Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/177630
Authors: 
Koopman, Siem Jan S.J.
Lit, Rutger
Year of Publication: 
2017
Series/Report no.: 
Tinbergen Institute Discussion Paper 17-062/III
Abstract: 
We develop a new dynamic multivariate model for the analysis and the forecasting of football match results in national league competitions. The proposed dynamic model is based on the score of the predictive observation mass function for a high-dimensional panel of weekly match results. Our main interest is to forecast whether the match result is a win, a loss or a draw for each team. To deliver such forecasts, the dynamic model can be based on three different dependent variables: the pairwise count of the number of goals, the difference between the number of goals, or the category of the match result (win, loss, draw). The different dependent variables require different distributional assumptions. Furthermore, different dynamic model specifications can be considered for generating the forecasts. We empirically investigate which dependent variable and which dynamic model specification yield the best forecasting results. In an extensive forecasting study, we consider match results from six large European football competitions and we validate the precision of the forecasts for a period of seven years for each competition. We conclude that our preferred dynamic model for pairwise counts delivers the most precise forecasts and outperforms benchmark and other competing models.
Subjects: 
Football
Forecasting
Score-driven models
Bivariate Poisson
Skellam
Ordered probit
Probabilistic loss function
JEL: 
C32
Document Type: 
Working Paper
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