IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v27y2011i2p619-630.html
   My bibliography  Save this article

A Bradley-Terry type model for forecasting tennis match results

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207010001019
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijforecast.2010.04.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. I. Graham & H. Stott, 2008. "Predicting bookmaker odds and efficiency for UK football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 99-109.
    6. 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.
    7. 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.
    8. Firth, David, 2005. "Bradley-Terry Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i01).
    9. 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.
    10. Stefan Szymanski, 2010. "The Economic Design of Sporting Contests," Palgrave Macmillan Books, in: The Comparative Economics of Sport, chapter 1, pages 1-78, Palgrave Macmillan.
    11. Peter Macmillan & Ian Smith, 2007. "Explaining International Soccer Rankings," Journal of Sports Economics, , vol. 8(2), pages 202-213, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    3. 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.
    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. Halkos, George & Tzeremes, Nickolaos, 2012. "Evaluating professional tennis players’ career performance: A Data Envelopment Analysis approach," MPRA Paper 41516, University Library of Munich, Germany.
    6. Julio del Corral, 2009. "Competitive Balance and Match Uncertainty in Grand-Slam Tennis," Journal of Sports Economics, , vol. 10(6), pages 563-581, December.
    7. 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.
    8. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    9. 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.
    10. 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.
    11. 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.
    12. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, vol. 9(1), pages 1-9, January.
    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. 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.
    15. 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.
    16. 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.
    17. Berlinschi, Ruxanda & Schokkaert, Jeroen & Swinnen, Johan, 2013. "When drains and gains coincide: Migration and international football performance," Labour Economics, Elsevier, vol. 21(C), pages 1-14.
    18. 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.
    19. Thierry Lallemand & Robert Plasman & François Rycx, 2008. "Women and Competition in Elimination Tournaments," Journal of Sports Economics, , vol. 9(1), pages 3-19, February.
    20. David Winkelmann & Marius Ötting & Christian Deutscher & Tomasz Makarewicz, 2024. "Are Betting Markets Inefficient? Evidence From Simulations and Real Data," Journal of Sports Economics, , vol. 25(1), pages 54-97, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:619-630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.