Clustering of NBA Teams Across Multiple Years

Research Article
Open access

Clustering of NBA Teams Across Multiple Years

Yiming Zhong 1*
  • 1 Department of Mathematics, University of Illinois Urbana Champaign, USA    
  • *corresponding author yzhong33@illinois.edu
Published on 28 March 2025 | https://doi.org/10.54254/2755-2721/2025.21716
ACE Vol.119
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-805-5
ISBN (Online): 978-1-83558-806-2

Abstract

The study seeks to provide an objective foundation for NBA team rankings beyond win-loss statistics. The research uses machine learning to examine offensive efficiency, defen- sive efficiency, and win % from the last four NBA seasons utilizing sports analytics and big data. The study uses PCA and K-Means clustering to classify team performance tiers. Teams with balanced offensive and defensive measures typically rank higher in clusters, establishing the technique as a more thorough evaluation tool. Moderate accuracy in error analysis shows the balance between analytical depth and practical applicability. The research found that this data-driven paradigm helps comprehend team relationships and performance, which might be used in strategic decision-making and other sports. Refinement may incorporate time patterns and player-specific data.

Keywords:

PCA, machine learning, sports analytics, K-means clustering

Zhong,Y. (2025). Clustering of NBA Teams Across Multiple Years. Applied and Computational Engineering,119,122-134.
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References

[1]. Michal S Gal and Daniel L Rubinfeld. Data standardization. NYUL Rev., 94:737, 2019.

[2]. Marija Norusis. SPSS 15.0 guide to data analysis. Prentice Hall Press, 2007.

[3]. Andrzej Mac ́kiewicz and Waldemar Ratajczak. Principal components analysis (pca). Computers & Geosciences, 19(3):303–342, 1993.

[4]. Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and Angela Y Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7):881–892, 2002.

[5]. Robert S Boyer and J Strother Moore. Mjrty—a fast majority vote algorithm. In Automated reasoning: essays in honor of Woody Bledsoe, pages 105–117. Springer, 1991.

[6]. Fionn Murtagh and Pedro Contreras. Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1):86–97, 2012.

[7]. ErichSchubert,Jo ̈rgSander,MartinEster,HansPeterKriegel,andXiaoweiXu.Dbscanrevisited, revisited: why and how you should (still) use dbscan. ACM Transactions on Database Systems (TODS), 42(3):1–21, 2017.

[8]. Douglas A Reynolds et al. Gaussian mixture models. Encyclopedia of biometrics, 741(659-663), 2009.


Cite this article

Zhong,Y. (2025). Clustering of NBA Teams Across Multiple Years. Applied and Computational Engineering,119,122-134.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning

ISBN:978-1-83558-805-5(Print) / 978-1-83558-806-2(Online)
Editor:Marwan Omar, Hui-Rang Hou
Conference website: https://2025.confseml.org/
Conference date: 2 July 2025
Series: Applied and Computational Engineering
Volume number: Vol.119
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Michal S Gal and Daniel L Rubinfeld. Data standardization. NYUL Rev., 94:737, 2019.

[2]. Marija Norusis. SPSS 15.0 guide to data analysis. Prentice Hall Press, 2007.

[3]. Andrzej Mac ́kiewicz and Waldemar Ratajczak. Principal components analysis (pca). Computers & Geosciences, 19(3):303–342, 1993.

[4]. Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and Angela Y Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7):881–892, 2002.

[5]. Robert S Boyer and J Strother Moore. Mjrty—a fast majority vote algorithm. In Automated reasoning: essays in honor of Woody Bledsoe, pages 105–117. Springer, 1991.

[6]. Fionn Murtagh and Pedro Contreras. Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1):86–97, 2012.

[7]. ErichSchubert,Jo ̈rgSander,MartinEster,HansPeterKriegel,andXiaoweiXu.Dbscanrevisited, revisited: why and how you should (still) use dbscan. ACM Transactions on Database Systems (TODS), 42(3):1–21, 2017.

[8]. Douglas A Reynolds et al. Gaussian mixture models. Encyclopedia of biometrics, 741(659-663), 2009.