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Forecasting outcomes in tennis matches using within-match betting markets

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  • Easton, Stephen
  • Uylangco, Katherine
Abstract
Klaassen and Magnus (2003) provide a model of the probability of a given player winning a tennis match, with the prediction updated on a point-by-point basis. This paper provides a point-by-point comparison of that model with the probability of a given player winning the match, as implied by betting odds. The predictions implied by the betting odds match the model predictions closely, with an extremely high correlation being found between the model and the betting market. The results for both men's and women's matches also suggest that there is a high level of efficiency in the betting market, demonstrating that betting markets are a good predictor of the outcomes of tennis matches. The significance of service breaks and service being held is anticipated up to four points prior to the end of the game. However, the tendency of players to lose more points than would be expected after conceding a break of service is not captured instantaneously in betting odds. In contrast, there is no evidence of a biased reaction to a player winning a game on service.

Suggested Citation

  • Easton, Stephen & Uylangco, Katherine, 2010. "Forecasting outcomes in tennis matches using within-match betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 564-575, July.
    • Handle: RePEc:eee:intfor:v:26:y::i:3:p:564-575
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    References listed on IDEAS

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    1. repec:reg:rpubli:259 is not listed on IDEAS
    2. Magnus, J.R. & Klaassen, F.J.G.M., 2000. "How to reduce the service dominance in tennis? Empirical results from four years at Wimbledon," Other publications TiSEM 438f231d-7989-463c-a193-f, Tilburg University, School of Economics and Management.
    3. Steven D. Levitt & John A. List, 2007. "Viewpoint: On the generalizability of lab behaviour to the field," Canadian Journal of Economics, Canadian Economics Association, vol. 40(2), pages 347-370, May.
    4. 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.
    5. Richard Borghesi, 2007. "Price Biases in a Prediction Market: NFL Contracts on Tradesports," Journal of Prediction Markets, University of Buckingham Press, vol. 1(3), pages 233-253, December.
    6. Ricard Gil & Steven D. Levitt, 2007. "Testing the Efficiency of Markets in the 2002 World Cup," Journal of Prediction Markets, University of Buckingham Press, vol. 1(3), pages 255-270, December.
    7. Davies, Mark & Pitt, Leyland & Shapiro, Daniel & Watson, Richard, 2005. "Betfair.com:: Five Technology Forces Revolutionize Worldwide Wagering," European Management Journal, Elsevier, vol. 23(5), pages 533-541, October.
    8. Steve Easton & Katherine Uylangco, 2007. "An Examination of In-Play Sports Betting Using One-Day Cricket Matches," Journal of Prediction Markets, University of Buckingham Press, vol. 1(2), pages 93-109, July.
    9. 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.
    10. Steven D. Levitt & John A. List, 2007. "What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 153-174, Spring.
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    Cited by:

    1. 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.
    2. Brown, Alasdair & Reade, J. James, 2019. "The wisdom of amateur crowds: Evidence from an online community of sports tipsters," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1073-1081.
    3. 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.
    4. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    5. 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.
    6. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    7. 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.
    8. 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.
    9. Brown, Alasdair & Yang, Fuyu, 2019. "The wisdom of large and small crowds: Evidence from repeated natural experiments in sports betting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 288-296.

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