Complex networks for community detection of basketball players

In this paper a weighted complex network is used to detect communities of basketball players on the basis of their performances. A sparsification procedure to remove weak edges is also applied. In our proposal, at each removal of an edge the best community structure of the “giant component” is calcu...

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Bibliographic Details
Published in:Annals of operations research 2023-06, Vol.325 (1), p.363-389
Main Authors: Chessa, Alessandro, D’Urso, Pierpaolo, De Giovanni, Livia, Vitale, Vincenzina, Gebbia, Alfonso
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Basketball players
Business and Management
Combinatorics
Data mining
Decision making
Evaluation
Modularity
Operations research
Operations Research/Decision Theory
Original Research
Players
Professional baseball
Professional basketball
Sports executives
Statistical analysis
Theory of Computation
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Summary:In this paper a weighted complex network is used to detect communities of basketball players on the basis of their performances. A sparsification procedure to remove weak edges is also applied. In our proposal, at each removal of an edge the best community structure of the “giant component” is calculated, maximizing the modularity as a measure of compactness within communities and separation among communities. The “sparsification transition” is confirmed by the normalized mutual information. In this way, not only the best distribution of nodes into communities is found, but also the ideal number of communities as well. An application to community detection of basketball players for the NBA regular season 2020–2021 is presented. The proposed methodology allows a data driven decision making process in basketball.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-022-04647-x