On the anatomy of a counter-attack

Originally published on StatsBomb.

One of the most enduring aspects of football is the multitude of tactical and stylistic approaches that can be employed to be successful. Context is king in analytics and football as a whole, so the ability to identify and quantify these approaches is crucial for both opposition scouting and player transfer profiles.

At the OptaPro Forum this year, I looked at data from the past five Premier League seasons and used a sprinkling of maths to categorise shots into different types.

One such style I identified was ‘fast attacks from deep’, which were a distinct class of shots born of fast and direct possessions originating in the defensive zone. While these aren’t entirely synonymous with counter-attacks, there is likely a lot of overlap; the classical counter-attack is likely a subset of the deep fast-attacks identified in the data.

These fast-attacks from deep typically offer good scoring chances, with above average shot conversion (10.7%) due to the better shot locations afforded to them. They made up approximately 23% of the shots in my analysis.

So what do they look like?

To provide an overview of the key features of these attacks, I’ve averaged them together to get a broad picture of their progression up the pitch. I’ve presented this below and included a look at attacks from deep that involve more build-up play for comparison.

Fast-attacks_vs_Build-up

Comparison between fast-attacks from deep and attacks from deep that focus on slower build-up play. Vertical pitch position refers to the progression of an attack towards the opponent’s goal (vertical pitch position equal to 100). Both attack types start and end in similar locations on average but their progress with time is quite different. The shading is the standard deviation to give an idea of the spread inherent in the data. Data via Opta.

Fast-attacks from deep are characterised by an initial speedy progression towards goal within a team’s own half, followed by a steadier advance in the attacking half. This makes sense qualitatively as counter-attacks often see a quick transition in their early stages to properly establish the attacking opportunity. The attack can then be less frenetic as a team seeks to create the best opportunity possible from the situation.

Over the past five seasons, the stand out teams as rated by shot volume and expected goals have been various incarnations of Arsenal, Manchester City, Chelsea and Liverpool.

The architects

Player-level metrics can be used to figure out who the crucial architects of a counter-attacking situation are. One method of examining this is how many yards a player’s passing progressed the ball during deep fast-attacking possessions.

Below I’ve listed the top 10 players from the 2016/17 season by this metric on a per 90 minute basis, alongside some other metrics for your delectation.

BallProgression_SummaryTable

Top players ranked by ball progression per 90 minutes (in yards) during fast-attacks from deep for the 2016/17 Premier League season. xGoals and Goals per 90 are for possessions that a player is involved in (known as xGChain in some parts). Players with more than 1800 minutes only. Data via Opta.

While the focus was often on him kicking people rather than the ball, we see that Granit Xhaka stands alone in terms of ball progression, with Daley Blind a long way behind him in second place. Xhaka’s long-range passing skills are well known, so combining this with the most passes per 90 in such situations propels him to the top of pile.

The graphic below illustrates Xhaka’s passing during deep fast-attacks, with his penchant for long passes spread all over the midfield zone evident. For comparison, I’ve included Eden Hazard’s passing map as someone who played many important passes that were limited in terms of ball progression as they were typically shorter or lateral passes in the final third.

Xhaka_Hazard_PassMaps.png

Passes played by Granit Xhaka and Eden Hazard during fast-attacks from deep during the 2016/17 season. Solid circles denote pass origin, while the arrows indicate the direction and end point of each pass. Data via Opta.

Evidently there is a link between position and ball progression, as players in deeper positions have greater scope to progress the ball as they have more grass in front of them. The likes of Coutinho, Özil and De Bruyne residing so high up the rankings is therefore impressive.

Coutinho_DeBruyne_PassMaps.png

Passes played by Philippe Coutinho and Kevin Dr Bruyne during fast-attacks from deep during the 2016/17 season. Data via Opta.

Coutinho’s passing chalkboard above illustrates his keen eye for a pass from midfield areas through opposition defensive lines, as does De Bruyne’s ability to find teammates inside the penalty area. De Bruyne’s contribution actually ranks highest in terms of xG per 90 for the past season.

The finishers

While ball progression through the defensive and midfield zones is important for these fast-attacks from deep, they still require the finishing touches in the final third. There are fewer more frustrating sights in football than watching a counter-attack be botched in its final moments.

The graphic below summarises the top players in this crucial aspect by examining their expected goal and assist outputs. Unsurprisingly, Kevin De Bruyne leads the way here and is powered by his exceptional creative passing.

xGandxA_DFA

Top-20 players rated by expected goals (xG) plus expenses assists (xA) for fast-attacks from deep. Players with more than 1800 minutes only. Data via Opta.

The list is dominated by players from the top-6 clubs, with Negredo the only interloper inside the top-10 ranking. Middlesbrough’s minimal attacking output left few scraps of solace for Negredo but at least he did get a few shots away in these high-value situations to alleviate the boredom.

Conclusion

The investigation of tactical and stylistic approaches carried out above merely scratches the surface of possibilities for opposition scouting and player profiling.

Being able to identify ‘successful’ attacking moves opens the door to examining ‘failed’ possessions, which would allow efficiency to be studied as well as defensive aspects. This is an area rich with promise that I’ll examine in the future, along with other styles identified within the same framework.

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Leicester City: Need for Speed?

Originally published on StatsBomb.

Leicester City’s rise to the top of the Premier League has led to many an analysis by now. Reasons for their ascent have mainly focused on smart recruitment and their counter-attacking style of play, as well as a healthy dose of luck. While their underlying defensive numbers leave something to be desired, their attack is genuinely good. The pace and directness of their attack has regularly been identified as a key facet of their style by writers with analytical leanings.

Analysis by Daniel Altman has been cited in both the Economist and the Guardian, with the crux being that the ‘key’ to stopping Leicester is to ‘slow them down’. Using slightly different metrics, David Sumpter illustrated this further at the recent Opta Pro Forum and on the Sky Sports website, where his analysis surmised that:

For Leicester, it’s about the speed of the attack.

An obvious and somewhat unaddressed question here is whether the pace of Leicester’s attack is the key to their increased effectiveness this season? Equating style with success in football is often a fraught exercise; the often tedious and pale imitations of Guardiola’s possession-orientated approach being a recent example across football.

Below are a raft of numbers comparing various facets of Leicester’s style and effectiveness this season with last season.

LCFC_Summary_Table.png

Comparison between Leicester City’s speed of attack and shot profile from ‘fast’ possessions. A possession is a passage of play where a team maintains unbroken control of the ball. Possessions moving at greater than 5 m/s on average are classed as ‘fast’. All values are for open-play possessions only. Data via Opta.

The take home message here is that the average pace of Leicester’s play has barely shifted this season compared to last. Only Burnley in 2014/15 and Aston Villa in 2013/14 have attacked at a greater pace than Leicester this season over the past four years.

The proportion of their shots generated via fast paced possessions has risen this year (from 27.5% to 32.1%) and Leicester currently occupy the top position by this metric over this period. In terms of counter-attacking situations, their numbers have barely changed this season (20.1%) compared to last season (20.8%), with only the aforementioned Aston Villa having a greater proportion (21.3%) than them in my dataset.

What has altered is the effectiveness of their attacks this season, as we can see that their expected goal figures have risen. Below are charts comparing their shots from counter-attacking situations, where we can see more shots in the central zone of the penalty area this season and several better quality chances.

LCFC_CounterAttack_Shots.png

Comparison of Leicester City’s shots from ‘fast’ and ‘deep’ attacks in 2014/15 and 2015/16. Points are coloured by their expected goal value (red = higher xG, lighter = lower xG). Any resemblance to the MK Shot Maps is entirely intentional. Data via Opta.

Their improvement this year sees them currently rank first and second in expected goals per game from fast-attacks and counter-attacks respectively over the past four season (THAT Liverpool team rank second and first). Based on my figures, Leicester’s goals from these situations are closely in line with expectations also (N.B. my expected goal model doesn’t explicitly account for counter-attacking moves).

The figure below shows how this has evolved over the past two seasons, where we see fast-attacks helping drive their improved attack at the end of 2014/15, which continued into this season. There has been a gradual decline since an early-season peak, although their expected goals from fast-attacks has reduced more than their overall attacking output in open-play, indicating some compensation from other forms of attack.

LCFC_CA_TimeLine

Rolling ten-match samples of Leicester City’s expected goals for in 2014/15 and 2015/16. All data is for open-play shots only. Data via Opta.

The effectiveness of these attacks has gone a long way to improving Leicester’s offensive numbers. According to my expected goal figures in open-play, they’ve improved from 0.70 per game to 0.94 per game this season. About half of that improvement has come from ‘fast’ paced possessions, with many of these possessions starting from deep areas in their own half.

Examining the way these chances are being created highlights that Leicester are completing more through-balls during their build-up play this season. The absolute numbers are small, with an increase from 11 to 17 through-balls during ‘fast’ possessions and from 6 to 12 during ‘fast’ possessions from their own half, but they do help to explain the increased effectiveness of their play. Approximately 27% of their shots from counter-attacks include a through-ball during their build-up this season, compared to just 11% last season. Through-balls are an effective means of opening up space and increasing the likelihood of scoring during these fast-paced moves. Leicester’s counter-attacks are also far less reliant on crosses this season, with just 2 of these attacks featuring a cross during build-up compared to 9 last season, which will further increase the likelihood of scoring.

Speed is an illusion. Leicester’s doubly so.

Overall, attacking at pace is a difficult skill to master but the rewards can be high. The pace and verve of Leicester’s attack has been eye-catching but it is the execution of these attacks, rather than the actual speed of them that has been the most important factor. Slowing Leicester down isn’t the key to stopping them, rather the focus should be either on denying them those potential counter-attacking situations or diluting their impact should you find yourself on the receiving end of one.

Whether they can sustain their attacking output from these situations is a difficult question to answer. If we examine how well output is maintained from one year to the next, the correlation for expected goals from counter-attacks is reasonable (0.55), while goal expectation per shot is lower (0.30). Many factors will determine the values here, not least the relatively small number of shots per season of this type, as well as a host of other intrinsic football factors. For fast-attacks, the correlations rise to 0.59 for expected goals and 0.52 for expected goals per shot. For comparison, the values for all open-play shots in my data-set are 0.91 and 0.63.

Examining the data in a little more depth suggests that the better counter-attacking and/or fast-paced teams tend to maintain their output, particularly if they retain managerial and squad continuity. Leicester have a good attack overall that is excellent at exploiting space with fast-attacking moves.

Retaining and perhaps even supplementing their attacking core over the summer would likely go a long way to maintaining a style of play that has brought them rich rewards.

 

Counting counters

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.

I should add that Michael Caley has published analysis and data relating to counter-attacking, although he is apparently in the process of revising these.

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

Fast_xGfor_Top20.png

Top 20 teams in terms of fast-attacking expected goals for over the past four seasons.

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

Counter_xGfor_Top20.png

Top 20 teams in terms of counter-attacking expected goals for over the past four seasons.

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

Fast_xGagainst_Top20.png

Top 20 teams in terms of fast-attacking expected goals against over the past four seasons.

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

Counter_xGagainst_Top20

Top 20 teams in terms of counter-attacking expected goals against over the past four seasons.

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

Fast_Players_Top20

Top 20 players in terms of fast-attacking expected goals per 90 minutes over the past four seasons. Minimum 2,700 minutes played.

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

Counter_Players_Top20.png

Top 20 players in terms of counter-attacking expected goals per 90 minutes over the past four seasons. Minimum 2,700 minutes played.

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.

Moving forward

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.