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An exploration of predictive football modelling

Author

Listed:
  • Pearson Mitchell

    (School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, Australia)

  • Jr Glen Livingston

    (School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, Australia)

  • King Robert

    (School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, Australia)

Abstract

Predictive football modelling has become progressively popular over the last two decades. Due to this, numerous studies have proposed different types of statistical models to predict the outcome of a football match. This study provides a review of three different models published in the academic literature and then implements these on recent match data from the top football leagues in Europe. These models are then compared utilising the rank probability score to assess their predictive capability. Additionally, a modification is proposed which includes the travel distance of the away team. When tested on football leagues from both Australia and Russia, it is shown to improve predictive capability according to the rank probability score.

Suggested Citation

  • Pearson Mitchell & Jr Glen Livingston & King Robert, 2020. "An exploration of predictive football modelling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 27-39, March.
    • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:1:p:27-39:n:5
    DOI: 10.1515/jqas-2019-0075
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    References listed on IDEAS

    as
    1. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
    2. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
    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.
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