A Mixed Model for Performance-Based Classification of NBA Players

Performance-Based Classification of NBA Players

  • Yeong Nain Chi University of Maryland Eastern Shore
  • Jennifer Chi University of Texas at Dallas
Keywords: NBA, Player Performance, Classification, K-means, Discriminant Analysis, One-Way ANOVA, Multilayer Perceptron Neural Network

Abstract

Using data collected from the Basketball-Reference.com, this study examined NBA player performance values to discern patterns and to classify clusters exhibiting common patterns of player performance. Empirical results based on the K-means clustering analysis identified three NBA player clusters. Results of the K-means clustering analysis were tested for accuracy using the discriminant analysis indicated that cluster means were significantly different. The results of one-way ANOVA also showed that significant differences in all twenty-one independent variables were found within the three identified NBA player clusters. The multilayer perceptron neural network model was utilized as a predictive model in deciding the classification of NBA players based on their performance related statistics. From an architectural perspective, it showed a 21-7-3 neural network construction. Results of this study may provide insight into the understanding of the performance of NBA players for NBA management purposes.

Published
2021-01-19
How to Cite
Chi, Y. N., & Chi, J. (2021). A Mixed Model for Performance-Based Classification of NBA Players. International Journal of Data Science and Advanced Analytics (ISSN 2563-4429), 3(3), 36-46. Retrieved from http://ijdsaa.com/index.php/welcome/article/view/82
Section
Articles