Correlation between profitability and transfer activity in European football

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The transfer market of European football can be classified as a system. In this system, the effectiveness of participant teams can depend on the activity in players’ transfers. This article assesses the utility of network analysis in analysing connections between the mentioned concepts. The hypothesis is that there is causality between a club’s activity in the transfer market and its profit from transfers. This research is based on empirical transfer data of major soccer teams, which have had a significant role in the last 12 years in Europe. It is assumed that the most active clubs in the transfer system have more financial power in the transfer market, while teams which are not active in transfers have less profit from transfers. In the network analysis, the teams can be defined as a set of nodes and connected by edges (interactions). The thickness of the edges and the size of the nodes depend on the volume of transfers among clubs. The number of interactions and the amount of the transfer price can measure this volume also. Considering the results of network indices, the relationships between the two phenomena were reviewed. In order to explore these relationships, the correlations among all of the relevant variables in the transfer market were also measured.

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  • 1. Dobson S. Gerrard B. (1999). The Determination of Player Transfer Fees in English Professional Soccer. Journal of Sport Management Vol. 13 No. 4 pp. 259-279.

  • 2. Galambosné Tiszberger M. (2015). A hálózatkutatás módszertani vizsgálati lehetőségei – szakirodalmi összefoglalás. Irodalomkutatás eredményei. Pécs Pécsi Tudományegyetem.

  • 3. Kapanova K. (2012). Football transfers looked from a social network analyses perspective. Available at [10 May 2018].

  • 4. Lee S. Hong I. Jung W.-S. (2015). A Network Approach to the Transfer Market of European Football Leagues. New Physics: Sae Mulli Vol. 65 No. 4 pp. 402-409.

  • 5. Liu X. Liu Y.-L. Lu X.-H. Wang Q.-X. & Wang T.-X. (2016). Available at [10 May 2018].

  • 6. Poli R. Ravenel L. Besson R. (2015). Transfer values and probabilities. CIES Football Observatory Monthly Report. Vol. 6.

  • 7. Sebestyén T. (2011). Hálózatelemzés a tudástranszferek vizsgálatában-régiók közötti tudáshálózatok struktúrájának alakulása Európában. Statisztikai Szemle Vol. 89 No. 6 pp. 667-697.

  • 8. Szymanski S. (2014). On the BALL. European soccer’s success can be credited in part to the liberalization of the players’ market. But what will the future bring? Finance and Development Vol. 3 pp. 26-28.

  • 9. Transfermarkt (2018). Transfers & Rumours. Available at [10 May 2018].

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