A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14510%2F24%3A00137549" target="_blank" >RIV/00216224:14510/24:00137549 - isvavai.cz</a>
Result on the web
<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0309085" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0309085</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pone.0309085" target="_blank" >10.1371/journal.pone.0309085</a>
Alternative languages
Result language
angličtina
Original language name
A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach
Original language description
Tennis, a widely enjoyed sport, motivates athletes and coaches to optimize training for competitive success. This retrospective predictive study examines anthropometric features and statistics of 1990 tennis players in the 2022 season, using 20,040 data points retrospectively obtained from the ATP official source after the end of the season. These data were cross-verified with information from other sources before categorisation to address any discrepancies. Employing various analytical methods, the results emphasize the strategic importance of tournament participation and gameplay for financial gains and higher rankings. Prize money analysis reveals a significant disparity favoring top players. Multivariate Analysis of Variance highlights the need to consider multiple variables for understanding ATP rankings. Multinomial Logistic Regression identifies age, height, and specific service-related metrics as key determinants, with older and taller players more likely to secure top positions. Neural Network models exhibit potential in predicting ATP Rank outcomes, particularly for ATP Rank (500). Our results argue for the use of Artificial Intelligence (AI), specifically Neural Networks, in handling complex interactions and emphasize that AI is a supportive tool in decision-making, requiring careful consideration by experienced individuals. In summary, this study enhances our understanding of ATP ranking factors, providing actionable insights for coaches, players, and stakeholders in the tennis community.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30306 - Sport and fitness sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Plos One
ISSN
1932-6203
e-ISSN
1932-6203
Volume of the periodical
19
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
27
Pages from-to
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UT code for WoS article
001349526900029
EID of the result in the Scopus database
2-s2.0-85208360801