Comparing the Performance of Basketball Players with Decision Trees and TOPSIS

Çene E., Parim C., Özkan B.

International Conference on Data Science and Applications, Yalova, Turkey, 4 - 07 October 2018, pp.117-118

  • Publication Type: Conference Paper / Summary Text
  • City: Yalova
  • Country: Turkey
  • Page Numbers: pp.117-118
  • Yıldız Technical University Affiliated: Yes


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 decisionmaking 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.