Modelling soccer matches using bivariate discrete distributions with general dependence structure

By Ian G. McHale, Philip A. Scarf. 2007

74 citations recorded by the source corpus.

In this paper copulas are used to generate bivariate discrete distributions. These distributions are fitted to soccer data from the English Premier League. An interesting aspect of these data is that the primary variable of interest, the discrete pair shots‐for and shots‐against, exhibits negative dependence; thus, in particular, we apply bivariate Poisson‐related distributions that allow such dependence. The paper focuses on Archimedian copulas, for which the dependence structure is fully determined by a one‐dimensional projection that is invariant under marginal transformations. Diagnostic plots for copula fit based on this projection are adapted to deal with discrete variables. Covariates relating to within‐match contributions such as numbers of passes and tackles are introduced to explain variability in shot outcomes. The results of this analysis would appear to support the notion that playing the ‘beautiful game’ is an effective strategy—more passes and crosses contribute to more effective play and more shots on the goal.

Subfields: Artificial Intelligence, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/e41ac5835621eb7a110cd13e8b06e314098c4a0e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Aristidis K. Nikoloulopoulos
  2. Philip A. Scarf
  3. Ian G. McHale
  4. Dimitris Karlis
  5. Xanthi Pedeli

In Economics and Econometrics

  1. Aristidis K. Nikoloulopoulos
  2. Philip A. Scarf
  3. Ian G. McHale
  4. Haijun Li
  5. Muhammad Mat Yusof

In Nature and Landscape Conservation

  1. Aristidis K. Nikoloulopoulos
  2. Philip A. Scarf
  3. Ian G. McHale
  4. Muhammad Mat Yusof

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