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A Bradley-Terry type model for forecasting tennis match results

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  • McHale, Ian
  • Morton, Alex
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    Abstract

    The paper introduces a model for forecasting match results for the top tier of men's professional tennis, the ATP tour. Employing a Bradley-Terry type model, and utilising the data available on players' past results and the surface of the contest, we predict match winners for the coming week's matches, having updated the model parameters to take the previous week's results into account. We compare the model to two logit models: one using official rankings and another using the official ranking points of the two competing players. Our model provides superior forecasts according to each of five criteria measuring the predictive performance, two of which relate to betting returns.

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    File URL: http://www.sciencedirect.com/science/article/B6V92-50KBP51-1/2/2e31d701fe1607a80c1a503030658ceb
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    Bibliographic Info

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

    Volume (Year): 27 (2011)
    Issue (Month): 2 (April)
    Pages: 619-630

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    Handle: RePEc:eee:intfor:v:27:y::i:2:p:619-630

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Bradley-Terry model Logit Ranking evaluation Sport Betting;

    References

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    1. 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.
    2. I. Graham & H. Stott, 2008. "Predicting bookmaker odds and efficiency for UK football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 99-109.
    3. Dixon, Mark J. & Pope, Peter F., 2004. "The value of statistical forecasts in the UK association football betting market," International Journal of Forecasting, Elsevier, vol. 20(4), pages 697-711.
    4. David Firth, . "Bradley-Terry Models in R," Journal of Statistical Software, American Statistical Association, vol. 12(i01).
    5. David Forrest & Ian Mchale, 2007. "Anyone for Tennis (Betting)?," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 751-768.
    6. Scheibehenne, Benjamin & Broder, Arndt, 2007. "Predicting Wimbledon 2005 tennis results by mere player name recognition," International Journal of Forecasting, Elsevier, vol. 23(3), pages 415-426.
    7. Peter Macmillan & Ian Smith, 2006. "Explaining International Soccer Rankings," CRIEFF Discussion Papers 0612, Centre for Research into Industry, Enterprise, Finance and the Firm.
    8. 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.
    9. del Corral, Julio & Prieto-RodrĂ­guez, 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.
    10. 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.
    11. Stefan Szymanski, 2003. "The Economic Design of Sporting Contests," Journal of Economic Literature, American Economic Association, vol. 41(4), pages 1137-1187, December.
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    Cited by:
    1. Baker, Rose D. & McHale, Ian G., 2014. "A dynamic paired comparisons model: Who is the greatest tennis player?," European Journal of Operational Research, Elsevier, vol. 236(2), pages 677-684.
    2. 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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