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More traditional statistical approaches still dominate this field. The use in sports prediction and the associated betting markets has not received the same amount of attention so far. 1 Applications stretch from financial services to medicine and autonomously driving vehicles. With the revival of long-known techniques in the context of exponentially more extensive calculation capabilities and data availability, “machine learning” is meanwhile part of many areas of science and daily life. The use of model ensembles that combine the predictions from multiple approaches proves to be the most promising choice. This conclusion holds across most tested models, various money management strategies, and for backing the match favorites or outsiders. Returns from applying predictions to the sports betting market are subject to high volatility and mainly negative over the longer term. Irrespective of the used model, most of the relevant information is embedded in the betting markets, and adding other match- and player-specific data does not lead to any significant improvement. The paper shows that the average prediction accuracy cannot be increased to more than about 70%.
#FOOTBALL BETTING SYSTEM INDICATORS PROFESSIONAL#
On the example of tennis, this paper extends previous research by conducting one of the most extensive studies of its kind and applying a wide range of machine learning techniques to male and female professional singles matches. Several attempts have been made to apply machine learning to the prediction of the outcome of professional sports events and to exploit “inefficiencies” in the corresponding betting markets.
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Machine learning and its numerous variants have meanwhile become established tools in many areas of society.