I will continue flogging this attacking shape data until I find a good use for it, but here’s a bit of fun: given the average height of team’s attacks, i.e. the amount of ground covered towards their opponent’s goal, and the duration of those attacks, we can calculate the pace at which teams hurtle towards the opposition goal. It’s a pretty nice measure of how ‘direct’ teams are, and here’s who comes out top:
|Team||Attacking Pace (m/s)|
|SV Darmstadt 98||3.33|
|FC Ingolstadt 04||3.16|
|TSG 1899 Hoffenheim||3.13|
|Sporting de Gijón||3.11|
|1. FSV Mainz 05||2.94|
You can read more about Caen’s quick transitions and counter-attacking play in this article by Mohamed Mohamed on StatsBomb. It’s worth noting that for the numbers I have available (2012+ outside the EPL), this year’s Caen are currently the fastest attacking side I can find, so they’re probably worth a watch this season. They’re currently sitting 5th in Ligue 1. 2010 Blackburn Rovers are second, with half a season of Big Sam (auditioning for the Inter and Real jobs, if you remember) and half Steve Kean. I’ll leave it to you to dig those tapes out…
Guess who’s propping up the table at the bottom?
|Team||Attacking Pace (m/s)|
|FC Bayern München||2.17|
You’ll be happy to hear that Man Utd’s buildup play this year is only the second slowest on record. They were beaten out by none other than… last year’s Man Utd.
One last bonus, the Pep effect:
|Season||Team||Attacking Pace (m/s)|
|2013||FC Bayern München||2.33|
|2012||FC Bayern München||2.33|
|2015||FC Bayern München||2.17|
|2014||FC Bayern München||2.16|
10 thoughts on “Europe’s Most Direct Teams”
Any indication of the variation in a team’s numbers? I noticed that Man City are towards the bottom of the table and considered the possibility that this is because they have such a high number of successful (well, culminating in a shooting opportunity) passages of play.
Also (1) can you divide the data into either an arbitrary short vs. long component (we do this in biology for certain molecular-level events) similar to your recent Tottenham hotspur analysis and (2) separate the data into passages of play on the ground and in the air? These might help to complement the data above nicely in terms how a team is playing the ball and how successful they are.
By the way, really nice and easy site to convey stats to those of us less savvy to the ways of sports analytics.
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Your first point’s actually a really important one – part of the reason that good teams have ‘slower’ numbers is that on average they lose the ball less, so long periods of possession turn into shots. Worse teams keep the ball less, so the only moves that turn into shots are ones that have a fairly direct path to goal. It’d be interesting to try and factor in each team’s general ability to string a series of passes together, shot or not.
I will attempt to chop the data up as you suggest next week, thanks for the suggestions (and nice words!)
Probably not very useful, but here’s an example of the fast and slow component analysis I was rambling about (methods) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144071/
It would be cool to know how well individual clubs fit expected distributions for 1, 2 or greater component analysis with regards to attack speed.
Ok, I’m going to stop suggesting stuff now…
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Interesting stats. Does the above data take into account an average pace of attacks? A team may still be fast in transition when they decide to attack. They may choose to alternate between holding on the ball at the back for long periods of time and then look to attack at pace when they get it
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