Analyzing game statistics and career trajectories of female
elite junior tennis players: A machine learning approach

J 2023

Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach

BOZDĚCH, Michal and Jiří ZHÁNĚL

Basic information

Original name

Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach

Authors

BOZDĚCH, Michal (203 Czech Republic, guarantor, belonging to the institution) and Jiří ZHÁNĚL (203 Czech Republic, belonging to the institution)

Edition

PLOS ONE, UNITED STATES, PUBLIC LIBRARY SCIENCE, 2023, 1932-6203

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30306 Sport and fitness sciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

PLOS ONE

Impact factor

Impact factor: 3.700 in 2022

RIV identification code

RIV/00216224:14510/23:00132389

Organization unit

Faculty of Sports Studies

DOI

http://dx.doi.org/10.1371/journal.pone.0295075

UT WoS

001139775100164

Keywords in English

Artificial Intelligence; WJTF; WTA; Rank

Abstract

V originále

Tennis is a popular and complex sport influenced by various factors. Early training increases the risk of career dropout before peak performance. This study analyzed game statistics of World Junior Tennis Final participants (2012–2016), their career paths and it examined how game statistics impact rankings of top 300 female players, aiming to develop an accurate model using percentage-based variables. Descriptive and inferential statistics, including neural networks, were employed. Four machine learning models with categorical predictors and one response were created. Seven models with up to 18 variables and one ordinal (WTA rank) were also developed. Tournament rankings could be predicted using categorical data, but not subsequent professional rankings. Although effects on rankings among top 300 female players were identified, a reliable predictive model using only percentage-based data was not achieved. AI models provided insights into rankings and performance indicators, revealing a lower dropout rate than reported. Participation in elite junior tournaments is crucial for career development and designing training plans in tennis. Further research should explore game statistics, dropout rates, additional variables, and fine-tuning of AI models to improve predictions and understanding of the sport.

Links

MUNI/A/1637/2020, interní kód MU

Name: Lateralita v kontextu diagnostiky vybraných faktorů sportovního výkonu v tenisu a prevence zranění

Investor: Masaryk University, Laterality in the context of diagnostics of selected factors of sports performance in tennis and injury prevention

Citovat

BOZDĚCH, Michal and Jiří ZHÁNĚL. Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach. PLOS ONE. UNITED STATES: PUBLIC LIBRARY SCIENCE, 2023, vol. 18, No 11, p. 1-16. ISSN 1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0295075.

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   author = {Bozděch, Michal and Zháněl, Jiří},
   article_location = {UNITED STATES},
   article_number = {11},
   doi = {http://dx.doi.org/10.1371/journal.pone.0295075},
   keywords = {Artificial Intelligence; WJTF; WTA; Rank},
   language = {eng},
   issn = {1932-6203},
   journal = {PLOS ONE},
   title = {Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach},
   url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295075},
   volume = {18},
   year = {2023}
}

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AU  - Bozděch, Michal - Zháněl, Jiří
PY  - 2023
TI  - Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach
JF  - PLOS ONE
VL  - 18
IS  - 11
SP  - 1-16
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PB  - PUBLIC LIBRARY SCIENCE
SN  - 19326203
KW  - Artificial Intelligence
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KW  - WTA
KW  - Rank
UR  - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295075
N2  - Tennis is a popular and complex sport influenced by various factors. Early training increases the risk of career dropout before peak performance. This study analyzed game statistics of World Junior Tennis Final participants (2012–2016), their career paths and it examined how game statistics impact rankings of top 300 female players, aiming to develop an accurate model using percentage-based variables. Descriptive and inferential statistics, including neural networks, were employed. Four machine learning models with categorical predictors and one response were created. Seven models with up to 18 variables and one ordinal (WTA rank) were also developed. Tournament rankings could be predicted using categorical data, but not subsequent professional rankings. Although effects on rankings among top 300 female players were identified, a reliable predictive model using only percentage-based data was not achieved. AI models provided insights into rankings and performance indicators, revealing a lower dropout rate than reported. Participation in elite junior tournaments is crucial for career development and designing training plans in tennis. Further research should explore game statistics, dropout rates, additional variables, and fine-tuning of AI models to improve predictions and understanding of the sport.
ER  -

BOZDĚCH, Michal and Jiří ZHÁNĚL. Analyzing game statistics and career trajectories of female elite junior tennis players: A machine learning approach. \textit{PLOS ONE}. UNITED STATES: PUBLIC LIBRARY SCIENCE, 2023, vol.~18, No~11, p.~1-16. ISSN~1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0295075.

Detailed Information on Publication Record