Abstract
AbstractUsing publicly available data from the football database transfermarkt.co.uk, it is possible to construct a trade network between football clubs. This work regards the network of the flow of transfer fees between European top league clubs from eight countries between 1992 and 2020 to analyse the network of each year’s transfer market. With the transfer fees as weights, the market can be represented as a weighted network in addition to the classic binary network approach. This opens up the possibility to study various topological quantities of the network, such as the degree and disparity distributions, the small-world property and different clustering measures. This article shows that these quantities stayed rather constant during the almost three decades of transfer market activity, even despite massive changes in the overall market volume.
Funder
Studienstiftung des Deutschen Volkes
Publisher
Springer Science and Business Media LLC
Subject
Mathematical Physics,Statistical and Nonlinear Physics
Reference38 articles.
1. Bornhold, S., Schuster, H.G. (eds.): Handbook of Graphs and Networks. Wiley-VCH, Weinheim (2005)
2. Caldarelli, G., Vespignani, A. (eds.): Large Scale Structure and Dynamics of Complex Networks. World Scientific Publishing, Singapore (2007)
3. Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48–50 (1956). https://doi.org/10.1090/s0002-9939-1956-0078686-7
4. Sandoval, L.: A map of the brazilian stock market. Adv. Complex Syst. 15(05), 1250042 (2012). https://doi.org/10.1142/S0219525912500427
5. Aslam, F., Mohmand, Y.T., Ferreira, P., Memon, B.A., Khan, M., Khan, M.: Network analysis of global stock markets at the beginning of the coronavirus disease (Covid-19) outbreak. Borsa Istanbul Rev. 20, 49–61 (2020). https://doi.org/10.1016/j.bir.2020.09.003
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献