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Predicting Match Outcomes in Football by an Ordered Forest Estimator

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  • Goller, Daniel
  • Knaus, Michael C.
  • Lechner, Michael
  • Okasa, Gabriel

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.

Suggested Citation

  • Goller, Daniel & Knaus, Michael C. & Lechner, Michael & Okasa, Gabriel, 2018. "Predicting Match Outcomes in Football by an Ordered Forest Estimator," Economics Working Paper Series 1811, University of St. Gallen, School of Economics and Political Science.
    • Handle: RePEc:usg:econwp:2018:11
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    File URL: http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-1811.pdf
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    References listed on IDEAS

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    Cited by:

    1. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    2. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.

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    More about this item

    Keywords

    Prediction; Machine Learning; Random Forest; Soccer; Bundesliga;
    All these keywords.

    JEL classification:

    • Z29 - Other Special Topics - - Sports Economics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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