Modelling the dependence of goals scored by opposing teams in international soccer matches

By Ian G. McHale, Philip A. Scarf. 2011

58 citations recorded by the source corpus.

This paper presents models for the number of goals scored by opposing teams in international soccer matches. The bivariate discrete distributions employed are defined in terms of the marginal distributions and a dependence copula. This copula representation allows dependence in the bivariate distribution to be modelled in a flexible manner by specifying a suitable family of copula functions and fitting this to the bivariate data using maximum likelihood. Marginal means are modelled with match covariates. The nature of the dependence in the number of goals scored is complex, and we develop the idea that the strength of dependence depends on the competitive balance of a match. Our analysis suggests that for games between closely matched teams, the overall dependence is low, and that the dependence becomes increasingly negative as the competitiveness of a match decreases. In this way, we relate dependence to competitive balance and suggest a method to measure the latter quantity. The models developed here may also offer better forecasts than those offered by match outcome models with independent marginal distributions.

Subfields: Economics and Econometrics, Nature and Landscape Conservation, Plant Science

View Paper: https://www.semanticscholar.org/paper/d674df7b704fd671bd5e709d4a676896b15ad387

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Citations in the corpus

A bivariate Weibull count model for forecasting association football scores (2017, methodology): “For speed, we implemented these computations in C++, though McShane et al. (2008) were able to perform the computations in Microsoft Excel.”
A numerical study of tournament structure and seeding policy for the soccer World Cup Finals (2011, methodology): “McHale and Scarf (2010) predict match outcomes using a bivariate negative binomial distribution with general dependence structure.”
Ranking soccer teams on basis of their current strength: a comparison of maximum likelihood approaches (2017, background): “…in this ﬁeld: Dixon and Coles (1997) added an additional parameter to adjust for the probabilities on low scoring games (0-0, 1-0, 0-1 and 1-1), McHale and Scarf (2011) investigated copula dependence structures, and Boshnakov et al. (2017) recently proposed a copula-based bivariate Weibull…”
Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014 (2015, unknown): “For example, in an unregularized setting McHale and Scarf (2006, 2011) model the dependence by using bivariate discrete distributions and by specifying a suitable family of dependence copulas.”