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Using ELO ratings for match result prediction in association football

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Listed author(s):
  • 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/pii/S0169-2070(09)00170-8
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    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
    Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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

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    Cited by:

    1. 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.
    2. Roberto Gásquez & Vicente Royuela, 2016. "The Determinants of International Football Success: A Panel Data Analysis of the Elo Rating," Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 125-141, 06.
    3. L.F.M. Groot & J. Ferwerda, 2014. "Soccer jersey sponsors and the world cup," Working Papers 14-07, Utrecht School of Economics.
    4. 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.
    5. Hvattum Lars Magnus, 2015. "Playing on artificial turf may be an advantage for Norwegian soccer teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(3), pages 183-192, September.
    6. Siem Jan (S.J.) Koopman & Rutger Lit, 2017. "Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models," Tinbergen Institute Discussion Papers 17-062/III, Tinbergen Institute.

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