FIFA World Cup 2014, the biggest sporting event in four years (sorry Olympics) is starting today. The tournament holds 736 players from 32 countries. When the players are not playing for their national teams, they play in 301 different clubs. Players from different national teams meet in these clubs. For example, Manchester United has players from 9 different national teams. This means that players in the World Cup who play in Manchester United know players from at least eight different national teams. Why is this important? If two players belong to the same team (national or club), they have a social connection. Using social network analysis we can analyze and visualize this connection (examples from UEFA 2012 and World Cup 2010 similar networks). So, here’s the social network of FIFA World Cup 2014:


In the picture above is the player-to player-connections (here’s a pdf with a better resolution). The size of a players name represents the total number of other players he shares a club with. The top players, Antonio Valencia, Javier Hernández, Julian Green, Shinji Kagawa, Robin van Persie, Nani, Arjen Robben, Mario Mandžukić, Patrice Evra, Xherdan Shaqiri, Daniel Van Buyten, Dante and Javi Martínez have all 13 club mates in the tournament. The color of the player is determined by a computer program that detects clusters. Most of the clusters are the same as the national teams, but we notice that in the middle, the line between Spain, France, Brazil etc. start to become blurry. To get a better picture of the most connected players, below is the core of the network: top 10 % of the players.

worldcup_players_coreWe can also visualize the connections between different national teams and the clubs.


A line between a club and team is formed when a player plays in both of them (pdf). The more a national team has players from a specific team, the thicker the line. For example the German team has seven players from Bayern Munich and Spain has seven players from Barcelona. The most diverse teams are Algeria and Nigeria whose players come all from different clubs. To clarify the situation, below is a picture of the teams and clubs that have at least two common players. Four countries have at least two players from Napoli.

worldcup_players_core_v3Most interesting finding for me is that all of the players are connected one way or another. On average, two players have less than three steps between them. It would be interesting to extend this analysis to the players’ previous clubs and see how the social network of past five years would look like.

EDIT: For those that are more of DIY type network analysts, here are the network files (GraphML): player-player network and team-club network.

Twitter: jattipaa

Data from Wikipedia. Visualizations with Gephi.