Regression models for forecasting goals and match results in association football

• Published in: International journal of forecasting Vol. 21; no. 2; pp. 331 - 340
• Main Author: Goddard, John
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-04-2005; Elsevier; Elsevier Sequoia S.A
• Series: International Journal of Forecasting
• Subjects: Bivariate Poisson; Football; Football match results; Forecasting; Ordered probit; Regression analysis; Soccer; Studies; Ordered probit; Football match results; Bivariate Poisson
• ISSN: 0169-2070; 1872-8200
• DOI: 10.1016/j.ijforecast.2004.08.002

In the previous literature, two approaches have been used to model match outcomes in association football (soccer): first, modelling the goals scored and conceded by each team; and second, modelling win–draw–lose match results directly. There have been no previous attempts to compare the forecasting performance of these two types of model. This paper aims to fill this gap. Bivariate Poisson regression is used to estimate forecasting models for goals scored and conceded. Ordered probit regression is used to estimate forecasting models for match results. Both types of models are estimated using the same 25-year data set on English league football match outcomes. The best forecasting performance is achieved using a ‘hybrid’ specification, in which goals-based team performance covariates are used to forecast win–draw–lose match results. However, the differences between the forecasting performance of models based on goals data and models based on results data appear to be relatively small.