The men's football transfer market represents a complex phenomenon requiring suitable methods for an in-depth study. Network Analysis may be employed to measure the key elements of the transfer market through network indicators, such as degree centrality, hub and authority scores, and betweenness centrality. Furthermore, community detection methods can be proposed to unveil unobservable patterns of the football market, even considering auxiliary variables such as the type of transfer, the age or the role of the player, and the agents involved in the transfer flow. These methodologies are applied to the flows of player transfers generated by the 20 teams of the Italian first division (Serie A). These flows include teams from all over the world. We consider the summer market session of 2019, at the beginning of the season 2019-2020. Results also help to better understand some peculiarities of the Italian football transfer market in terms of the different approaches of the elite teams. Network indices show the presence of different market strategies, highlighting the role of mid-level teams such as Atalanta, Genoa, and Sassuolo. The network reveals a core-periphery structure splitted into several communities. The Infomap algorithm identifies 14 single team-based communities and three communities formed by two teams. Two of the latter are composed of a top team and a mid-level team, suggesting the presence of collaboration and similar market behavior, while the third is guided by two teams promoted by the second division (Serie B).
Community structure of the football transfer market network: the case of Italian Serie A
Ievoli, Riccardo;
2023
Abstract
The men's football transfer market represents a complex phenomenon requiring suitable methods for an in-depth study. Network Analysis may be employed to measure the key elements of the transfer market through network indicators, such as degree centrality, hub and authority scores, and betweenness centrality. Furthermore, community detection methods can be proposed to unveil unobservable patterns of the football market, even considering auxiliary variables such as the type of transfer, the age or the role of the player, and the agents involved in the transfer flow. These methodologies are applied to the flows of player transfers generated by the 20 teams of the Italian first division (Serie A). These flows include teams from all over the world. We consider the summer market session of 2019, at the beginning of the season 2019-2020. Results also help to better understand some peculiarities of the Italian football transfer market in terms of the different approaches of the elite teams. Network indices show the presence of different market strategies, highlighting the role of mid-level teams such as Atalanta, Genoa, and Sassuolo. The network reveals a core-periphery structure splitted into several communities. The Infomap algorithm identifies 14 single team-based communities and three communities formed by two teams. Two of the latter are composed of a top team and a mid-level team, suggesting the presence of collaboration and similar market behavior, while the third is guided by two teams promoted by the second division (Serie B).File | Dimensione | Formato | |
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