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

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  • Michal Bozděch
  • Dominik Puda
  • Pavel Grasgruber

Abstract

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.

Suggested Citation

  • Michal Bozděch & Dominik Puda & Pavel Grasgruber, 2024. "A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-27, November.
    • Handle: RePEc:plo:pone00:0309085
    DOI: 10.1371/journal.pone.0309085
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    References listed on IDEAS

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    1. Hazuan Hizan & Peter Whipp & Machar Reid, 2011. "Comparison of serve and serve return statistics of high performance male and female tennis players from different age-groups," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 11(2), pages 365-375, August.
    2. Stefan Schmidhofer & Roland Leser & Michael Ebert, 2014. "A comparison between the structure in elite tennis and kids tennis on scaled courts (Tennis 10s)," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 14(3), pages 829-840, December.
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