Sooner than you think.
Read: PageRank Algorithm Reveals Soccer Teams’ Strategies.
From the post:
Many readers will have watched the final of the Euro 2012 soccer championships on Sunday in which Spain demolished a tired Italian team by 4 goals to nil. The result, Spain’s third major championship in a row, confirms the team as the best in the world and one of the greatest in history.
So what makes Spain so good? Fans, pundits and sports journalists all point to Spain’s famous strategy of accurate quick-fire passing, known as the tiki-taka style. It’s easy to spot and fabulous to watch, as the game on Sunday proved. But it’s much harder to describe and define.
That looks set to change. Today, Javier Lopez Pena at University College London and Hugo Touchette at Queen Mary University of London reveal an entirely new way to analyse and characterise the performance of soccer teams and players using network theory.
They say their approach produces a quantifiable representation of a team’s style, identifies key individuals and highlights potential weaknesses.
Their idea is to think of each player as a node in a network and each pass as an edge that connects nodes. They then distribute the nodes in a way that reflects the playing position of each player on the pitch.
Add to the graph representation links to related resources and analysis, can you say topic map?
I wonder when they will think about adding the officials and what fouls they usually call on what players?
In full: A network theory analysis of football strategies
Abstract:
We showcase in this paper the use of some tools from network theory to describe the strategy of football teams. Using passing data made available by FIFA during the 2010 World Cup, we construct for each team a weighted and directed network in which nodes correspond to players and arrows to passes. The resulting network or graph provides a direct visual inspection of a team’s strategy, from which we can identify play pattern, determine hot-spots on the play and localize potential weaknesses. Using different centrality measures, we can also determine the relative importance of each player in the game, the `popularity’ of a player, and the effect of removing players from the game.
I first saw this at Four short links: 4 July 2012 by Nat Torkington.