Comparing the performance of basketball players with decision trees and TOPSIS

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Erhan Çene
Coşkun Parim
Batuhan Özkan

Abstract

In this study, individual game statistics for basketball players from Euroleague 2017-2018 season are analysed with Decision Trees and Technique for Order-Preference by Similarity to Ideal Solution (TOPSIS) methods. The aim of this study is to create an alternative ranking system to find the best and the worst performing players in each position eg. Guards, forwards and centers. Decision trees are a supervised learning method used for classification and regression. The aim of the decision trees is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. On the other side, TOPSIS is another method to construct a ranking system by using a multi-criteria decision-making system. All the individual statistics such as points, rebounds, assists, steals, blocks, turnovers, free throw percentage and fouls are used to construct the rankings of players. Both decision trees and TOPSIS results are compared with the Performace Index Rating (PIR) index of players which is a single number expressing the performance of the player. Comparing these 3 measures revealed the over and underperformers in the Euroleague for the 2017-2018 season. The results of individual players performance are visualized with the proper methods such as Chernoff's faces.


 

Article Details

How to Cite
[1]
E. Çene, C. Parim, and B. Özkan, “Comparing the performance of basketball players with decision trees and TOPSIS”, DataSCI, vol. 1, no. 1, pp. 21-28, Dec. 2018.
Section
Research Articles