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Are differences in ranks good predictors for Grand Slam tennis matches?

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  • del Corral, Julio
  • Prieto-Rodríguez, Juan

Abstract

This paper tests whether the differences in rankings between individual players are good predictors for Grand Slam tennis outcomes. We estimate separate probit models for men and women using Grand Slam tennis match data from 2005 to 2008. The explanatory variables are divided into three groups: a player's past performance, a player's physical characteristics, and match characteristics. We estimate three alternative probit models. In the first model, all of the explanatory variables are included, whereas in the other two specifications, either the player's physical characteristics or the player's past performances are not considered. The accuracies of the different models are evaluated both in-sample and out-of-sample by computing Brier scores and comparing the predicted probabilities with the actual outcomes from the Grand Slam tennis matches from 2005 to 2008 and from the 2009 Australian Open. In addition, using bootstrapping techniques, we also evaluate the out-of-sample Brier scores for the 2005-2008 data.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:26:y::i:3:p:551-563
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    Cited by:

    1. 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.
    2. Kovalchik Stephanie Ann, 2016. "Searching for the GOAT of tennis win prediction," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(3), pages 127-138, September.
    3. Restocchi, Valerio & McGroarty, Frank & Gerding, Enrico & Johnson, Johnnie E.V., 2018. "It takes all sorts: A heterogeneous agent explanation for prediction market mispricing," European Journal of Operational Research, Elsevier, vol. 270(2), pages 556-569.
    4. Alberto Arcagni & Vincenzo Candila & Rosanna Grassi, 2023. "A new model for predicting the winner in tennis based on the eigenvector centrality," Annals of Operations Research, Springer, vol. 325(1), pages 615-632, June.
    5. Fagan Francois & Haugh Martin & Cooper Hal, 2019. "The advantage of lefties in one-on-one sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(1), pages 1-25, March.
    6. Tomi Ovaska & Albert J. Sumell, 2014. "Who Has The Advantage? An Economic Exploration of Winning in Men's Professional Tennis," The American Economist, Sage Publications, vol. 59(1), pages 34-51, May.
    7. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    8. Sylvain Béal & Sylvain Ferrières & Eric Rémila & Phillippe Solal, 2016. "An axiomatization of the iterated h-index and applications to sport rankings," Working Papers 2016-11, CRESE.
    9. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    10. 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.
    11. Stefan M. Herzog & Ralph Hertwig, 2011. "The wisdom of ignorant crowds: Predicting sport outcomes by mere recognition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 58-72, February.
    12. Angelini, Giovanni & Candila, Vincenzo & De Angelis, Luca, 2022. "Weighted Elo rating for tennis match predictions," European Journal of Operational Research, Elsevier, vol. 297(1), pages 120-132.
    13. He, Xue-Zhong & Treich, Nicolas, 2017. "Prediction market prices under risk aversion and heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 105-114.
    14. 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.
    15. Selçuk Özaydın & Thomas Könecke, 2024. "Match-Level Uncertainty in Professional Tennis Revisited—A Novel Approach Applied for the Time Between 2010 and 2019," Journal of Sports Economics, , vol. 25(4), pages 507-532, May.
    16. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
    17. repec:cup:judgdm:v:6:y:2011:i:1:p:58-72 is not listed on IDEAS
    18. 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.
    19. 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 109-118, June.
    20. Halkos, George & Tzeremes, Nickolaos, 2012. "Evaluating professional tennis players’ career performance: A Data Envelopment Analysis approach," MPRA Paper 41516, University Library of Munich, Germany.

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