Prediction of the FIFA World Cup 2018 – A random forest approach with an emphasis on estimated team ability parameters


Abstract in English

In this work, we compare three different modeling approaches for the scores of soccer matches with regard to their predictive performances based on all matches from the four previous FIFA World Cups 2002 – 2014: Poisson regression models, random forests and ranking methods.

References used

Groll, A., G. Schauberger, and G. Tutz (2015): “Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: an application to the FIFA World Cup 2014,” Journal of Quantitative Analysis in Sports, 11, 97–115.
Groll, A., T. Kneib, A. Mayr, and G. Schauberger (2018): “On the dependency of soccer scores – A sparse bivariate Poisson model for the UEFA European Football Championship 2016,” Statistical Modelling, to appear.
Groll, A. and J. Abedieh (2013): “Spain retains its title and sets a new record - generalized linear mixed models on European football championships,” Journal of Quantitative Analysis in Sports, 9, 51–66
Gneiting, T. and A. Raftery (2007): “Strictly proper scoring rules, prediction, and estimation,” Journal of the American Statistical Association, 102, 359–376

Download