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Using ELO ratings for match result prediction in association football

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  • Hvattum, Lars Magnus
  • Arntzen, Halvard
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    Abstract

    Sports betting markets are becoming increasingly competitive. These markets are of interest when testing new ideas for quantitative prediction models. This paper examines the value of assigning ratings to teams based on their past performance in order to predict match results in association football. The ELO rating system is used to derive covariates that are then used in ordered logit regression models. In order to make informed statements about the relative merit of the ELO-based predictions compared to those from a set of six benchmark prediction methods, both economic and statistical measures are used. The results of large-scale computational experiments are presented.

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    File URL: http://www.sciencedirect.com/science/article/B6V92-4XRBGX5-1/2/b041cebf01c2941aa642cf9376ce772e
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    Bibliographic Info

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

    Volume (Year): 26 (2010)
    Issue (Month): 3 (July)
    Pages: 460-470

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    Handle: RePEc:eee:intfor:v:26:y::i:3:p:460-470

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

    Related research

    Keywords: Sports forecasting Loss function Evaluating forecasts Rating Ordered logit;

    References

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    1. David Forrest & Robert Simmons, 2008. "Sentiment in the betting market on Spanish football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 119-126.
    2. David Johnstone, 2007. "Economic Darwinism: Who has the Best Probabilities?," Theory and Decision, Springer, vol. 62(1), pages 47-96, February.
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    4. Tim Kuypers, 2000. "Information and efficiency: an empirical study of a fixed odds betting market," Applied Economics, Taylor & Francis Journals, vol. 32(11), pages 1353-1363.
    5. Raymond D. Sauer, 1998. "The Economics of Wagering Markets," Journal of Economic Literature, American Economic Association, vol. 36(4), pages 2021-2064, December.
    6. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    7. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    8. 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.
    9. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    10. Boulier, Bryan L. & Stekler, H. O., 2003. "Predicting the outcomes of National Football League games," International Journal of Forecasting, Elsevier, vol. 19(2), pages 257-270.
    11. Bryan Boulier & H. O. Stekler & Sarah Amundson, 2006. "Testing the efficiency of the National Football League betting market," Applied Economics, Taylor & Francis Journals, vol. 38(3), pages 279-284.
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    Cited by:
    1. J. James Reade & Sachiko Akie, 2013. "Using Forecasting to Detect Corruption in International Football," Working Papers 2013-005, The George Washington University, Department of Economics, Research Program on Forecasting.
    2. Štrumbelj, Erik & Vračar, Petar, 2012. "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting, Elsevier, vol. 28(2), pages 532-542.

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