There is a lot of skepticism about passing statistics. In particular, # of passes completed and pass completion % are generally eschewed by the soccer analytics community. For the most part these criticisms are valid. These metrics don't control at all for the difficulty or type of pass being attempted. For example, a player such as Barcelona's Xavi might rack up 100 passes a game completed at a 90% clip, but for the most part they are all short passes completed under minimal defensive duress. His passing statistics are as much a product of Barcelona's "tiki-taka" system as they are a reflection of his individual skill. Therefore, if we place these statistics within the context of a team's system, might that be more informative? Perhaps.
Pass Usage Rate (%)
The concept is pretty simple: take a player's passes/90 and divide it by the team's passes/90. Additionally, I have introduced a concept called Pass Share, which frames the Pass Usage Rate (%) in a slightly different manner: Pass Share takes a players Pass Usage Rate (%) and divides it by an average player's Pass Usage Rate (9.1%) (assumes all 11 players share equally in passing).
See below for results from MLS 2013. For avid watchers they aren't that surprising, though seeing the two Montreal Impact central defenders so high up the list (Nesta and Ferrari) is interesting.
"Pass Share takes a players Pass Usage Rate (%) and divides it by an average player's Pass Usage Rate (9.1%) (assumes all 11 players share equally in passing)."
ReplyDeleteIt would be interesting to take this one step further and segregate by position(ish) - maybe d, mids and forwards?
That's a great point and I am definitely considering doing something with it in future iterations of this metric. It gets a little tricky classifying some players, but would be a worthwhile exercise.
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