Network and attribute-based clustering of tennis players and tournaments

Pierpaolo D’Urso (), Livia Giovanni (), Lorenzo Federico () and Vincenzina Vitale ()
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Pierpaolo D’Urso: Sapienza University
Livia Giovanni: Luiss University
Lorenzo Federico: Luiss University
Vincenzina Vitale: Sapienza University

Computational Statistics, 2025, vol. 40, issue 4, No 2, 1689-1712

Abstract: Abstract This paper aims at targeting some relevant issues for clustering tennis players and tournaments: (i) it considers players, tournaments and the relation between them; (ii) the relation is taken into account in the fuzzy clustering model based on the Partitioning Around Medoids (PAM) algorithm through spatial constraints; (iii) the attributes of the players and of the tournaments are of different nature, qualitative and quantitative. The proposal is novel for the methodology used, a spatial Fuzzy clustering model for players and for tournaments (based on related attributes), where the spatial penalty term in each clustering model depends on the relation between players and tournaments described in the adjacency matrix. The proposed model is compared with a bipartite players-tournament complex network model (the Degree-Corrected Stochastic Blockmodel) that considers only the relation between players and tournaments, described in the adjacency matrix, to obtain communities on each side of the bipartite network. An application on data taken from the ATP official website with regards to the draws of the tournaments, and from the sport statistics website Wheelo ratings for the performance data of players and tournaments, shows the performances of the proposed clustering model.

Keywords: Fuzzy clustering; Spatial constraints; Bipartite networks; Tennis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01493-2

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