The formation mechanism of the player transfer network among football clubs

ABSTRACT

Clubs compete for talented players to succeed in the global football industry. Drawing on a network analytical framework, this research explores how endogenous and exogenous factors influence the partner selection process in the player transfer market. The boundary of the transfer network consists of 98 football clubs from the top five European leagues. Using exponential random graph models (ERGMs), the current study confirms that clubs have a significantly high propensity of exchanging players. Player transactions often occur through closed triads, such that clubs tended to make a deal with those who shared the same third partners. Additionally, league type and match performance produce stable and significant effects on the formation of transfer ties. Practical implications are also discussed.

Disclosure statement

No potential conflict of interest was reported by the author.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. Matesanz et al., ‘Transfer Market Activities and Supportive Performance in European First Football Leagues’, e0209362; Nolasco, ‘Player Migration in Portuguese Football’, 795–809.

2. Nolasco, ‘Player Migration in Portuguese Football’, 795.

3. Granovetter, ‘Economic Action and Social Structure’, 481.

4. Ibid., 481–510; Monge and Contractor, Theories of Communication Networks.

5. Granovetter, ‘Economic Action and Social Structure’, 481–510.

6. Liu et al., ‘The Anatomy of the Global Football Player Transfer Network’, e0156504.

7. Matesanz et al., ‘Transfer Market Activities and Supportive Performance in European First Football Leagues’, e0209362.

8. Bond, Widdop, and Parnell, ‘Topological Network Properties of the European Football Loan System’, 1–24.

9. Li, Zhou, and Stanley, ‘Network Analysis of the World Footballer Transfer Market’, 18005.

10. Lewis, Gonzalez, and Kaufman, ‘Social Selection and Peer Influence in an Online Social Network’, 68–72.

11. Robins et al., ‘An Introduction to Exponential Random Graph (p*) Models for Social Networks’, 173–91.

12. Monge and Contractor, Theories of Communication Networks.

13. Ibid., 55.

14. Lusher and Robins, ‘Formation of social network structure’, 23.

15. Ibid; Contractor, Wasserman, and Faust, ‘Testing Mutlitheoretical, Multilevel Hypotheses About Organizational Networks’, 681–703.

16. Lusher and Robins, ‘Formation of social network structure’, 16–28.

17. Shumate, ‘The Evolution of the HIV/AIDS NGO Hyperlink Network’, 120–34.

18. Merton, ‘The Matthew Effect in Science’, 56–63.

19. Bond, Widdop, and Parnell, ‘Topological Network Properties of the European Football Loan System’, 1–24.

20. Monge and Contractor, Theories of Communication Networks, 59.

21. Coleman, ‘Social Capital in the Creation of Human Capital’, S95–S121.

22. Lusher and Robins, ‘Formation of social network structure’, 16–28; Shumate, ‘The Evolution of the HIV/AIDS NGO Hyperlink Network’, 120–34.

23. Contractor, Wasserman, and Faust, ‘Testing Mutlitheoretical, Multilevel Hypotheses About Organizational Networks’, 686.

24. Lusher and Robins, ‘Formation of social network structure’, 16–28.

25. Nolasco, ‘Player Migration in Portuguese Football’, 795–809.

26. Lusher and Robins, ‘Formation of social network structure’, 16–28; Monge and Contractor, Theories of Communication Networks.

27. McPherson, Smith-Lovin, and Cook, ‘Birds of a Feather: Homophily in Social Networks’, 415–44.

28. Monge and Contractor, Theories of Communication Networks.

29. Lewis, Gonzalez, and Kaufman, ‘Social Selection and Peer Influence in an Online Social Network’, 68–72.

30. Robins et al., ‘An Introduction to Exponential Random Graph (p*) Models for Social Networks’, 173–91.

31. Zappa and Lomi, ‘The Analysis of Multilevel Networks in Organizations: Models and Empirical Test’, 557.

32. Goodreau et al., ‘A Statnet Tutorial’, 1–27.

33. Lusher and Robins, ‘Example Exponential Random Graph Model Analysis’, 37–46.

34. Leifeld and Schneider, ‘Information Exchange in Policy Networks’, 739.

35. Goodreau et al., ‘A Statnet Tutorial’, 1–27.

36. Ibid.

37. Merton, ‘The Matthew Effect in Science’, 56–63.

38. Li, Zhou, and Stanley, ‘Network Analysis of the World Footballer Transfer Market’, 18005.

39. Bond, Widdop, and Parnell, ‘Topological Network Properties of the European Football Loan System’, 1–24; Li, Zhou, and Stanley, ‘Network Analysis of the World Footballer Transfer Market’, 18005; Liu et al., ‘The Anatomy of the Global Football Player Transfer Network’, e0156504; Matesanz et al., ‘Transfer Market Activities and Supportive Performance in European First Football Leagues’, e0209362.

40. Elliott, ‘Football’s Irish Exodus’, 150.

41. Liu et al., ‘The Anatomy of the Global Football Player Transfer Network’, e0156504.

42. Bond, Widdop, and Parnell, ‘Topological Network Properties of the European Football Loan System’, 1–24; Grund, ‘Network Structure and Team Performance’, 682–90.

43. Contractor, Wasserman, and Faust, ‘Testing Mutlitheoretical, Multilevel Hypotheses About Organizational Networks’, 683.

44. Lusher and Robins, ‘Formation of social network structure’, 16–28.

45. Ibid.