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Predicting Match Outcomes in Football by an Ordered Forest Estimator

Daniel Goller (), Michael Knaus (), Michael Lechner and Gabriel Okasa ()

No 1811, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science

Abstract: We predict the probabilities for a draw, a home win, and an away win, for the games of the German Football Bundesliga (BL1) with a new machine-learning estimator using the (large) information available up to that date. We use these individual predictions in order to simulate a league table for every game day until the end of the season. This combination of a (stochastic) simulation approach with machine learning allows us to come up with statements about the likelihood that a particular team is reaching specific places in the final league table (i.e. champion, relegation, etc.). The machine-learning algorithm used, builds on a recent development of an Ordered Random Forest. This estimator generalises common estimators like ordered probit or ordered logit maximum likelihood and is able to recover essentially the same output as the standard estimators, such as the probabilities of the alternative conditional on covariates. The approach is already in use and results for the current season can be found at www.sew.unisg.ch/soccer_analytics.

Keywords: Prediction; Machine Learning; Random Forest; Soccer; Bundesliga (search for similar items in EconPapers)
JEL-codes: Z29 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp and nep-spo
Date: 2018-11
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