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

Piotr Borowski () and Marcin Chlebus ()
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Piotr Borowski: Faculty of Economic Sciences, University of Warsaw

No 2021-13, Working Papers from 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.

Keywords: horse racing prediction; racetrack betting; Thoroughbred and Arabian flat racing; machine learning; Variable Importance (search for similar items in EconPapers)
JEL-codes: C45 C53 C55 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp and nep-spo
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https://www.wne.uw.edu.pl/index.php/download_file/6530/ First version, 2021 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2021-13

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