| By: |
Siem Jan S.J. Koopman (VU Amsterdam, The Netherlands; CREATES, Aarhus University, Denmark; Tinbergen Institute, The Netherlands);
Rutger Lit (VU Amsterdam, The Netherlands) |
| 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. |
| Keywords: |
Football; Forecasting; Score-driven models; Bivariate Poisson; Skellam; Ordered probit; Probabilistic loss function |
| JEL: |
C32 |
| Date: |
2017–07–05 |
| URL: |
https://d.repec.org/n?u=RePEc:tin:wpaper:20170062 |