Kenedict Innovation Analytics

World Cup Special: How are the World’s Top Players Connected?

Which countries, teams and players dominate the core of the World Cup network, and how are they connected? Although the usual focus of this blog is on a combination of network analytics and innovation / Research & Development, let’s make an exception here and apply a network perspective to show how all players participating in this year’s World Cup are connected to each other.

Written by André Vermeij

Next to their current participation in the world’s biggest single-event sporting competition, all players active in their national teams are of course also active in club teams across the world. This means that a lot of social connections exist across national teams as well. This post takes a deep dive into the social network of World Cup players to find out how they are connected and which players and countries comprise the core of the network.

The network includes all 736 players active in this year’s World Cup (23 players divided over 32 teams¹). Players are connected when they are part of the same national team and when they play together for a club during the regular season. Together, a total of 9228 connections exist between all players. Colours reflect clusters of players and are often, but not always, directly linkable to national teams. This is especially the case in the dense core of the network, which includes players clustering together through their association with Europe’s top teams and with their national teams. For instance, Spain and Brazil make up one cluster of players, as do England and Belgium.

Any node/player can be clicked to obtain more information. Caps refer to the number of times a player has played for his national team. Feel free to play around (a full-page version of the network can be found here):

The countries which are sparsely connected to the core of the network because of relatively few connections to players at Europe’s top clubs are mainly African (Nigeria, Ghana, Algeria, Ivory Coast) and Latin American (Ecuador, Honduras, Costa Rica, Colombia, Chile, Mexico). Other national teams in the periphery of the network are Russia, Bosnia and Herzegovina, Greece, Australia, United States, South Korea, and Iran.

The core contains many of the usual suspects: Argentina, Uruguay and Brazil are there, as well as all of Europe’s large football nations. Japan and Cameroon are also found here, showing the representation of their players at various large European clubs.

The best-connected players in the network (in terms of their total amount of connections or degree centrality) are players who are part of clubs that supply a significant amount of players to various national teams: Barcelona, Bayern Munich and Manchester United each supply a staggering 16, 14 and 14 players respectively. Chelsea, Juventus, Napoli and Madrid each supply 12 players; Arsenal, Liverpool, Manchester City and Paris Saint-Germain are responsible for 10 players each.

Logically then, the best-connected players also originate from these teams. The visualisation shows the best-connected players through their node sizes: the larger the node, the more connections a player has. At a standard zooming level, the names of these players are automatically shown.

If you draw any other interesting conclusions from the above, please do let me know in a comment below!

¹: Data originate from Wikipedia (

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