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Machine learning in the prediction of flat horse racing results in Poland

Author

Listed:
  • Piotr Borowski

    (Faculty of Economic Sciences, University of Warsaw)

  • Marcin Chlebus

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

Horse racing was the source of many researchers considerations who studied market efficiency and applied complex mathematic formulas to predict their results. We were the first who compared the selected machine learning methods to create a profitable betting strategy for two common bets, Win and Quinella. The six classification algorithms under the different betting scenarios were used, namely Classification and Regression Tree (CART), Generalized Linear Model (Glmnet), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Neural Network (NN) and Linear Discriminant Analysis (LDA). Additionally, the Variable Importance was applied to determine the leading horse racing factors. The data were collected from the flat racetracks in Poland from 2011-2020 and described 3,782 Arabian and Thoroughbred races in total. We managed to profit under specific circumstances and get a correct bets ratio of 41% for the Win bet and over 36% for the Quinella bet using LDA and Neural Networks. The results demonstrated that it was possible to bet effectively using the chosen methods and indicated a possible market inefficiency.

Suggested Citation

  • Piotr Borowski & Marcin Chlebus, 2021. "Machine learning in the prediction of flat horse racing results in Poland," Working Papers 2021-13, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2021-13
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/6530/
    File Function: First version, 2021
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    horse racing prediction; racetrack betting; Thoroughbred and Arabian flat racing; machine learning; Variable Importance;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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