Over on StatsBomb, I’ve written about Leicester’s attacking exploits this season, specifically focusing on the style and effectiveness of their attack. That required a fair amount of research into various aspects relating to the speed and directness of teams attacks, which I’ve looked into since I started looking at possessions and expected goals.
One output of all that is a bunch of numbers at the team and player level stretching back over the past four seasons about fast-attacks and counter-attacks, some of which I will post below along with some comments.
As a brief reminder, a possession is a passage of play where a team maintains unbroken control of the ball. I class a possession moving at greater than 5 m/s on average as ‘fast’ based on looking at a bunch of diagnostics relating to all possessions i.e. not just those ending with a shot. The final number is fairly arbitrary as I just went with a round number rather than a precisely calculated one but the interpretation of the results didn’t shift much when altering the boundary. Looking at the data, there is probably some separation into slow attacks (<2 m/s), medium-paced attacks (2-5 m/s) and then the fast attacks (>5 m/s). Note that some attacks go away from goal, so they end up with a negative speed (technically I’m calculating velocity here but I’ll leave that for another time), so these are attacks towards the goal.
Counter-attacks are when these fast-paced moves begin in a teams own half. Again this is fairly arbitrary from a data point-of-view but it at least fits in with what I think most would consider to be a counter-attack and it’s very easy to split the data into narrower bands in future.
All of the numbers below are based on my expected goals model using open-play shots only. I don’t include a speed of attack or counter-attacking adjustment in my model.
So, without further ado, here are some graphs…
Top-20 offensive fast-attacking teams
Champions Elect Leicester City sit atop the pile with a reasonable gap on THAT Liverpool team, with a fairly big drop to the chasing pack behind. Arsenal and Manchester City are quite well represented here illustrating the diversity of their attacks – while both are typically among the slowest teams on average, they can step it up effectively when presented with the opportunity.
Top-20 offensive counter-attacking teams
Number one isn’t a huge shock, with this years Leicester City narrowly ahead of the 12/13 iteration of Liverpool. A lot of the same teams are found in both the fast-attacking and counter-attacking brackets, which isn’t a great surprise perhaps.
Southampton this year are perhaps a little surprising and it is a big shift from previous seasons (0.056-0.075 per game), although I’ll admit I haven’t paid them that much attention this year. Their defense is the 6th worst in this period on counter-attacks also (3rd worst on fast-attacks). When did Southampton become a basketball team?
What is particularly noticeable is the prevalence of teams from the past two seasons in the top-10. A trend towards more-transition orientated play? Something to examine in more detail at another time perhaps.
Top-20 defensive fast-attacking teams
Most of the best performances on the defensive side are from the 12/13 and 13/14 seasons, which might give some credence to a greater emphasis more recently on transitions along with an inability to cope with them.
The list overall is populated by the relative mainstays of Manchester City, Liverpool and West Brom along with various fingerprints from Mourinho, Warnock and Pulis
Top-20 defensive counter-attacking teams
Interestingly there is a greater diversity between the counter-attacking and fast-attacking metrics on the defensive side of the ball than on the offensive side, which might point to potential strengths and/or weaknesses in certain teams.
Spurs last season rank as the worst defensive side in terms of counter-attacking expected goals against, and are narrowly beaten into second spot for fast-attacks by the truly awful 2012/13 Reading team.
Top-20 fast-attacking players
Lastly, we’ll take a quick look at players. For now, I’m just isolating the player who took the shot, rather than those who participated in the build-up to the goal. A lot of this will be tied up in playing style and team effects.
Jamie Vardy is clearly the standout name here, followed by Daniel Sturridge and Danny Ings. Sturridge leads the chart in terms of actual goals with 0.21 goals per 90 minutes, with Vardy third on 0.18.
Vardy’s overall open-play expected goals per 90 minutes stands at 0.26 by my numbers over the past two seasons, so over half of his xG per 90 comes from getting on the end of fast-attacking moves. He sits in 16th place over all for those with over 2,700 minutes played, which is respectable but he is clearly elite when it comes to faster-paced attacks.
Top-20 counter-attacking players
Danny Ings sits on top when it comes to counter-attacking, which bodes well for his future under Jürgen Klopp at Liverpool, providing his injury hasn’t unduly affected him. Again, Sturridge leads the list in terms of actual goals with 0.13 per 90 minutes, with Vardy second on 0.12. The sample sizes are lower here, so we would expect a greater degree of variance in terms of the comparison between reality and expectation.
One of the interesting things when comparing these lists is the divergence and/or similarities between the overall goal scorer chart. For example, Edin Džeko and Wilfried Bony sit in first and fourth place respectively in the overall table for this period but lie outside the top-20 when it comes to faster-paced attacks. A clear application of this type of work is player profiling to fit the particular style and needs of a prospective team, which Paul Riley has previously shown to be a useful method for evaluating forwards.
I wanted to post these as a starting point for discussion before I drill down further into the details in the future. The data presented here and that underlying it are very rich in detail and potential applications, which I have already started to explore. In particular, there is a lot of spatial information encapsulated in the data that can inform how teams attack and defend, which can help to build further descriptive elements to team styles along side measures of their effectiveness.
I’ll keep you posted.