for early talent identification and support. These measures collectively aim to optimize success trajectories, navigating the critical JTS phase in junior tennis players' sporting careers.
- MeSH
- Bayesova věta MeSH
- lidé MeSH
- mladiství MeSH
- retrospektivní studie MeSH
- sportovci MeSH
- sportovní výkon * fyziologie MeSH
- tenis * MeSH
- Check Tag
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH