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Dynamic Bayesian forecasting of English Premier League match results with the Skellam distribution

Author

Listed:
  • Robert C. Smit

    (Vrije Universiteit Amsterdam, The Netherlands)

  • Francesco Ravazzolo

    (Free University of Bolzano‐Bozen, Faculty of Economics and Management, Italy)

  • Luca Rossini

    (Queen Mary University of London, United Kingdom and Vrije Universiteit Amsterdam, The Netherlands)

Abstract

Due to teams trading players and/or changing their manager and players getting either in or out of injuries and other factors the abilities of teams to score or to prevent an opponent from scoring changes throughout the season in most if not all sports and association football is no exception. As such, we developed a dynamic model based on the Poisson difference distribution, called Skellam, where the scoring abilities are changing over time and are different across teams. The model is developed in a Bayesian framework and is fitted using the Stan modelling language. In this paper, we introduce a unique method used to handle promotion and relegation within the league. The model uses 3 different seasons and the forecasting ability has been measured and validated on the 2018-2019 English Premier League season. As a result, the model predicts the outcome of the matches correctly about 60% of the time. Moreover, we find that the model under-performs somewhat with the best performing, worst performing teams and some of the promotion teams, which could be attributed to both the fact that the season was an outlier in regards to performance for these teams and to the possibility that the hierarchical model may have caused shrinkage.

Suggested Citation

  • Robert C. Smit & Francesco Ravazzolo & Luca Rossini, 2020. "Dynamic Bayesian forecasting of English Premier League match results with the Skellam distribution," BEMPS - Bozen Economics & Management Paper Series BEMPS72, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps72
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    References listed on IDEAS

    as
    1. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
    2. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
    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. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    5. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
    6. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bayesian hierarchical models; dynamic models; English Premier League; football data; Skellam distribution;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • Z20 - Other Special Topics - - Sports Economics - - - General

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