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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14510%2F23%3A00132389" target="_blank" >RIV/00216224:14510/23:00132389 - isvavai.cz</a>

Result on the web

<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295075" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295075</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0295075" target="_blank" >10.1371/journal.pone.0295075</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30306 - Sport and fitness sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLOS ONE

ISSN

1932-6203

e-ISSN

—

Volume of the periodical

18

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001139775100164

EID of the result in the Scopus database

2-s2.0-85178499750