Time to Tiki-Taka

Speaking to Bild this week, Giovanni Trapattoni is quoted criticising Pep’s style at Bayern Munich:

For me there’s too much possession. Tick, tack, tick, tack. Tuck, tuck, tuck. To and fro. With too little return. And after 27 minutes they shoot on the goal for the first time – that’s inefficient.

Tobi (@redrobbery on Twitter) posted some passes-per-shot numbers dispelling this myth, showing that FCB are actually pretty decent at getting regular shots on target:

I thought I’d add to this with some time-to-shot-for numbers because they are even more pronounced. Here’s the median TTSF for each team’s passing in the Champions League, that is, the average number of seconds it takes between a pass and a shot:

Team TTSF
Real Madrid 119
Bayer 04 Leverkusen 146
FC Bayern München 148
Atlético de Madrid 150
Juventus 167
Lyon 173
Galatasaray 174
Roma 175
Manchester City 181
Barcelona 188
Paris Saint-Germain 192
Olympiakos 193
Manchester United 199
VfL Wolfsburg 199
Benfica 201
FC Astana 210
Chelsea 220
Arsenal 222
FC Porto 223
Sevilla 224
Valencia CF 227
Borussia Mönchengladbach 237
Dinamo Zagreb 238
Shakhtar Donetsk 238
Dynamo Kyiv 253
KAA Gent 261
Zenit St Petersburg 267
CSKA Moscow 277
Maccabi Tel Aviv 284
PSV 315
Malmö FF 399
BATE Borisov 510

Borisov messing with my colour scheme there, but still – there you go: 2 minutes, 28 seconds, a mere factor of 10 out for Trapattoni. It’s a pretty reliable number for Bayern too, if you look here at the distributions of each team’s TTSF values for passes:

cl-pass-ttsf

Even at their absolute slowest, Pep’s team have only been about 750 seconds from tick, to tack, to shot.

Time to Tiki-Taka

PATCHing Teams

I explained the current PATCH methodology in my previous post. Today I’m going to do a deep dive into how PATCH views the current teams in the EPL. Here’s what the table looks like (pre-Southampton on Saturday):

Team PATCH
Chelsea 2.84
Manchester United 2.79
Liverpool 2.73
Tottenham Hotspur 2.70
Manchester City 2.68
Bournemouth 2.64
Arsenal 2.61
Southampton 2.45
Leicester City 2.38
Aston Villa 2.34
Norwich City 2.28
Watford 2.27
Palace 2.27
West Ham United 2.27
Everton 2.22
Swansea City 2.18
Stoke City 2.13
West Bromwich Albion 2.11
Newcastle United 1.97
Sunderland 1.88

The values for PATCH are the 60th percentile of all performances for each team. You could, if you were highly motivated, work out the actual units for PATCH, but treat it as abstract. 2 is around average, somewhere just under 4 is the 90th percentile amongst player performances and 5+ would be outstanding. Given that, I am reasonably happy with how this shapes up.

PATCH Correlations

At first glance the numbers above don’t look bonkers, but how does the metric correlate with other team defensive stats? Let’s have a look:

Percentile GA xGA Shots Against SoT Against
10th 0.06 0.10 0.08 0.06
20th 0.22 0.30 0.20 0.15
30th 0.28 0.44 0.38 0.30
40th 0.29 0.56 0.54 0.44
50th 0.27 0.61 0.63 0.45
60th 0.27 0.71 0.76 0.55
70th 0.19 0.65 0.71 0.48
80th 0.16 0.60 0.62 0.38
90th 0.10 0.47 0.50 0.29

Those are the R2 values each team’s PATCH values at a certain percentile (10th being the lowest 10%, i.e. worst defensive performances), compared to some traditional measures. It’s great to see that we’re nicely correlated with expected goals against and shots, though I should point out that shots do directly go into the calculation – if you allow a shot through your territory, it’s marked against you. However, that’s only a small proportion of the gains measured. I tested with shots removed from the ball progression metric just to be sure and the correlations barely went down.

Defensive Ranks

So far we’ve only looked at team’s performances en masse, as measured by PATCH. This is what things look like if we break them down by rank in a team’s formation:

defensive-ranks

There are a few interesting patterns that immediately jump out:

  • Bournemouth’s attacking midfielders and forwards are doing a bunch of defensive work.
  • Manchester City’s less so.
  • Tottenham have the least penetrable midfield of any team in the league.
  • As you might expect, Leicester’s attack and defence are a little more robust than their midfield, reflecting the fact that they press high then retreat low.

Lingering on these numbers a little longer, I thought I’d compare these numbers to someone else’s model for a further sanity check. Mark Thompson of Every Team Needs a Ron is one of my favourite writers, and is devoted to studying defenders in all their forms. He has a system to analyse how teams convert possessions into attacks, and attacks into shots, and how they allow their opponents to do the same. I compared the defensive rank data above with his data to see what the correlations were:

Defence Midfield Attack
Attacks per Possession 0.65 0.47 0.40
Shots per Attack 0.46 0.53 0.38

So, comparing the Attack, Midfield, Defence PATCH values from the graph above to Mark’s Attacks per Possession and Shots per Attack, we can get an idea of how much different parts of a team contribute to breaking up attacks. Defensive PATCH values explain 65% of the variance in opponent attacks per possession, whereas midfield is a much lower 47%. This makes some sense, while a lot of teams would love their midfield to quash potential attacks before they happen, it’s far more common that they make it throught to the last line. What’s interesting is the second row, where midfield performances explain shots pre attack better than defence. Again I wonder if this is bad shot quality – the defence don’t (and often don’t want to) stop low-expectation long shots. However if your midfield are putting in a good screening performance, attackers won’t even get the space for bad shots.

That’s one explanation, anyway. At the very least I’m happy to see a decent correlation with someone else’s model.

Patchwork Defences

Defences are more than the sum of their parts. There are plenty of games where teams in aggregate can put in a great performance in terms of total or average PATCH values, but still be torn apart on the field. This happens often because of mistakes, which PATCH will probably never be able to account for, but it also happens because of weak links that let down the greater whole. Have a look at Manchester City from this weekend’s absolutely definitive title decider against Leicester:

defensive-areas-1901664770408076

This is a fairly green chart – City policed a lot of territory, and in various parts of the pitch prevented Leicester from making regular gains. But look at their right-hand side: Otamendi didn’t score especially highly, and Zabaleta (who seems to be pushing quite far forward) scored even worse. Teams rightly fear Leicester’s right wing, because that’s where the formidable Mahrez nominally takes the field, but here we saw Mahrez pop up on the left a few times, including for Leicester’s 2nd goal, and Drinkwater also made some penetrative passes. We can see this from Leicester’s attacking chart for the day:

LEI-MCI

Very left leaning, basically nothing on the right. Despite the fact that City conceded twice from set-pieces, you still saw scare after scare from open play. The combination of a weak defensive right-hand side, and players taking higher positions than was perhaps advisable against the league’s fastest counter-attacking team (still 2nd in Europe after Caen), meant that good PATCH scores in many parts of the pitch did not necessarily add up to a good defensive performance.

Weak Links

Given what we saw in the Man City vs Leicester game, perhaps we should judge a defence by its weakest link? After all, if they’re allowing lots of ball progression in their area, that’s obviously where the opposition are attacking, whether or not they’re thinking of that player as exploitable. If we just look at the lowest score for a defender in each game (using just those with 90+ minutes in a game to be safe), this is what teams come out looking like:

Team Mean Weak Link PATCH
Chelsea 2.32
Manchester United 2.06
Arsenal 1.99
Manchester City 1.97
Liverpool 1.78
Aston Villa 1.77
Leicester City 1.74
Tottenham Hotspur 1.74
Southampton 1.74
Bournemouth 1.68
Swansea City 1.62
Norwich City 1.61
Crystal Palace 1.59
West Bromwich Albion 1.58
Watford 1.54
Everton 1.53
West Ham United 1.48
Stoke City 1.48
Newcastle United 1.45
Sunderland 1.45

Nothing radically different here, perhaps I should be a little uncomfortable seeing Villa that high, but they have save percentage and shot creation issues, not necessarily an awful defence. That said, these numbers correlate less well with each of the four measures we compared to earlier, so it seems less representative.

Total Territory

PATCH fundamentally rewards defenders for claiming territory, so lets look into any team characteristics can we pick up from looking at their territory as a whole. Who uses the most space? Who leaves the most gaps?

This is total area per game of players’ defensive territory for each team, measured first as the sum of individual areas, then as a merged team area:

Team Total Individual Area Team Area
Arsenal 12375 5286
Aston Villa 12338 5590
Bournemouth 12487 5540
Chelsea 13385 5612
Crystal Palace 14240 5750
Everton 10494 5181
Leicester City 14580 6078
Liverpool 12817 5626
Manchester City 12025 5405
Manchester United 13156 5438
Newcastle United 11767 5003
Norwich City 11949 5614
Southampton 12885 5525
Stoke City 11464 5347
Sunderland 12880 5560
Swansea City 10882 5052
Tottenham Hotspur 13520 5522
Watford 12482 5779
West Bromwich Albion 12943 5694
West Ham United 14022 5693

Which looks like this:

territorial-area

The X axis is the total individual area, which includes overlaps between players. The Y axis is the team shape, the area you get when you merge all the individual territories together and forget overlaps – also worth noting that the lower this value, the more empty spaces the team is leaving on the pitch.

It’s interesting because it reveals teams that are quite expansive in their defensive efforts (to the right are basically the pressers and aggressors, to the left is… Everton, asking very little of its defence). It also shows teams that have an overall compact defensive shape (Newcastle) versus those that are push up more (Leicester, Watford). Above the trend line are teams with less overlap, below are those that are more crowded when defending.

If we apply a similar sort of calculation to PATCH, we can take a team’s area and judge them not by the progression they allow through their territory, but by the progression that happens outside it. If we do that, these are the numbers we see:

Team Outside Territory PATCH
Manchester City 24.09
Liverpool 23.55
Norwich City 22.78
Southampton 22.04
Leicester City 20.77
Watford 20.75
Tottenham Hotspur 20.59
Aston Villa 20.22
West Bromwich Albion 20.19
Crystal Palace 19.37
Bournemouth 19.35
West Ham United 19.26
Chelsea 18.34
Manchester United 17.94
Arsenal 16.98
Swansea City 16.51
Stoke City 16.36
Sunderland 15.96
Everton 13.31
Newcastle United 11.28

So Man City, Liverpool and… Norwich (apparently) allow the least progression outside their territory. Newcastle and Everton leave the biggest gaps for opponents to operate inside.

Getting Goal Side

Above you saw how a lot goes on in empty spaces. The thing that worries me most about PATCH, and particularly the approach I’ve taken to trimming events for territory, is space behind a defender. Perhaps we should leave in all goal side events for a defender? Even more, should we project their territory back to the goal line, or even the goalmouth itself?

Well, you’re going to have to wait to find out. In my next post I’m going to finally get around to looking at some individual player scores, and I’ll experiment with how defenders should be blamed for goal side events then.

PATCHing Teams

Arsenal’s Injury Woes: Changing Directions

An interesting conversation broke out on Twitter tonight about the timeless mystery of Arsenal’s injury record. Personally, I’m with Raymond Verheijen – Arsene Wenger should stop holding Running Man style training sessions with chainsaws and stuff, that’s just common sense. But what other factors might be at play?

Naveen Maliakkal wondered if something about Arsenal’s style might contribute:

I’d love to see how much recover sprinting arsenal have to do since they don’t rely enough on stopping counters high up the pitch and instead trying to recover into deep positions then from rather deep positions they attempt to counter. Essentially it seems like then play a style that relies a lot on covering large distances quickly.

This piqued my interest, and I wondered if all this running backwards and forwards might be quantifiable. So I came up with a simple approach:

  1. For every player, take the list of their touches in a game.
  2. Split them into sets of three – (1) where the player was, (2) where they currently are and (3) where they will be next.
  3. Draw a line between 1 and 2, and 2 and 3.
  4. Calculate the difference in angle between these two lines, i.e. how much the player has to turn.
  5. Sum all of this for each team in each season.

Picture some examples:

total-angle

So, three touches, all going forwards in a straight line is an angle of zero – the player hasn’t turned at all. Turning either direction, left or right, is measured the same, and of course the maximum angle is 180° if the player makes a forward touch and then goes directly backwards to make another. The numbers below are actually done in radians, but I didn’t want to frighten anyone.

Whether or not that makes sense, what it roughly measures is how much back and forth in total each team’s bodies have had to go through. Guess who put in five out of the top ten EPL seasons?

Season Team Total Angle Turned
2014 Manchester City 117061
2013 Arsenal 114293
2012 Arsenal 112857
2014 Arsenal 112388
2013 Swansea City 112267
2011 Arsenal 111055
2014 Manchester United 110062
2011 Manchester City 110017
2010 Arsenal 109965
2010 Chelsea 109663

Arsenal appear five times in the top ten – year after year, their players are changing direction more than pretty much any other team.

Now, let me throw some caution on this approach:

  • I don’t take timestamps into account, so you don’t know if there’s a second or five minutes between touches, but this is the same for all teams and is hopefully evened out in the aggregate.
  • This doesn’t capture how players actually move, as they can run sideways and backwards.
  • Arsenal would necessarily appear at the top, because they are a dominant, attacking team that has lots of possession and moves the ball around a lot (like the Manchesters and Chelseas you see up there). This is also true, but maybe playing well hurts.
  • I haven’t checked the correlation between these numbers and historical injury data. For example Newcastle don’t place highly here but are having a nightmare this season, with 10 players out. I’ll attempt to gather some data tomorrow to see what correlation exists.

But at the very least, the fact that Arsenal hover near the top of the list every single year is intriguing, and I must thank Naveen again for pointing this out.

Arsenal’s Injury Woes: Changing Directions

Christmas Shopping: Goalkeepers

The nights are getting longer up here in the Northern Hemisphere, and soon children will be donning their traditional transfer window jumpers and gathering around open fires to sing traditional transfer window songs. In preparation for the festive season, I’m going to think about teams with really obvious deficiencies, and work out what Santa’s elves might be able to fax over on deadline day to fix them.

We’re going to start with goalkeepers, because frankly it’s easiest to draw up a naughty list of of rubbish keepers using our expected saves model. Below is the list of all keepers that have on average underperformed in the last five seasons, i.e. they’ve made fewer saves than the expected saves model expected. The rating is simply saves over expected saves, times 100. 100 is a keeper that saved exactly what the model thought they should, over is good, under is bad.

An aside as an Everton fan: I am going to note here that the player just above this list, who only just scraped a rating of 100.1, is Tim Howard. I don’t believe he’s as bad as most Everton fans like to make out (he’s just above Joe Hart in this year’s ratings, basically in the middle of the pack), but those that want to play along can by all means picture my recommendations below as applying to Everton as well (or indeed whichever team you happen to support). Just note that whoever Everton might get in will be facing the second most shots of any keeper in the Premier League, and mistakes will be made.

Keeper Season
2010 2011 2012 2013 2014 2015 Avg
Simon Mignolet 99.3 101.5 107.7 96.6 98.2 93.3 99.4
Julian Speroni 103.1 95.1 99.1
Tom Heaton 98.7 98.7
Richard Kingson 98.7 98.7
Adam Federici 98.4 98.4
Ben Hamer 98.2 98.2
Ali Al-Habsi 102.2 100.2 92.1 98.2
Matthew Gilks 98.0 98.0
John Ruddy 100.4 100.1 98.3 92.9 97.9
Robert Elliot 92.3 97.6 102.9 97.6
Brad Friedel 94.4 101.7 96.4 97.5
Bradley Jones 97.4 97.4
Kasper Schmeichel 98.7 95.9 97.3
Costel Pantilimon 90.9 104.5 96.4 97.3
David Marshall 97.2 97.2
Paulo Gazzaniga 103.6 90.3 97.0
Tim Krul 87.2 101.2 99.7 101.0 95.4 95.3 96.6
Boaz Myhill 85.4 108.8 87.4 104.9 96.1 96.5
Thomas Sørensen 99.4 99.7 89.9 96.3
Steve Harper 97.4 87.0 96.4 104.5 96.3
Adam Bogdan 93.3 99.3 96.3
Mark Bunn 96.3 96.3
Marcus Hahnemann 96.2 96.2
Robert Green 97.2 91.6 99.9 96.2
Gerhard Tremmel 102.5 88.2 95.3
Wayne Hennessey 95.6 100.4 89.9 95.3
Anders Lindegaard 107.0 83.3 95.1
Brad Guzan 98.1 97.8 92.6 96.7 89.9 95.0
Joel Robles 92.5 96.0 94.3
Kelvin Davis 90.5 96.9 93.7
Paul Robinson 101.3 85.9 93.6
Artur Boruc 95.3 100.4 85.0 93.5
Scott Carson 93.1 93.1
Patrick Kenny 91.4 91.4
Allan McGregor 93.7 87.4 90.5
Maarten Stekelenburg 93.9 83.1 88.5
Dorus de Vries 85.8 85.8
Stuart Taylor 81.2 81.2

There are a few main things I want to note here:

  1. Southampton have terrible taste in keepers – Boruc, Davis, Stekelenburg, all generally underperforming expected saves. Fraser Forster may come good, but until then, Southampton’s overall organisation is covering up a lack of quality between the posts.
  2. Bournemouth are in real trouble – Boruc isn’t great (not shown here is his 3 mistakes leading to goals already this year), and Adam Federici hasn’t done much better, but he’s left off this table as he’s below the 10-save cutoff. On top of these fairly poor performances is the fact that the shots Bournemouth are allowing are far, far trickier than any other team in the league (0.42xg against Boruc, 0.48 against Federici, against a league average of about 0.3), so literally anyone in their goalmouth would struggle.
  3. Brad Guzan is the only keeper consistently, year after year, to underperform expected goals but keep his place. The 100-based ratings actually boost him up the table a bit – in terms of raw goals above/below expected, Guzan is last this year, last in 2013, and firmly bottom 6 every season he plays. That’s partly Aston Villa’s woeful defence, but I do not know how Guzan has kept his place for so long.

Of this year’s relegation candidates, Robert Elliot, standing in for Tim Krul at Newcastle, is the only keeper to be performing above expected saves, by a teeny 0.3 goal margin. Pantilimon at Sunderland is poor but not the worst, Bournemouth would probably benefit more from a defensive shakeup to reduce the quality of chances conceded, and I think that leaves Aston Villa as the prime candidates for an upgrade. I might argue in a future post that their defence needs patching (*cough* Alan Hutton *cough*), but they’re conceding chances with an average 0.25xg which isn’t terrible. Guzan, however, is four goals down on where he should be this season and if history’s anything to go by, he’s going to get continue leaking goals. This is the last five seasons in detail:

Season Mins Shots Saves Goals Save % Expected Saves +/- Expected Shot Difficulty Rating
2015/16 1134 58 39 19 67% 43.4 -4.4 25.2 89.9
2014/15 3201 148 101 47 68% 104.5 -3.5 29.4 96.7
2013/14 3570 167 110 57 66% 118.8 -8.8 28.9 92.6
2012/13 3385 174 114 60 66% 116.6 -2.6 33.0 97.8
2011/12 620 26 18 8 69% 18.3 -0.3 29.4 98.1

So it kinda goes without saying, looking at the historical data above, that Villa could have sorted this out over the Summer, or last year, or the year before. But we’re entering a hypothetical world here where teams might agree to sell their first-choice goalkeeper in the January window, and those keepers might agree to join a team at or near the bottom of the Premier League, plus or minus any sort of reaction that Remi Garde gets between now and then. Let’s assume that nobody is going to drop down from a team above Villa to help out, otherwise I’d probably just point at Jack Butland and be done with it. Villa have been bringing in youth over the Summer, so let’s look at keepers 25 and under in Europe, playing at teams not currently in European competition, with decent ratings from our model. Let’s just assume that Premier League TV money is enough to land one of these targets. Who’s out there?

Keeper Mins Shots Saves Goals Save % Expected Saves +/- Expected Shot Difficulty Rating
Timo Horn 4155 231 177 54 76.6% 165.2 11.8 28.1 107.2
Gerónimo Rulli 2922 133 93 40 69.9% 87.9 5.1 33.1 105.8
Julián 1491 101 75 26 74.3% 71.1 3.9 20.2 105.5
Loris Karius 6208 349 257 92 73.6% 244.8 12.2 29.6 105.0
Benjamin Lecomte 4968 246 178 68 72.4% 171.0 7.0 29.1 104.1
Alphonse Areola 4386 183 131 52 71.6% 126.3 4.7 33.2 103.7
Marco Sportiello 4881 269 197 72 73.2% 191.1 5.9 30.2 103.1
Mattia Perin 9428 548 388 160 70.8% 380.0 8.0 30.6 102.1
Nicola Leali 3587 195 135 60 69.2% 132.9 2.1 31.3 101.6
Oliver Baumann 10447 573 406 167 70.9% 404.9 1.1 29.2 100.3

I’ve snuck Alphonse Areola in here despite the fact that he’s on a season long loan, just because he is/was vaguely available in principle. Any of these players, dead or alive, would probably be an improvement, and it seems like the transfer rumour mill, and potentially even Villa’s scouts, are ahead of me, they’ve been linked with Mainz’s Karius, and indeed Timo Horn. I don’t have Championship data, or smaller foreign leagues, so I will rely on those of you with eyes to fill me in there.

It’s worth noting that perhaps these numbers miss important parts of a modern goalkeeper’s game: Paul Lambert certainly rated Guzan’s distribution, we ought to look into that. Here’s everybody’s overall passing numbers:

Keeper Passes Completed Ratio
Oliver Baumann 4898 3096 0.63
Timo Horn 1537 953 0.62
Loris Karius 2633 1560 0.59
Gerónimo Rulli 973 574 0.59
Marco Sportiello 1616 931 0.58
Alphonse Areola 1383 798 0.58
Nicola Leali 1122 651 0.58
Mattia Perin 3167 1839 0.58
Benjamin Lecomte 1690 939 0.56
Brad Guzan 4455 2450 0.55
Julián 473 234 0.49

And here’s everything over 40 yards:

Keeper Passes Completed Ratio
Gerónimo Rulli 666 295 0.44
Nicola Leali 779 337 0.43
Brad Guzan 3181 1319 0.41
Marco Sportiello 1034 417 0.40
Julián 370 149 0.40
Oliver Baumann 2592 940 0.36
Benjamin Lecomte 1064 378 0.36
Timo Horn 857 305 0.36
Loris Karius 1527 548 0.36
Alphonse Areola 836 290 0.35
Mattia Perin 1845 641 0.35

So Guzan has 5% over Timo Horn on long balls, take it or leave it.

It remains to be seen whether Aston Villa’s transfer window tree will be sheltering a Timo Horn-shaped present this holiday season – I nearly ran the numbers on January goalkeeper transfers to see if it happened that regularly – but I’ll leave that for the more enterprising of you. It’s possible these targets have been approached and Villa have neither the ambition nor the spending power to land any of them. All you can ask for in your letters to Lapland this year is that Remi Garde gets Villa’s Summer signings to gel into some sort of attacking unit, Jack Graelish stops being peak-Ross Barkley wasteful, and someone keeps putting their face in the way of the ball.

Christmas Shopping: Goalkeepers

Prime Creators

Time for a big, long Monday-morning table. Given our attacking buildup data, turns out it’s easy to calculate the number of attacking moves (moves that lead to shots, remember) in which each European player has been involved. That in turn makes it easy to identify each team’s prime creator – the player involved in the most attacking moves per 90, whether it be passes, shots, dribbles, whatever. The cut-off is 450 minutes, here they are:

Team Player Attacks p90
Napoli Lorenzo Insigne 11.72
Arsenal Mesut Özil 10.84
Manchester City Kevin De Bruyne 10.83
Real Madrid Cristiano Ronaldo 10.80
Barcelona Neymar 10.77
Juventus Paulo Dybala 10.38
Paris Saint-Germain Ángel Di María 9.82
Lyon Mathieu Valbuena 9.50
Celta de Vigo Nolito 9.47
FC Bayern München Douglas Costa 9.41
Roma Miralem Pjanic 9.21
Fiorentina Josip Ilicic 8.99
Bayer 04 Leverkusen Hakan Calhanoglu 8.92
VfB Stuttgart Daniel Didavi 8.62
Internazionale Stevan Jovetic 8.53
West Ham United Dimitri Payet 8.48
Milan Giacomo Bonaventura 8.33
Borussia Dortmund Henrikh Mkhitaryan 8.24
Tottenham Hotspur Christian Eriksen 8.20
Liverpool Philippe Coutinho 8.16
Marseille Abdel Barrada 8.15
Sevilla Michael Krohn-Dehli 8.07
Chelsea Cesc Fàbregas 7.98
Empoli Riccardo Saponara 7.65
FC Schalke 04 Julian Draxler 7.57
Swansea City Jonjo Shelvey 7.49
Chievo Valter Birsa 7.48
Palermo Franco Vázquez 7.43
Norwich City Nathan Redmond 7.35
FC Augsburg Caiuby 7.29
Bordeaux Wahbi Khazri 7.26
Atalanta Maximiliano Moralez 7.17
Rayo Vallecano Jozabed 7.16
Southampton Dusan Tadic 7.07
Everton Ross Barkley 7.05
Deportivo de La Coruña Luis Alberto 7.03
Caen Andy Delort 6.92
SV Werder Bremen Zlatko Junuzovic 6.88
Sassuolo Domenico Berardi 6.88
FC Ingolstadt 04 Pascal Groß 6.85
Monaco Stephan El Shaarawy 6.82
Genoa Diego Perotti 6.81
Manchester United Memphis Depay 6.78
Udinese Francesco Lodi 6.75
Málaga Duda 6.69
Eibar Saúl Berjón 6.66
Guingamp Yannis Salibur 6.62
Athletic Club Raúl García 6.58
Lazio Keita 6.56
Atlético de Madrid Antoine Griezmann 6.53
Watford Almen Abdi 6.53
Leicester City Riyad Mahrez 6.49
Nice Jean Seri 6.49
Frosinone Robert Gucher 6.44
Espanyol Marco Asensio 6.39
VfL Wolfsburg André Schürrle 6.31
Valencia CF Daniel Parejo 6.29
Newcastle United Ayoze Pérez 6.27
Las Palmas Jonathan Viera 6.17
Real Sociedad Rubén Pardo 6.16
Lorient Yann Jouffre 6.10
Angers Thomas Mangani 6.06
Crystal Palace Bakary Sako 6.05
Borussia Mönchengladbach Ibrahima Traoré 6.05
Sunderland Adam Johnson 6.03
Montpellier Ryad Boudebouz 5.98
Getafe Pedro León 5.97
Levante Morales 5.86
Rennes Kamil Grosicki 5.82
Nantes Jules Iloki 5.81
St Etienne Nolan Roux 5.76
Lille Sofiane Boufal 5.75
Troyes Corentin Jean 5.72
Toulouse Óscar Trejo 5.67
Hannover 96 Hiroshi Kiyotake 5.65
Sampdoria Éder 5.58
Bournemouth Matt Ritchie 5.52
Bologna Franco Brienza 5.40
GFC Ajaccio Damjan Djokovic 5.35
Verona Federico Viviani 5.27
Carpi Matteo Fedele 5.25
Eintracht Frankfurt Marc Stendera 5.24
Stoke City Marko Arnautovic 5.18
Hamburger SV Lewis Holtby 5.08
Granada CF Rubén Rochina 5.04
West Bromwich Albion James Morrison 5.02
Hertha BSC Vladimir Darida 5.02
Real Betis Joaquín 4.97
SV Darmstadt 98 Konstantin Rausch 4.95
1. FSV Mainz 05 Yoshinori Muto 4.90
Bastia Sadio Diallo 4.86
Aston Villa Rudy Gestede 4.79
1. FC Köln Anthony Modeste 4.74
TSG 1899 Hoffenheim Eduardo Vargas 4.71
Reims Nicolas de Preville 4.71
Sporting de Gijón Alen Halilovic 4.63
Torino Daniele Baselli 4.60
Villarreal Manu Trigueros 4.22

Özil only just beating out half a dozen or so other Arsenal players in that free-wheeling attack, de Bruyne sneaking up on a limited number of minutes, and someone explain Villareal to me. Turns out they’re 5th, but they don’t appear to attack much?

Prime Creators

Europe’s Most Direct Teams

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)
Caen 3.63
SV Darmstadt 98 3.33
Leicester City 3.26
Villarreal 3.24
Crystal Palace 3.17
FC Ingolstadt 04 3.16
Eibar 3.16
TSG 1899 Hoffenheim 3.13
Sevilla 3.13
Sporting de Gijón 3.11
VfB Stuttgart 3.04
Eintracht Frankfurt 2.98
Lille 2.95
Guingamp 2.94
Carpi 2.94
1. FSV Mainz 05 2.94
Troyes 2.93
St Etienne 2.92
Angers 2.91
Toulouse 2.88

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)
Manchester City 2.26
Swansea City 2.24
FC Augsburg 2.20
FC Bayern München 2.17
VfL Wolfsburg 2.07
Fiorentina 2.07
Nice 2.05
Paris Saint-Germain 2.05
Juventus 2.00
Manchester United 1.92

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)
2015 Barcelona 2.78
2013 FC Bayern München 2.33
2012 FC Bayern München 2.33
2013 Barcelona 2.27
2014 Barcelona 2.27
2012 Barcelona 2.24
2015 FC Bayern München 2.17
2014 FC Bayern München 2.16
Europe’s Most Direct Teams

Attacking, Fast and Slow

Note 05/11/2015: the original numbers published here were based on some faulty data – I found attacking moves that persisted even when the opposition won the ball back. This had the effect of making most moves seem longer and wider. Fortunately, after fixing that, Man Utd stay at the top and Leicester at the bottom, the changes at Newcastle and West Ham still seem real, and the correlation from year-to-year is now even stronger. Many thanks to the commenters on this post whose Tottenham spidey-sense caused me to take a second look.

I’ve been curating some data so that I can look at teams’ attacking buildup play. There’s some more in-depth stuff coming, but I thought it was fascinating to just look at the basic geometry of different teams’ attacks. So, let’s measure the passing moves that lead to shots (rebounds and direct free kicks excluded), starting with a turnover or dead ball situation. For each team I’ve calculated:

  • the average ‘X’ position, this is the position of the ‘centre’ of the move, as a percentage up the the field towards the opponent’s goal, 50 being the half-way line
  • the average width of the move, sideline to sideline, with 50 being half the pitch
  • the average height of the move, box to box, again 50 being half the pitch
  • the average duration in seconds

Let’s have a look, ordered by duration:

Team Average X Width Height Duration
Manchester United 50.3 54.0 42.6 28.7
Manchester City 48.1 55.8 43.7 25.6
Swansea City 44.2 55.7 42.7 24.0
Everton 49.7 55.5 42.8 22.4
Newcastle United 44.5 55.2 43.6 22.2
Chelsea 44.1 52.2 43.3 21.9
Stoke City 43.0 54.1 44.5 21.0
Liverpool 43.2 50.1 39.6 20.9
Norwich City 55.4 51.5 38.9 20.1
Arsenal 46.3 49.2 45.0 19.8
Tottenham Hotspur 43.1 50.3 41.7 19.8
West Bromwich Albion 47.8 50.9 40.5 19.8
Sunderland 47.0 51.9 42.1 19.6
Aston Villa 45.8 50.5 40.0 19.3
Southampton 48.3 50.3 38.6 19.3
West Ham United 48.5 52.8 40.3 19.1
Watford 44.9 48.2 41.3 18.6
Bournemouth 55.0 50.1 33.8 17.9
Crystal Palace 47.3 48.9 38.9 16.9
Leicester City 51.0 45.5 40.9 16.0

The incredibly ponderous Man Utd sit at the top of the table, giving themselves on average 28 seconds with the ball in the buildup to their shots. At the bottom are Leicester City – could they be the most direct team in the Premier League? Looking at their width number, they also seem to use less space on average in their attacks than any other team. Part of the reason is that the space you have to cover and the time it takes to cover it are of course tightly linked, but look at West Ham – short-lived attacks but making use of much more width.

What’s more interesting about these numbers it that they seem to hold over from season to season. Part of that is obviously the quality of players at a club – Leicester’s success aside, you would probably bet on the wider, slower teams in the top half of the table to secure European places over the bottom half. Better players can keep the ball longer, and can move the ball about the park more easily, so we’d expect the best teams to have higher width and duration numbers. That said, I did a quick check and the average buildup time for goals is the same as all shots, around 18-19 seconds – there’s no indication that quicker or slower is necessarily more effective in creating goals.

Here’s last year’s table:

Team Average X Width Height Duration
Manchester City 47.0 54.0 43.3 25.8
Everton 43.9 54.3 44.0 25.1
Manchester United 47.1 59.3 40.3 25.0
Chelsea 50.0 52.1 40.8 24.0
Liverpool 44.6 54.3 43.7 23.3
Southampton 47.9 52.8 40.2 21.3
West Bromwich Albion 44.1 51.5 39.6 21.2
Arsenal 50.6 48.3 40.1 21.1
Aston Villa 46.9 54.5 40.5 20.9
Swansea City 42.9 52.6 39.7 20.5
Tottenham Hotspur 43.2 52.5 40.2 20.4
Sunderland 45.7 51.9 39.5 20.3
Stoke City 44.3 51.8 40.4 19.4
Hull City 48.0 53.1 39.1 19.4
Queens Park Rangers 47.5 49.4 37.5 17.3
West Ham United 51.6 47.7 36.7 16.5
Leicester City 49.5 45.2 37.8 16.0
Newcastle United 48.3 46.2 37.8 15.9
Crystal Palace 47.8 46.1 38.2 15.5
Burnley 53.7 50.5 37.3 15.3

Almost all teams post similar numbers (duration has an R2 of about 0.8), and again you could easily explain that as player quality, but for the radical changes at Newcastle, who sacked John Carver and replaced him with Steve McClaren over the Summer. They’re now taking a whole extra 6 seconds in the buildup to their shots, and using 9% more of the field to do it in. West Ham brought in Slaven Bilic and have added 2.5 seconds and 5 percent more space. There are more outliers that spoil the story a bit – Man Utd were very wide in 2014, less so now, and both they and Swansea are building up a lot slower this year), but I think these duration and width numbers – how fast, how directly you attack – are a very clear part of a manager’s signature.

Attacking, Fast and Slow