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Mutual Point-winning Probabilities (MPW): a New Performance Measure for Table Tennis

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  • Christophe Ley
  • Yves Dominicy

Abstract

We propose a new performance measure for table tennis players: the mutual point-winning probabilities (MPW) as server and receiver. The MPWs quantify a player's chances to win a point against a given opponent, and hence nicely complement the classical match statistics history between two players. We shall describe the MPWs, explain the statistics underpinning their calculation, and show via a Monte Carlo simulation study that our estimation procedure works well. As an illustration of the MPWs' versatile use, we use it as an alternative ranking method in two round-robin tournaments of ten respectively eleven table tennis players that we have ourselves organized.

Suggested Citation

  • Christophe Ley & Yves Dominicy, 2017. "Mutual Point-winning Probabilities (MPW): a New Performance Measure for Table Tennis," Working Papers ECARES ECARES 2017-23, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/250695
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    References listed on IDEAS

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    1. Gerhard Tutz & Gunther Schauberger, 2015. "Extended ordered paired comparison models with application to football data from German Bundesliga," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 209-227, April.
    2. Baker, Rose D. & McHale, Ian G., 2014. "A dynamic paired comparisons model: Who is the greatest tennis player?," European Journal of Operational Research, Elsevier, vol. 236(2), pages 677-684.
    3. Davy Paindaveine & Yvik Swan, 2009. "A stochastic analysis of some two-person sports," Working Papers ECARES 2009_025, ULB -- Universite Libre de Bruxelles.
    4. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    5. Sean Harris, 2016. "The Effects of Different Scoring Systems on Upset Percentage and Match Length in Tennis: A Simulation Study," Journal of Sports Research, Conscientia Beam, vol. 3(3), pages 81-94.
    6. Sean Harris, 2016. "The Effects of Different Scoring Systems on Upset Percentage and Match Length in Tennis: A Simulation Study," Journal of Sports Research, Conscientia Beam, vol. 3(3), pages 81-94.
    7. D F Percy, 2009. "A mathematical analysis of badminton scoring systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 63-71, January.
    8. James T. Peach & Steven L. Fullerton & Thomas M. Fullerton, 2016. "An empirical analysis of the 2014 Major League Baseball season," Applied Economics Letters, Taylor & Francis Journals, vol. 23(2), pages 138-141, February.
    9. Peter J. Diggle, 2015. "Statistics: a data science for the 21st century," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 793-813, October.
    10. Klaassen F. J G M & Magnus J. R., 2001. "Are Points in Tennis Independent and Identically Distributed? Evidence From a Dynamic Binary Panel Data Model," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 500-509, June.
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    Keywords

    bradley-terry model; maximum likelihood estimation; round-robin tournament; sport performance analysis; strength model;
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