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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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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
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    4. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    5. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    6. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
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    Cited by:

    1. 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.

    More about this item

    Keywords

    Prediction; Machine Learning; Random Forest; Soccer; Bundesliga;

    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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