This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible xtures. The empirical evaluation until the seminals shows that the conceptually simple approach proves accurate for countries with historical data.
© Copyright 2016 Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2016 workshop. Published by Department of Computer Science, KU Leuven. All rights reserved.