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Ranking the performance of tennis players: an application to women’s professional tennis

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  • Blackburn McKinley L.

    (University of South Carolina – Economics, 1705 College St., Columbia, SC 29206, USA)

Abstract

Paired-comparison models have been previously used in the literature to assess the relative performance of tennis players over a given period of time. In this paper, I discuss how the rankings of tennis players can be modified to address variations in the importance of tennis tournaments, and concerns about under-participation over the tennis season. The methods are applied to the 2011 WTA season, where the WTA-ranked number one player Caroline Wozniacki was often criticized for not being the true top player. The alternative rankings proposed here indicate that Petra Kvitova was the top player in 2011, with Serena Williams a close second. These rankings do appear to perform better in predicting match probabilities in early 2012 than methods based on the official rankings.

Suggested Citation

  • Blackburn McKinley L., 2013. "Ranking the performance of tennis players: an application to women’s professional tennis," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(4), pages 367-378, December.
  • Handle: RePEc:bpj:jqsprt:v:9:y:2013:i:4:p:367-378:n:4
    DOI: 10.1515/jqas-2013-0006
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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. Filippo Radicchi, 2011. "Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    3. Klaassen, Franc J. G. M. & Magnus, Jan R., 2003. "Forecasting the winner of a tennis match," European Journal of Operational Research, Elsevier, vol. 148(2), pages 257-267, July.
    4. Stefani Ray & Pollard Richard, 2007. "Football Rating Systems for Top-Level Competition: A Critical Survey," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-22, July.
    5. 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.
    6. 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.
    7. 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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