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Comparing Goal-Based and Result-Based Approaches in Modelling Football Outcomes

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  • Leonardo Egidi

    (Università degli Studi di Trieste)

  • Nicola Torelli

    (Università degli Studi di Trieste)

Abstract

Two main approaches are considered when building statistical models for football outcomes: (1) the goal-based approach, where the number of goals scored by two competing teams is modelled, and (2) the result-based approach, where a three-category outcome (win–draw–loss) is modelled. The debate about which approach is preferable is still ongoing, although the general agreement is that any direct comparison between the forecasting abilities of the two approaches should be based on the quality of the forecasts. Alternatively, a greater emphasis can be given to diagnostic measures in order to judge the quality of model specifications, as is more customary in statistical modelling. In this paper, we develop a broad comparison of four possible Bayesian models, focusing on model checking and calibration and then using Markov chain Monte Carlo replications to explore the predictive performance over future matches. Although inconclusive, we believe our set of comparison tools may be beneficial for future scholars in differentiating the two approaches.

Suggested Citation

  • Leonardo Egidi & Nicola Torelli, 2021. "Comparing Goal-Based and Result-Based Approaches in Modelling Football Outcomes," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 801-813, August.
  • Handle: RePEc:spr:soinre:v:156:y:2021:i:2:d:10.1007_s11205-020-02293-z
    DOI: 10.1007/s11205-020-02293-z
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    References listed on IDEAS

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    1. Hatzinger, Reinhold & Dittrich, Regina, 2012. "prefmod: An R Package for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i10).
    2. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    3. 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.
    4. D. Böhning & E. Dietz & P. Schlattmann & L. Mendonça & U. Kirchner, 1999. "The zero‐inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 195-209.
    5. 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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    Cited by:

    1. Igor Viveiros & Henrique Rizzo, 2022. "Ganhando no grito: análise do impacto da pressão social nas decisões da arbitragem em partidas de futebol," Textos para Discussão Cedeplar-UFMG 648, Cedeplar, Universidade Federal de Minas Gerais.

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