A Mixed Model for Performance-Based Classification of NBA Players
Performance-Based Classification of NBA Players
Keywords:
NBA, Player Performance, Classification, K-means, Discriminant Analysis, One-Way ANOVA, Multilayer Perceptron Neural NetworkAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Data Science and Advanced Analytics (IJDSAA) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. This license allows users to copy, distribute and transmit an article, adapt the article as long as the author is attributed and the article is not used for commercial purposes.
The author(s) confirms
- The manuscript submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
- The published materials used in the manuscript were obtained permission for reproduction. (if any)