When we look in to our past, we see different communities: schools, companies, teams etc. Most important thing in these communities is that we meet people; we network. One big community, UEFA European Championship 2012 is nearing its final: Spain vs. Italy.
Like all of us, the players in these teams have a past of different communities. In this case, the most interesting communities are football clubs. Like in my previous post of all of the teams, I ventured a guess: if two players have played in the same club, they share a connection (in this post they share a connection if they have played in the same club in the same year). Combine all those connections and you get a social network of the players.
Size of the players name represents the betweenness of the player: how important the player is to the experience flow of the team. We see that Pepe Reina is the most important player from the network perspective. Second and third are Christian Maggio and Álvaro Arbeloa. We see that most of the players are connected. Note: I only took in to account the prime league clubs.
The network structure of the both teams are similar in their density: both utilize about 20 % of all possible ties. Spain, however form almost twice the amount of cliques. This translates as more tightly connected groups. Data from team Spain from here and from team Italy here. Interesting is that Thiago Motta and Mario Balotelli are more connected to the Spanish team than to the Italian team. This may translate to better knowledge of the opposing team. The Spanish team does however have more players that have played together for several years. In other organizations this usually leads to better performance, so based on social network analysis, my money would be on Spain.
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Visualized with Gephi.
The UEFA European Championship 2012 has some interesting statistics. For example its 16 teams have 368 players from 137 clubs. This means that in average each club has almost 3 of its players on the tournament. But averages are a poor tool when you can use social network analysis.
Like in 2010 World Cup the teams, clubs and players form a social network. In short, if a two players belong to the same team (country or club), they have a connection and this connection can be visualized. For similar visualizations, check out this viz from a Finnish newspaper Helsingin Sanomat (I also got the raw data used to do this analysis from the site).
The picture below is all the players, teams and clubs in the same network. The size of the node represents the amount of connections the node has.
Bayern München is the best represented club: it has 13 players in the tournament.
The next picture has only the clubs and teams. They are connected through players. The size of the node represents the “importance” (or eigenvector centrality) of the node. This means that the players from the Netherlands team are best connected throughout the network. Surprisingly Sweden is second. As the number of common players increase, so does the thickness of the line between the club and the team. They also get closer they get on the map. For example Spain and Portugal both have lot of players from Real Madrid and these two countries end up close to each other on the map. From the clubs’ perspective the players from Arsenal, Bayern München and Manchester City are central to the network.
To take the analysis even further here is the network of all the players in the tournament. A connections here means a shared team or a club membership. The size of the node represent the importance (or a good position) of the player. The color represents the “community” of players; a clique of sorts.
Franck Ribéry and Arjen Robben are the most well positioned players in the network. Every player can reach every other player in max 5 steps. A more clearer picture emerges after we remove all the players that play with less than 27 other players (22 from their team + 5 from the club). A sort of crème de la crème of players.
For you SNA enthusiasts here is the .net file of the players, teams and all of the actors. Visualized with Gephi.
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Verkostoanatomia - the Network Approach 