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Developing an improved tennis ranking system

Author

Listed:
  • Irons David J.
  • Buckley Stephen
  • Paulden Tim

    (Atass Sports)

Abstract

Sports ranking systems are often viewed as inadequate for judging the quality of the teams or players involved. Meanwhile, statistical models have been shown to produce more accurate ratings for those competitors, based on their ability to forecast future results. However, whilst predictive power is a desirable property of any official ranking system, these systems must also be fair, transparent and insensitive to bias. Additional requirements may also be required, such as promoting major tournaments and deciding seedings. By considering rankings for ATP tennis players, we propose that statistical models can be used to improve the existing ranking system, in such a way that the resulting rankings are fair and usable by the governing body. In many cases, there is a trade-off between predictive power and other desirable properties, and so compromise is required to produce a system that can be implemented successfully.

Suggested Citation

  • Irons David J. & Buckley Stephen & Paulden Tim, 2014. "Developing an improved tennis ranking system," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-10, June.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:10:n:11
    DOI: 10.1515/jqas-2013-0101
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    References listed on IDEAS

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    1. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630, April.
    2. Mark E. Glickman, 1999. "Parameter Estimation in Large Dynamic Paired Comparison Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 377-394.
    3. Boulier, Bryan L. & Stekler, H. O., 1999. "Are sports seedings good predictors?: an evaluation," International Journal of Forecasting, Elsevier, vol. 15(1), pages 83-91, February.
    4. del Corral, Julio & Prieto-Rodríguez, Juan, 2010. "Are differences in ranks good predictors for Grand Slam tennis matches?," International Journal of Forecasting, Elsevier, vol. 26(3), pages 551-563, July.
    5. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630.
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    Cited by:

    1. Bozóki, Sándor & Csató, László & Temesi, József, 2016. "An application of incomplete pairwise comparison matrices for ranking top tennis players," European Journal of Operational Research, Elsevier, vol. 248(1), pages 211-218.
    2. P. Gorgi & Siem Jan (S.J.) Koopman & R. Lit, 2018. "The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model," Tinbergen Institute Discussion Papers 18-009/III, Tinbergen Institute.
    3. Silvia Montagna & Vanessa Orani & Raffaele Argiento, 2021. "Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 573-604, June.
    4. Vaughan Williams Leighton & Liu Chunping & Dixon Lerato & Gerrard Hannah, 2021. "How well do Elo-based ratings predict professional tennis matches?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 91-105, June.
    5. Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.

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