Abstract:

In this study, team statistics in 1358 EuroLeague basketball matches played between 2016-2017 and 2020-2021 seasons were taken into account and it was tried to determine which of these team statistics had the most impact on the winner of the match. The matches were divided into three groups as close, balanced and unbalanced games according to the score differences indicated by the k-means cluster analysis result. K nearest neighbor, naive bayes, logistic regression, support vector machines, decision tree, random forest and artificial neural network algorithms were applied to both these three groups and all matches, and the most effective algorithms were found to be logistic regression, support vector machines and artificial neural networks. These three algorithms can predict correctly the match result for all matches with approximately 84% accuracy. This rate decreased to 79% in close matches, increased to 97% in balanced matches and to 100% in unbalanced matches. The variables that have the most influence on the outcome of the match are defensive rebounds, true shooting percentage, steals, turnovers, offensive rebounds and free throw attempts. The results give an idea on the issues that the teams should focus on while determining their in-match strategies and help them determine their strategy by focusing on these issues.

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