Predictions of European Basketball Match Results
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.
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).