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On the Élö–Runyan–Poisson–Pearson Method to Forecast Football Matches

Author

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  • José Daniel López-Barrientos

    (Facultad de Ciencias Actuariales, Universidad Anáhuac México, Naucalpan de Juárez 52786, Mexico)

  • Damián Alejandro Zayat-Niño

    (Facultad de Ciencias Actuariales, Universidad Anáhuac México, Naucalpan de Juárez 52786, Mexico)

  • Eric Xavier Hernández-Prado

    (Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla 72000, Mexico)

  • Yolanda Estudillo-Bravo

    (Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla 72000, Mexico)

Abstract

This is a work about football. In it, we depart from two well-known approaches to forecast the outcome of a football match (or even a full tournament) and take advantage of their strengths to develop a new method of prediction. We illustrate the Élö–Runyan rating system and the Poisson technique in the English Premier League and we analyze their accuracies with respect to the actual results. We obtained an accuracy of 84.37% for the former, and 79.99% for the latter in this first exercise. Then, we present a criticism of these methods and use it to complement the aforementioned procedures, and hence, introduce the so-called Élö–Runyan–Poisson–Pearson method, which consists of adopting the distribution that best fits the historical distribution of goals to simulate the score of each match. Finally, we obtain a Monte Carlo-based forecast of the result. We test our mechanism to backcast the World Cup of Russia 2018, obtaining an accuracy of 87.09%; and forecast the results of the World Cup of Qatar 2022.

Suggested Citation

  • José Daniel López-Barrientos & Damián Alejandro Zayat-Niño & Eric Xavier Hernández-Prado & Yolanda Estudillo-Bravo, 2022. "On the Élö–Runyan–Poisson–Pearson Method to Forecast Football Matches," Mathematics, MDPI, vol. 10(23), pages 1-29, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4587-:d:992781
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    References listed on IDEAS

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    1. Wheatcroft, Edward, 2021. "Forecasting football matches by predicting match statistics," LSE Research Online Documents on Economics 111495, London School of Economics and Political Science, LSE Library.
    2. Stefani Ray & Pollard Richard, 2007. "Football Rating Systems for Top-Level Competition: A Critical Survey," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-22, July.
    3. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    4. 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.
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