What is it about?
This article is focused to build and compare methods for the prediction of the final outcomes of basketball games. We analyzed data from four different European tournaments: Euroleague, Eurocup, Greek Basket League and Spanish Liga ACB. The data-set consists of information collected from box scores of 5214 games for the period of 2013-2018.
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Why is it important?
We define many informative features which are included new performance indicators constructed by using historical statistics, key performance indicators, and measurements from three rating systems (Elo, PageRank, pi-rating). These new game features are improving our predictions efficiently and can be easily obtained in any basketball league. Our predictions were obtained by implementing three different statistics and machine learning algorithms: logistic regression, random forest, and extreme gradient boosting trees. Moreover, we report predictions based on the combination of these algorithms (ensemble learning).
Perspectives
Basketball is one of the most popular and exciting sports in the world and attracting millions of fans. However, predicting the outcomes of basketball games is not an easy task, as there are many factors that influence the performance and behavior of teams and players. In this paper, we present a novel approach to predict the final outcomes of basketball games using various data sources and machine learning algorithms. We hope that our paper will inspire further research on this topic, as well as other sports domains. We also hope that our paper will spark some discussions and debates among basketball enthusiasts about the factors that influence game outcomes.
Lampis Tzai
Athens University of Economics and Business
Read the Original
This page is a summary of: Predictions of european basketball match results with machine learning algorithms, Journal of Sports Analytics, July 2023, IOS Press,
DOI: 10.3233/jsa-220639.
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