Preamble

Back in January, I wrote a StatsBomb article titled Measuring Changes in Attacking Style across the season for Premier League Teams. This built on some earlier work I’d done showing that clustering can be used to identify teams within similar attacking styles just from match averaged stats and some similar work looking at Championship attacking styles along with video examples. Since then I’ve presented a poster at Opta using clustering to look at fullback styles with Mark Carey at Opta and we also did a write up of our work at the Opta Pro Forum. Since then I’ve been very busy but Easter weekend has given me a chance to update my clustering work and this article will look at how similar Premier League teams are in terms of : Attacking Style, Defensive Style and Overall team style profile. All data here comes from the Whoscored database. Also I did wonder about possession adjusting the defensive stats but in the end, it doesn’t make sense doing that for a team analysis as the team playing style is reflected by how busy their defence is.

Team Summary Stats

A good first step before applying any clustering is to just look at the summary stats. Below is a heatmap showing match averaged attacking and defensive team stats, red on the heatmap means the team does this thing above the league average and blue means the team does this below the league average. Unsurprisingly the top 6 are pretty low on most defensive stats because they tend to dominate possession and therefore their defences are less busy. A couple of notable stats are that Manchester United are above the league average for fouls and interceptions. Burnley stand out in the top half too because of their last-ditch back foot defending style, a lot of clearances, blocked shots and blocked crosses. Palace’s pressing style stands out for the teams in the bottom half teams with their high levels of tackles, interceptions and blocked passes. Obviously, there are limits to just looking at league averaged stats and one simple way to look at team style similarities is to correlate team profiles (row by row below) for attacking and defending, that way the absolute numbers don’t matter just the pattern of the team profiles.

Attacking Style Clusters

Above we can see the clustermap for attacking stat profiles for EPL teams, red squares indicate high the correlations (similarity playing styles) between that pair of teams and blue squares indicate low correlations (opposing playing styles). The dendrograms in the diagram show the ‘family tree’ tree of style similarity (p.s. the clustering method used here was agglomerative). If you want a version of the clustermap with correlation figures superimposed look here.

Cluster 1. ‘Attractive’ attacking teams

We can see a few attacking style clusters. There’s an obvious cluster of the top 6 plus Crystal Palace. Manchester United stick out a bit as their style is the least similar to the other 5 of the top 6 and the majority of this difference seems to stem from their slightly higher focus on playing in wide areas and the fact they have had the lowest number of shots from open play and set pieces out of the top 6. Palace are also related to this cluster (although a bit tenuously) most of this comes the fact they play fewer short balls, carry the ball a lot through dribbles and put in a good number of crosses. Interestingly they have also had the most shot attempts inside the box outside the top 6. If they had been sharper up front they’d be chasing a Europa League place rather than battling relegation. Interestingly Newcastle are also related to this cluster (a little tenuously too) mainly driven by the fact they generate a lot of shots through counter attacks and long range efforts on goal, they also balance their play between wide areas and the middle much like the top 6 and they generate a decent amount of shots from open play.

Cluster 2. Teams with a mixed attacking approach

There’s an interesting minicluster made up of Waford, Southampton and Bournemouth. Bournemouth and Watford are somewhat similar as they have a focus on playing wide and players carrying the ball quite a lot, they also both try a decent number of shots from range. Southampton stand out in that they don’t seem to have a defined style, they are above average for number of crosses and both long and short passes but they are very near the league average on all other attacking stats, this suggests they haven’t figured out their strongest attacking plan this season.

Cluster 3. Teams that focus on width, crosses and longer passes

There’s a very well defined cluster of teams who to put it politely I’d call ‘last on Match of the Day Teams’ that includes West Ham, Everton, Brighton, West Brom, Swansea and Huddersfield. The majority of these teams focus on playing wide more than the average, they also put in a lot of crosses and play an above average amount of long balls. They all have attacking options and talented attacking players in their squads but they haven’t fired this season and it’s no coincidence that all of them bar Everton (somehow) are bottom half teams. Burnley, Stoke and Leicester are also a (distant) part of this cluster but Stoke and Leicester create a lot more shots through counter attacks than the main part of the cluster while Burnley create a lot more shots from set pieces and play the most long balls in the league.

Defensive Style Clusters

The brand new part of the analysis that wasn’t included before is the defensive style cluster analysis shown here. The dendrograms in the diagram show the ‘family tree’ tree of style similarity (p.s. the clustering method used here was agglomerative). If you want a version of the clustermap with correlation figures superimposed look here.

The defensive cluster analysis has come out in a very interesting way. Given how ‘entertaining’ the EPL has been this year (outside the top 6 teams) it’s unsurprising that the defensive clustering results make a lot of sense.

Cluster 1. Teams that press

The first cluster that comes out clearly is the ‘pressing teams cluster’ predictably this includes Spurs, Liverpool and Man City teams that all rely on fit, hardworking midfielders to help out and press the opposition to regain possession high up the field. Palace, Southampton and Huddersfield are also part of this cluster and in part that is due to Huddersfield and Palace being top 6 in the league for interceptions, blocked passes and attempted tackles. Southampton aren’t as high for interceptions and tackles but they also block a high number of passes. Leicester and Newcastle are also related to this cluster but they don’t intercept as much, but they do block more crosses and clear more balls from the box reflecting the fact they protect their penalty area in deeper positions.

Cluster 2. Teams that sit deep

The next cluster is made up of teams that sit deeper and are happy to let the play come to them, this cluster includes Brighton, Stoke, West Ham, Bournemouth, Burnley and Swansea. Looking at the stats, the style these teams employ relies a lot on last-ditch defending in and around the penalty area, they block shots and crosses and clear a lot of balls from the box. They don’t put in a high amount of tackles this is reflected in the fact that they are pretty much all below average for fouls committed. So although they are all tough to play they aren’t dirty teams.

Cluster 3. Teams that duel

The final cluster is interesting as it contains top 6 teams (Chelsea, Arsenal and Manchester United) as well as Everton, Watford and West Brom. The main thing these teams have in common is they defend on the front foot in one on one situations rather than pressing as a team; they break up a lot of attacks by committing fouls, attempting tackles and getting blocks in but they don’t bock many passes and only have a moderate number of interceptions.

Overall Team Style Clusters

All in all, there are interesting similarities in the similarities of how different teams defend and attack. Looking at these stats in isolation is interesting but getting the whole picture is important, for this reason, I repeated the analysis but included the attacking and defending team stats in the analysis. This should give us a better understanding of how teams attack and defend (if you want to see the annotated version with correlation values look here).

When combine defensive and attacking style we see that the league table reflects overall team style pretty well, which makes perfect sense. The first cluster contains the top 6, all teams with a highly positive goal difference and overall strong defences and attacks although Manchester United stand out a little bit within this cluster (mainly due to their lower attacking stats).

The other cluster basically shows everyone else who isn’t a top 6 team (although there are miniclusters within this big cluster e.g. Leicester and Bournemouth who both defend deep and try to counter-attack). The gap between top 6 and the rest has been documented well this season and it seems to be reflected in overall team style.

When we looked at defence and attack in isolation we saw mixing between top 6 and non-top 6 teams but when we look at attacking a defensive style together we see large differences. There are likely to be 2 main reasons for this. Firstly, the top 6 have a lot more firepower and they have players who can hurt any defence by playing their natural game their attacking game stays consistent whoever they play. The rest of the league have fewer options and go through streaks of poor form which hurt their attacking numbers. The second likely reason for the jumble of styles in the second cluster is that more than half of the non-top 6 teams have changed manager this season. This is also likely to hurt consistency and have an effect on the match averaged stats meaning a less clearly defined attacking plan comes out for these clubs.

Conclusion

Overall I’ve shown here that the clustering technique brings out interesting findings for both the attacking and defensive style clusters showing mixing between top 6 and non-top 6 teams when considering attack and defence in isolation. When we combine defence and attack, however, we see that 2 main, well-defined clusters come out that reflect the ambitious ‘try to win every game’ football played by the top 6 and the ‘avoid losing’ mentality of everyone else. In future, I’ll be applying this method to other leagues (hopefully with access to a richer dataset) to see whether the European Leagues are more fragmented in terms of overall team style.