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A Cluster Analysis Approach to Profile Men and Women’s Volley Positions in Professional Tennis Matches (Doubles)

Author

Listed:
  • Rafael Martínez-Gallego

    (Department of Sport and Physical Education, University of Valencia, 46010 Valencia, Spain)

  • Jesús Ramón-Llin

    (Department of Teaching of Music, Visual and Corporal Expression, University of Valencia, 46021 Valencia, Spain)

  • Miguel Crespo

    (Integrity and Development Department, International Tennis Federation, London SW15 5XZ, UK)

Abstract

(1) Background: Tennis ball tracking technology allows the aquirement of novel and reliable data about several performance indicators, such as volley positions. This information is key to understand match dynamics in doubles tennis and to better help preparing players for the demands they will face in match play. As such, the purpose of this study was to describe and compare the different types of volley positions in men’s and women’s doubles professional tennis. (2) Methods: Ball tracking data were collected for 46 women (Billie Jean King Cup) and 96 men’s doubles matches (Davis Cup). The variables used were the distance to the net, the distance to the centre of the court and the height of the impact. A K-Means cluster analysis was used to identify in each subsample different profiles of volley locations. (3) Results: The inferential analysis revealed differences in men’s (distance to the net η 2 = 0.72, distance to the centre of the court η 2 = 0.77 and impact height η 2 = 0.63) and women’s subsamples (distance to the net η 2 = 0.48, distance to the centre of the court η 2 = 0.52 and impact height η 2 = 0.51). (4) Conclusions: The results allowed the suggestion of a higher variability in men’s matches, as there were seven different clusters identified, and only four in women’s.

Suggested Citation

  • Rafael Martínez-Gallego & Jesús Ramón-Llin & Miguel Crespo, 2021. "A Cluster Analysis Approach to Profile Men and Women’s Volley Positions in Professional Tennis Matches (Doubles)," Sustainability, MDPI, vol. 13(11), pages 1-9, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6370-:d:568417
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    References listed on IDEAS

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    1. Jaime Sampaio & Tim McGarry & Julio Calleja-González & Sergio Jiménez Sáiz & Xavi Schelling i del Alcázar & Mindaugas Balciunas, 2015. "Exploring Game Performance in the National Basketball Association Using Player Tracking Data," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-14, July.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Rafael Martínez-Gallego & Fernando Vives & José Francisco Guzmán & Jesús Ramón-Llin & Miguel Crespo, 2021. "Time structure in men’s professional doubles tennis: does team experience allow finishing the points faster?," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 21(2), pages 215-225, March.
    4. Kristijan Breznik, 2015. "Revealing the best doubles teams and players in tennis history," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 15(3), pages 1213-1226, December.
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