In this paper, the authors apply network theory to soccer analysis. They focus specifically on how players on a given soccer team pass the ball. The graphs have edges directed the way the ball travels. Very conveniently, the graphs are organized to reflect the general location of the players based on the position they play. This means the graphs show not only who passes to whom, but where on the field the passes are coming from and going.
Besides showing who on the field is connected, the authors also show how strong the connections are. The thickness of the arrows between players is based on how many passes each player passed to each other. Because Player A might pass to Player B much more than Player B passes to Player A, the arrows between two players can be of different thickness.
While it’s interesting to look at the graphs, it’s also important to assign some numbers to the patterns observed. The authors of the paper define three statistics that describe a player’s importance to the passing. The first, closeness, measures how easily a player can be passed to. The second, betweenness, is the percentage of shortest paths that go through a player. The last one, pagerank centrality, is a measure of popularity based on the number of passes a player receives and who passes to him.
The authors got their data from the 2010 FIFA World Cup. Although they made tables for the final 16 teams, they only made graphs for the teams that reached the finals, Spain and the Netherlands. Interestingly, out of the 16 teams that made it to the knockout stage, Spain and the Netherlands had the highest average clustering coefficient, meaning that their passing was balanced. It involved the whole team, not just a few players. Their style is one other teams might want to consider because it allows for players having bad games. If a team relies on two or three key players and one of them isn’t playing well, that team will most likely lose.
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