Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Number of Results 1

WP(22/2021)370. Predicting football outcomes from Spanish league using machine learning models

Authors: Lewandowski Michał, Chlebus Marcin
High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with histori…

WP370. Predicting football outcomes from Spanish league using machine learning models

High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with historical forecasts extracted from bookmakers, which knowledge is commonly considered to be deep and high-quality. The aim of the paper was to design models with the highest possible predictive performances, get results close to bookmakers or even building better estimators. The work included detailed feature engineering based on previous achievements of this domain and own proposals. A built and selected set of variables was used with four machine learning methods, namely Random Forest, AdaBoost, XGBoost and CatBoost. The algorithms were compared based on: Area Under the Curve (AUC) and Ranked Probability Score (RPS). RPS was used as a benchmark in the comparison of estimated probabilities from trained models and forecasts from bookmakers' odds. For a deeper understanding and explanation of the demonstrated methods, which are considered as black-box approaches, Permutation Feature Importance (PFI) was used to evaluate the impacts of individual variables. Features extracted from bookmakers odds’ occurred the most important in terms of PFI. Furthermore, XGBoost achieved the best results on the validation set (RPS equals 0.1989), which obtained similar predictive power to bookmakers' odds (their RPS between 0.1977 and 0.1984). Results of the trained estimators were promising and this article showed that competition with bookmakers is possible using demonstrated techniques.


Lewandowski Michał, Chlebus Marcin
predicting football outcomes machine learning betting adaboost random forest xgboost catboost ranked probability score auc permutation feature importance
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