Title: Discrete Weibull regression for modelling football outcomes

Authors: Alessandro Barbiero

Addresses: Department of Economics, Management and Quantitative Methods, Università di Milano, via Conservatorio 7, 20122 Milan, Italy

Abstract: We propose the use of the discrete Weibull distribution for modelling football match results, as an alternative to existing Poisson and generalised Poisson models. The number of goals scored by the two teams playing a football match are regarded as a pairwise observation and are modelled first through two independent discrete Weibull variables, and then through two dependent discrete Weibull variables, using a copula approach that accommodates non-null correlation. The parameters of the bivariate discrete Weibull distributions are assumed to depend on covariates such as the attack and defence abilities of the two teams and the 'home effect'. Several discrete Weibull regression models are proposed and then applied to the 2015-2016 Italian Serie A. Even if the interpretation of parameters is less immediate than in the case of bivariate Poisson models, nevertheless these models represent a suitable alternative, which can be applied also in other fields than sport data analysis.

Keywords: count data; count regression model; Frank copula; Poisson distribution; sport analytics.

DOI: 10.1504/IJBIDM.2020.108033

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.1, pp.76 - 100

Received: 24 Jul 2017
Accepted: 07 Dec 2017

Published online: 07 Apr 2020 *

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