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Abstract
In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.
Highlights
In recent decades, football has continued to draw worldwide attention from people of various ages and social situations
This manuscript aims to predict the results of football matches based on different machine learning approaches
As a strong regression model we introduce the weighted ensemble ALL, which integrates the information of the approaches Random forest (RAF), BOO, Support vector machine (SVM) and Linear regression (LIR)
Summary
Football has continued to draw worldwide attention from people of various ages and social situations. The motivation is on the one hand driven by the admiration of other enthusiasts, on the other hand monetary rewards serve as an incentive system This manuscript introduces a methodology for estimating the results of football matches using techniques from the field of machine learning. The corresponding data base consists of a large number of features which incorporate game characteristics as well as proportions of all soccer players from both teams. For this purpose, a comparison of different approaches was conducted to assess whether more complex algorithms are capable of better predicting football betting. The out-of-sample results of our statistical arbitrage showed continuously positive returns over the entire period
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