A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach

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 o...

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Published in:PloS one 2024-11, Vol.19 (11), p.e0309085
Main Authors: Bozděch, Michal, Puda, Dominik, Grasgruber, Pavel
Format: Article
Language:English
Subjects:
Adolescent
Adult
Analysis
Artificial intelligence
Athletes
Athletes - statistics & numerical data
Athletic Performance - statistics & numerical data
ATP
Biology and Life Sciences
Computer and Information Sciences
Data analysis
Data collection
Data points
Decision making
Female
Humans
Machine Learning
Male
Mathematical models
Medicine and Health Sciences
Multivariate analysis
Neural networks
Physical Sciences
Players
Regression analysis
Research and Analysis Methods
Retrospective Studies
Social Sciences
Statistical analysis
Statistics
Success
Tennis
Tennis players
Trends
Variables
Variance analysis
Video games
Young Adult
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Puda, Dominik
Grasgruber, Pavel
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.
doi_str_mv 10.1371/journal.pone.0309085
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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). 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subjects Adolescent
Adult
Analysis
Artificial intelligence
Athletes
Athletes - statistics & numerical data
Athletic Performance - statistics & numerical data
ATP
Biology and Life Sciences
Computer and Information Sciences
Data analysis
Data collection
Data points
Decision making
Female
Humans
Machine Learning
Male
Mathematical models
Medicine and Health Sciences
Multivariate analysis
Neural networks
Physical Sciences
Players
Regression analysis
Research and Analysis Methods
Retrospective Studies
Social Sciences
Statistical analysis
Statistics
Success
Tennis
Tennis players
Trends
Variables
Variance analysis
Video games
Young Adult
title A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach
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