Analyzing and predicting career trajectory of male elite junior
tennis players: A machine learning approach

k 2024

Analyzing and predicting career trajectory of male elite junior tennis players: A machine learning approach

BOZDĚCH, Michal a Jiří ZHÁNĚL

Základní údaje

Originální název

Analyzing and predicting career trajectory of male elite junior tennis players: A machine learning approach

Autoři

BOZDĚCH, Michal a Jiří ZHÁNĚL

Vydání

10th International Scientific Conference on Kinesiology, 2024

Další údaje

Jazyk

angličtina

Typ výsledku

Prezentace na konferencích

Obor

50902 Social sciences, interdisciplinary

Stát vydavatele

Chorvatsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

Ano

Organizační jednotka

Fakulta sociálních studií

Klíčová slova anglicky

JTS; boys; senior; professional; sport; talent; Artificial Intelligence

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 9. 2025 11:49, Mgr. Michal Bozděch, Ph.D.

Anotace

V originále

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-predictive analysis, 240 male tennis players from national teams in the World Junior Tennis Finals (2012-2016) were studied. The cleaned dataset (n = 2847) underwent statistical analyses, including Chi-square tests, Cramer’s V, Bayesian approaches, and Multinomial Logistic Regression (MLR). Artificial Intelligence (AI) models, using supervised learning classification, were applied. Results revealed 62.08% elite junior participants in the Association of Tennis Professionals (ATP) database, emphasizing the significance of team nominations and tournament results in predicting ATP status. Inferential and Bayesian statistics confirmed robustness, with MLR highlighting tournament results' importance. The most accurate AI model (2.1) achieved 84.5% testing accuracy and a 0.76 AUC, suggesting practical application. Findings underscore JTS complexities, emphasizing the pivotal roles of participation, national team nominations, and tournament results. The study recommends 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.

Citovat

BOZDĚCH, Michal a Jiří ZHÁNĚL. Analyzing and predicting career trajectory of male elite junior tennis players: A machine learning approach. In 10th International Scientific Conference on Kinesiology. 2024.

@proceedings{2433642,
   author = {Bozděch, Michal and Zháněl, Jiří},
   booktitle = {10th International Scientific Conference on Kinesiology},
   keywords = {JTS; boys; senior; professional; sport; talent; Artificial Intelligence},
   language = {eng},
   title = {Analyzing and predicting career trajectory of male elite junior tennis players: A machine learning approach},
   url = {https://www.kif.unizg.hr/conference.kinesiology},
   year = {2024}
}

TY  - CONF
ID  - 2433642
AU  - Bozděch, Michal - Zháněl, Jiří
PY  - 2024
TI  - Analyzing and predicting career trajectory of male elite junior tennis players: A machine learning approach
KW  - JTS
KW  - boys
KW  - senior
KW  - professional
KW  - sport
KW  - talent
KW  - Artificial Intelligence
UR  - https://www.kif.unizg.hr/conference.kinesiology
N2  - 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-predictive analysis, 240 male tennis players from national teams in the World Junior Tennis Finals (2012-2016) were studied. The cleaned dataset (n = 2847) underwent statistical analyses, including Chi-square tests, Cramer’s V, Bayesian approaches, and Multinomial Logistic Regression (MLR). Artificial Intelligence (AI) models, using supervised learning classification, were applied. Results revealed 62.08% elite junior participants in the Association of Tennis Professionals (ATP) database, emphasizing the significance of team nominations and tournament results in predicting ATP status. Inferential and Bayesian statistics confirmed robustness, with MLR highlighting tournament results' importance. The most accurate AI model (2.1) achieved 84.5% testing accuracy and a 0.76 AUC, suggesting practical application. Findings underscore JTS complexities, emphasizing the pivotal roles of participation, national team nominations, and tournament results. The study recommends 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.
ER  -

BOZDĚCH, Michal a Jiří ZHÁNĚL. Analyzing and predicting career trajectory of male elite junior tennis players: A machine learning approach. In \textit{10th International Scientific Conference on Kinesiology}. 2024.

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