Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 17-062/III

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: Football ; Forecasting ; Score-driven models ; Bivariate Poisson ; Skellam ; Ordered probit ; Probabilistic loss function

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Koopman, Siem Jan S.J. ; Lit, Rutger
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/177630
• Last update: 10.03.2025, 11:43 AM CET