IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i23p4587-d992781.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/23/4587/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/23/4587/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    2. Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020. "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
    3. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
    4. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.
    5. Lasek, Jan & Gagolewski, Marek, 2021. "Interpretable sports team rating models based on the gradient descent algorithm," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1061-1071.
    6. P. Gorgi & S. J. Koopman & R. Lit, 2023. "Estimation of final standings in football competitions with a premature ending: the case of COVID-19," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 233-250, March.
    7. Andrei Shynkevich, 2022. "Informational efficiency of football transfer market," Economics Bulletin, AccessEcon, vol. 42(2), pages 1032-1039.
    8. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    9. Jiří LahviÄ ka, 2015. "The Impact of Playoffs on Seasonal Uncertainty in the Czech Ice Hockey Extraliga," Journal of Sports Economics, , vol. 16(7), pages 784-801, October.
    10. Giovanni Angelini & Luca De Angelis, 2017. "PARX model for football match predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(7), pages 795-807, November.
    11. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    12. 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, H. O. Stekler Research Program on Forecasting.
    13. Raffaele Mattera, 2023. "Forecasting binary outcomes in soccer," Annals of Operations Research, Springer, vol. 325(1), pages 115-134, June.
    14. Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.
    15. Luke S. Benz & Michael J. Lopez, 2023. "Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 205-232, March.
    16. Schwarz Wolf, 2012. "Predicting the Maximum Lead from Final Scores in Basketball: A Diffusion Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(4), pages 1-15, November.
    17. Christophe Ley & Yves Dominicy, 2017. "Mutual Point-winning Probabilities (MPW): a New Performance Measure for Table Tennis," Working Papers ECARES ECARES 2017-23, ULB -- Universite Libre de Bruxelles.
    18. 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.
    19. 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.
    20. Ray Bachan & Barry Reilly & Robert Witt, 2014. "Team performance and race: evidence from the English and French national soccer teams," Applied Economics, Taylor & Francis Journals, vol. 46(13), pages 1535-1546, May.

    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:gam:jmathe:v:10:y:2022:i:23:p:4587-:d:992781. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.