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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. [ABSTRACT FROM AUTHOR]