Comparing the performance of basketball players with decision trees and TOPSIS

Year 2018, Volume: 1 Issue: 1, 21 - 28, 26.12.2018

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.

Keywords

Decision Trees, Multi-Criteria Decision Making, Sports Statistics, Data mining, Multivariate Statistics

References

• [1] B. F. Tunç, A. Beşkese ve T. S. Kaya, “TOPSIS method on player selection in NBA,” 12th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology” TMT 2008, 2008, ss. 401–404.
• [2] W. W. Cooper, J. L. Ruiz ve I. Sirvent, “Selecting non-zero weights to evaluate effectiveness of basketball players with DEA,” Eur. J. Oper. Res., cilt 195, sayı 2, ss. 563–574, 2009.
• [3] J. Piette, L. Pham ve S. Anand, “Evaluating basketball player performance via statistical network modeling,” MIT Sloan Sports Analytics Conference, 2011, no. June, ss. 1–11.
• [4] R. K. Mavi, N. K. Mavi ve L. Kiani, “Ranking football teams with AHP and TOPSIS methods,” Int. J. Decis. Sci. Risk Manag., cilt 4, sayı 1–2, ss. 108–126, 2012.
• [5] S. Radovanovic, M. Radojicic, V. Jeremic ve G. Savic, “A novel approach in evaluating efficiency of basketball players,” Manag. - J. theory Pract. Manag., cilt 18, sayı 67, ss. 37–46, 2013.
• [6] C. C. Chen, Y. T. Lee ve C. M. Tsai, “Professional baseball team starting pitcher selection using AHP and topsis methods,” Int. J. Perform. Anal. Sport, cilt 14, sayı 2, ss. 545–563, 2014.
• [7] H. Changwu, “Application of the TOPSIS method and gray correlation model in the competitiveness evaluation of basketball teams,” Comput. Model. New Technol., cilt 18, sayı 12C, ss. 833–837, 2014.
• [8] P. Moreno ve S. Lozano, “A network DEA assessment of team efficiency in the NBA,” Ann. Oper. Res., cilt 214, sayı 1, ss. 99–124, 2014.
• [9] Toloo, M. A. Mehdi ve A. Amirteimoori, “Performance assessment in production systems without explicit inputs: An application to basketball players,” IMA J. Manag. Math., cilt 27, sayı 2, ss. 143–156, 2016.
• [10] N. Ergül, “Spor kulüplerinin futboldaki başarıları ile spor şirketlerinin finansal başarıları arasındaki ilişkinin test edilmesi,” Hacettepe Üniversitesi İktisadi ve İdari Bilim. Fakültesi Derg., cilt 35, sayı 3, ss. 43–71, 2017.
• [11] O. Geyik ve T. Eren, “SporToto Basketbol Süper Ligi ve Turkish Airline Euroleague Basketbol Takımlarının AHS-TOPSIS Yöntemleriyle Değerlendirilmesi,” Spor Bilim. Araştırmaları Derg., cilt 3, sayı 1, ss. 32–53, 2018.
• [12] L. Rokach ve O. Maimon, Data mining with decision trees, Second Edi. Singapur: World Scientific Publishing, 2015.
• [13] C. Hwang ve K. Yoon, Multiple Attribute Decision Making: Methods and Applications, A State of the Art Survey, cilt 1. 1981.
• [14] G.-H. Tzeng ve J.-J. Huang, Multiple attribute decision making. Florida: CRC Press, 2011.
• [15] H. Chernoff, “The use of faces to represent points in k-dimensional space graphically,” J. Am. Stat. Assoc., cilt 68, sayı 342, ss. 361–368, 1973.