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A Bradley-Terry type model for forecasting tennis match results

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  • McHale, Ian
  • Morton, Alex

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.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:619-630
    DOI: 10.1016/j.ijforecast.2010.04.004
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    References listed on IDEAS

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    1. David Forrest & Ian Mchale, 2007. "Anyone for Tennis (Betting)?," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 751-768.
    2. 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.
    3. 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.
    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. Stefan Szymanski, 2003. "The Economic Design of Sporting Contests," Journal of Economic Literature, American Economic Association, vol. 41(4), pages 1137-1187, December.
    6. I. Graham & H. Stott, 2008. "Predicting bookmaker odds and efficiency for UK football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 99-109.
    7. Peter Macmillan & Ian Smith, 2007. "Explaining International Soccer Rankings," Journal of Sports Economics, , vol. 8(2), pages 202-213, May.
    8. 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.
    9. 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.
    10. Firth, David, 2005. "Bradley-Terry Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i01).
    11. 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.
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    Citations

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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. Bozóki, Sándor & Csató, László & Temesi, József, 2016. "An application of incomplete pairwise comparison matrices for ranking top tennis players," European Journal of Operational Research, Elsevier, vol. 248(1), pages 211-218.
    3. 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.
    4. 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.
    5. P. Gorgi & Siem Jan (S.J.) Koopman & R. Lit, 2018. "The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model," Tinbergen Institute Discussion Papers 18-009/III, Tinbergen Institute.
    6. 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 1-10, June.
    7. Halkos, George & Tzeremes, Nickolaos, 2012. "Evaluating professional tennis players’ career performance: A Data Envelopment Analysis approach," MPRA Paper 41516, University Library of Munich, Germany.
    8. Kovalchik Stephanie Ann, 2016. "Is there a Pythagorean theorem for winning in tennis?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 43-49, March.
    9. Ruud H. Koning & Ian G. McHale, 2012. "Estimating Match and World Cup Winning Probabilities," Chapters,in: International Handbook on the Economics of Mega Sporting Events, chapter 11 Edward Elgar Publishing.

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