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Forecasting outcomes in tennis matches using within-match betting markets

  • Easton, Stephen
  • Uylangco, Katherine
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    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.

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    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 26 (2010)
    Issue (Month): 3 (July)
    Pages: 564-575

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    Handle: RePEc:eee:intfor:v:26:y::i:3:p:564-575
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    1. 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.
    2. John List & Steven Levitt, 2007. "Viewpoint: On the generalizability of lab behaviour to the field," Artefactual Field Experiments 00001, The Field Experiments Website.
    3. 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.
    4. 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.
    5. Klaassen, F.J.G.M. & Magnus, J.R., 2001. "Forecasting the Winner of a Tennis Match," Discussion Paper 2001-38, Tilburg University, Center for Economic Research.
    6. 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.
    7. 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.
    8. Davies, Mark & Pitt, Leyland & Shapiro, Daniel & Watson, Richard, 2005. " Five Technology Forces Revolutionize Worldwide Wagering," European Management Journal, Elsevier, vol. 23(5), pages 533-541, October.
    9. repec:reg:rpubli:259 is not listed on IDEAS
    10. 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.
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