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: Tottenham Edition

One of the numbers that stuck out to me when I calculated the data in Attacking, Fast and Slow, was the slow pace of Tottenham’s attack. We know Pochettino values winning the ball back high up the pitch and launching fast-paced counter-attacks, so why isn’t that clearer from the data? In the light of Jake Meador‘s piece today which had a few commenters quite rightly puzzling over my numbers, I thought I’d look deeper and work out where the bugs were in my approach, and how better to present the data so it captures the nuances in Tottenham’s attack.

First off, there were issues with my data, and I’ve added a note and updated numbers to the the last post. Luckily, most of my conclusions remain correct (and in fact the correlation of attack duration from year to year is even stronger in the fixed data), but one of the big movers are indeed Tottenham, who now reside in the bottom half for attack duration.

Let’s look in more detail at exactly how quick each team’s attacks are. Here I’ve broken attacks down into 5 second buckets up to 30s, and sorted by the 0-5s bucket:

Team 0-5s 5-10s 10-15s 15-20s 20-25s 25-30s 30s+
Southampton 47 22 20 13 10 4 28
Tottenham Hotspur 40 20 21 15 18 11 29
Arsenal 39 21 32 25 17 12 35
Liverpool 38 27 16 9 11 7 38
Aston Villa 34 17 9 7 8 5 23
Leicester City 34 30 28 21 8 2 20
Norwich City 33 23 12 16 13 7 29
Crystal Palace 33 21 19 13 9 4 17
Manchester City 33 24 30 16 14 13 48
Bournemouth 30 22 10 10 6 7 22
Watford 30 22 21 11 11 5 25
Chelsea 28 17 23 11 10 11 29
West Bromwich Albion 25 14 14 8 5 5 20
West Ham United 25 27 15 16 8 9 26
Sunderland 24 9 14 9 11 6 18
Everton 23 16 15 18 7 10 29
Newcastle United 19 23 14 10 7 9 23
Swansea City 18 26 8 17 15 8 32
Stoke City 16 14 15 6 14 6 20
Manchester United 16 14 10 8 9 34

And here’s what that looks like stacked up together:
shot-durations

Well that matches our intuitions much better – the two teams we know share Pochettino’s desire for quick attacks off turnovers are right there are the top, with more shots within 5s than anyone else, and with 10s numbers that stack up pretty well too. I initially worried that these numbers might just be a side-effect of weird corner numbers, but Tottenham and Southampton sit 8th and 9th in corner count.

If you look at Tottenham’s numbers in the aggregate, they’re slowed down by patient build-up play. Despite the contrast in the chart above, they can be similar to Man Utd – moving the ball from side to side, waiting for an opportunity to open up. If you remember my passing gains chart, Tottenham’s passing on average in the centre of the field is backwards. They probe forwards on the wings, recycle backwards into the centre. Eventually this leads to shots that have taken a lot of time and space in the build-up, and I’ll certainly look for better ways to categorise this. But hopefully with the approach above, people are at least now seeing the Tottenham they know and love.

Attacking, Fast and Slow: Tottenham Edition

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

Ultimate xG Suckerpunches

This made gruesome viewing during Michael Caley‘s xG roundup of yesterday’s Europa League games:

xG map for @AZAlkmaar-Augsburg. Man oh man does that look like a harsh result for AZ.

— Michael Caley (@MC_of_A) October 22, 2015

I don’t actually have Europa League data, but I thought it would be fun on a Friday afternoon to find all the games where the team with superior xG has been defeated 1-0 by the lowest xG shot in the game. Here is the small and miserable group:

Date Home Team Away Team Home Score Away Score Winning Goal xG Home xG Away xG
2013-10-21 19:00:00 Celta de Vigo Levante 0 1 0.027402 1.06464 0.343559
2014-04-12 15:07:00 Stoke City Newcastle United 1 0 0.029598 1.142455 1.444735
2014-05-18 17:00:00 Real Valladolid Granada CF 0 1 0.027402 0.948084 0.605783
2014-10-05 16:00:00 Guingamp Nantes 0 1 0.02931 1.259913 0.814567
2014-12-28 15:00:00 Hull City Leicester City 0 1 0.031815 1.865783 0.251423
2015-02-07 19:00:00 Evian Thonon Gaillard Bordeaux 0 1 0.03291 0.62993 0.358794

Of these, the most savage seems to be Hull and Leicester’s Christmas relegation bout, decided by a bouncing Mahrez shot from outside the box, and not cancelled out despite Hull’s 19 shots during the game. A bit of luck apparently goes a long way, and we all know how this tale ended for the two teams involved, with Hull relegated, and Leicester’s luck continuing.

Ultimate xG Suckerpunches

In the Gaps Between Models

In my Anatomy of a Shot I hinted that we might measure different component parts of xG and compare them. That’s exactly what I’m going to do in this post – take what I call chance quality, a form of xG that includes positional data but excludes the shot itself, and compare it to my expected save value for that shot. Because think about what happens between those two measurements – the first model says, “in general, teams have such-and-such a chance of scoring from some sort of shot over here”, the second says “shit, did you see that? He must have a foot like a traction engine.

What comes between those two models? Well, something resembling finishing quality, or at least good decision making. Even if a player isn’t converting a ton of chances, if they’re reliable making shots more difficult to save, they’re shooting well. If they’re taking prime quality chances but making them easy to save, well, maybe that’s rubbish shooting. That’s the theory at least, what do the numbers look like? Here’s everyone with 20+ shots in the Premier League this year:

Player Shots On Target Goals SoTR Conv% Chance Quality Save Difficulty SD/CQ SD-CQ
Olivier Giroud 23 12 4 52.17% 17.39% 14.43% 20.57% 142.57% 6.14%
Juan Mata 20 7 3 35.00% 15.00% 9.86% 15.08% 153.01% 5.23%
Sergio Agüero 33 14 6 42.42% 18.18% 12.44% 17.64% 141.77% 5.20%
Bafétimbi Gomis 23 11 4 47.83% 17.39% 14.17% 17.06% 120.40% 2.89%
Harry Kane 33 12 2 36.36% 6.06% 9.43% 12.28% 130.20% 2.85%
Ross Barkley 28 9 2 32.14% 7.14% 5.30% 7.92% 149.30% 2.61%
Jamie Vardy 38 17 9 44.74% 23.68% 15.73% 17.96% 114.18% 2.23%
Sadio Mané 26 10 2 38.46% 7.69% 9.42% 11.65% 123.63% 2.23%
Yohan Cabaye 21 8 4 38.10% 19.05% 16.11% 17.99% 111.70% 1.88%
Romelu Lukaku 28 12 5 42.86% 17.86% 12.59% 13.46% 106.91% 0.87%
Riyad Mahrez 25 11 5 44.00% 20.00% 13.27% 13.70% 103.22% 0.43%
Theo Walcott 26 12 2 46.15% 7.69% 14.68% 15.09% 102.83% 0.42%
Odion Ighalo 26 9 5 34.62% 19.23% 9.56% 9.61% 100.49% 0.05%
Graziano Pellè 38 11 5 28.95% 13.16% 12.60% 12.33% 97.88% -0.27%
Alexis Sánchez 45 15 6 33.33% 13.33% 12.17% 11.39% 93.53% -0.79%
Memphis Depay 25 8 1 32.00% 4.00% 8.09% 7.13% 88.15% -0.96%
Diafra Sakho 29 10 3 34.48% 10.34% 13.32% 11.82% 88.76% -1.50%
Philippe Coutinho 39 11 1 28.21% 2.56% 7.23% 5.68% 78.63% -1.54%
Santiago Cazorla 20 7 0 35.00% 0.00% 6.92% 5.28% 76.24% -1.64%
Jonjo Shelvey 21 7 0 33.33% 0.00% 4.83% 2.92% 60.54% -1.90%
Gnegneri Yaya Touré 26 8 1 30.77% 3.85% 9.23% 6.49% 70.28% -2.74%
Rudy Gestede 22 7 3 31.82% 13.64% 10.96% 8.10% 73.97% -2.85%
Aaron Ramsey 27 8 1 29.63% 3.70% 10.09% 6.94% 68.81% -3.15%
Jason Puncheon 20 3 0 15.00% 0.00% 7.08% 1.74% 24.65% -5.33%
Troy Deeney 23 4 0 17.39% 0.00% 8.43% 1.62% 19.28% -6.80%

I should note that the save difficulty number here, because I want an aggregate over all their shots, counts off-target shots as a save difficulty on zero. The raw number obviously averages out roughly to the global conversion rate of on-target shots (around 30%). So, we can see some players increase the average difficulty of their shots for keepers, others make them easier. I’ve calculated both the ratio (i.e. Juan Mata increases his shots’ difficulty by 1.5x), and the difference, (i.e. Juan Mata increased his shot quality of around 10% to a save difficulty of around 15%).

cq-vs-sd

To the right are better chances, top the top are better shots. You can see examples like Olivier Giroud and Sergio Agüero, who are making already quite good chances even scarier, Ross Barkley’s making bad chances look very slightly more exciting, and Jason Puncheon and Troy Deeney just need to stop.

Let’s look at a bigger sample, here’s 2014, 50+ shots:

Player Shots On Target Goals SoTR Conv% Chance Quality Save Difficulty SD/CQ SD-CQ
Nacer Chadli 54 22 11 40.74% 20.37% 9.69% 16.00% 165.11% 6.31%
Steven Gerrard 55 22 10 40.00% 18.18% 13.18% 18.66% 141.62% 5.48%
Olivier Giroud 70 29 14 41.43% 20.00% 11.49% 15.67% 136.36% 4.18%
Diego Da Silva Costa 76 37 20 48.68% 26.32% 15.22% 19.36% 127.25% 4.15%
Harry Kane 113 48 22 42.48% 19.47% 11.65% 15.71% 134.87% 4.06%
David Silva 66 27 12 40.91% 18.18% 11.51% 15.26% 132.61% 3.75%
Eden Hazard 78 33 14 42.31% 17.95% 13.94% 16.87% 121.04% 2.93%
Aaron Ramsey 63 17 6 26.98% 9.52% 8.56% 10.81% 126.27% 2.25%
Wayne Rooney 79 27 12 34.18% 15.19% 10.89% 12.66% 116.25% 1.77%
Mame Biram Diouf 55 22 11 40.00% 20.00% 17.00% 18.71% 110.01% 1.70%
Robin van Persie 76 37 10 48.68% 13.16% 13.27% 14.92% 112.41% 1.65%
Ayoze Pérez Gutiérrez 61 24 7 39.34% 11.48% 10.37% 12.02% 115.85% 1.64%
Bafétimbi Gomis 69 24 7 34.78% 10.14% 9.50% 11.10% 116.76% 1.59%
Raheem Sterling 84 33 7 39.29% 8.33% 8.96% 10.52% 117.51% 1.57%
Jonjo Shelvey 63 20 4 31.75% 6.35% 7.44% 8.92% 119.87% 1.48%
Charlie Austin 130 53 18 40.77% 13.85% 12.41% 13.86% 111.67% 1.45%
Gylfi Sigurdsson 67 24 7 35.82% 10.45% 7.50% 8.91% 118.77% 1.41%
Kevin Mirallas 52 16 7 30.77% 13.46% 7.63% 8.76% 114.92% 1.14%
Sergio Agüero 148 62 26 41.89% 17.57% 14.82% 15.75% 106.32% 0.94%
Saido Berahino 86 37 14 43.02% 16.28% 13.67% 14.59% 106.71% 0.92%
Alexis Sánchez 121 49 16 40.50% 13.22% 9.97% 10.85% 108.90% 0.89%
Charlie Adam 62 17 7 27.42% 11.29% 7.53% 8.34% 110.69% 0.80%
Sadio Mané 60 25 10 41.67% 16.67% 11.93% 12.68% 106.30% 0.75%
Christian Eriksen 97 26 10 26.80% 10.31% 7.22% 7.94% 109.90% 0.71%
Christian Benteke 80 29 13 36.25% 16.25% 12.29% 12.96% 105.43% 0.67%
Leroy Fer 54 14 6 25.93% 11.11% 8.47% 9.02% 106.47% 0.55%
Stewart Downing 70 19 6 27.14% 8.57% 6.73% 7.14% 106.10% 0.41%
Riyad Mahrez 63 24 4 38.10% 6.35% 7.65% 7.97% 104.15% 0.32%
Gnegneri Yaya Touré 89 27 10 30.34% 11.24% 8.66% 8.98% 103.61% 0.31%
Romelu Lukaku 106 43 11 40.57% 10.38% 11.56% 11.65% 100.81% 0.09%
Nikica Jelavic 57 15 8 26.32% 14.04% 10.53% 10.55% 100.23% 0.02%
Diafra Sakho 66 22 10 33.33% 15.15% 13.53% 13.52% 99.97% -0.00%
Craig Gardner 56 18 3 32.14% 5.36% 7.57% 7.49% 99.01% -0.07%
Wilfried Bony 89 35 11 39.33% 12.36% 11.33% 11.15% 98.43% -0.18%
Danny Ings 97 33 11 34.02% 11.34% 11.41% 10.65% 93.35% -0.76%
Jordan Henderson 50 14 6 28.00% 12.00% 9.95% 9.17% 92.16% -0.78%
Dusan Tadic 53 21 4 39.62% 7.55% 11.21% 10.32% 92.10% -0.89%
Danny Welbeck 58 23 4 39.66% 6.90% 12.18% 11.24% 92.33% -0.93%
Philippe Coutinho 103 34 5 33.01% 4.85% 6.13% 5.11% 83.40% -1.02%
Willian Borges Da Silva 55 17 2 30.91% 3.64% 6.90% 5.70% 82.65% -1.20%
Jason Puncheon 65 20 6 30.77% 9.23% 6.28% 5.06% 80.63% -1.22%
Oscar dos Santos Emboaba Junior 72 23 6 31.94% 8.33% 8.43% 7.17% 85.05% -1.26%
Santiago Cazorla 93 33 7 35.48% 7.53% 12.00% 10.44% 87.04% -1.55%
Ángel Di María 61 18 3 29.51% 4.92% 6.06% 4.38% 72.26% -1.68%
Yannick Bolasie 69 19 4 27.54% 5.80% 7.49% 5.80% 77.43% -1.69%
Abel Hernández 52 19 4 36.54% 7.69% 11.66% 9.95% 85.33% -1.71%
Gabriel Agbonlahor 53 17 6 32.08% 11.32% 11.33% 9.34% 82.39% -2.00%
Ross Barkley 51 14 2 27.45% 3.92% 7.31% 5.27% 72.01% -2.05%
Connor Wickham 83 24 5 28.92% 6.02% 9.04% 6.88% 76.11% -2.16%
Enner Valencia 72 21 4 29.17% 5.56% 10.62% 8.05% 75.77% -2.57%
Ashley Barnes 66 21 5 31.82% 7.58% 11.15% 8.33% 74.71% -2.82%
Graziano Pellè 123 38 12 30.89% 9.76% 14.29% 10.80% 75.61% -3.49%
Mario Balotelli 56 20 1 35.71% 1.79% 10.08% 6.51% 64.60% -3.57%
Peter Crouch 59 17 8 28.81% 13.56% 13.07% 8.63% 65.99% -4.45%

Which in turn looks like this:

cq-vs-sd-2014

Steven Gerrard’s numbers here are padded a bit by penalties, but he took good penalties, so you can see the boost he gets. Costa was a monster, Nacer Chadli was incredibly sharp (though seems to have crashed hard this season, basically halving the xG on every shot). Ross Barkley’s chances were just as bad, but unlike this year, they didn’t go in. Jason Puncheon just needs to stop.

So this is fun, but is it really any more interesting than conversion rate et al? Let’s look at how predictive each season is of the next. I’ll limit it to full seasons, players with 50+ shots. Here’s how various metrics perform:

Metric R2
2011-2012 2012-2013 2013-2014
SoTR 0.1967 0.041 0.0215
Conversion 0.4299 0.1271 0.2224
Scoring % 0.1844 0.0228 0.0584
SD/SQ 0.2122 0.2436 0.1161
SD-SQ 0.3498 0.2729 0.0929

While it’s clear we haven’t found the holy gail of a strongly repeatable shooting metric, I still like our composite model. It has the benefit that as my chance quality and save difficulty models get better, these numbers may also improve, and I’ll be sure to look into that.

At the very least, I think the idea of having small, granular models, and looking at the gaps between them is an interesting way to find some new metrics and insights, and I’ll see what else I can find with a similar approach.

In the Gaps Between Models

Goals Conceded Likelihood

Having generated some expected save numbers with my new model, I thought it’d be interesting to see who has been dodging their luck so far this season. So given each team’s shots against and average xS, it’s easy to simulate the likelihood that each team’s goals conceded should be where it is or better:

Team Shots Condeded xS Likelihood
Tottenham Hotspur 32 5 77% 22%
Arsenal 41 8 74% 24%
Crystal Palace 42 8 77% 34%
Manchester City 24 8 75% 87%
Manchester United 33 9 68% 36%
Swansea City 36 9 75% 57%
Liverpool 30 10 70% 72%
Watford 38 10 68% 30%
Stoke City 47 10 70% 12%
Everton 43 11 72% 44%
West Bromwich Albion 42 11 75% 66%
Southampton 27 13 64% 93%
West Ham United 47 14 66% 34%
Aston Villa 46 15 76% 92%
Leicester City 42 17 65% 83%
Bournemouth 35 17 59% 85%
Newcastle United 54 18 71% 81%
Sunderland 52 19 69% 84%
Chelsea 53 20 66% 77%
Norwich City 45 20 60% 79%

So of the teams with the tightest defences, only Man City can really be trusted so far. Further down, Everton are outperforming by a goal or so, and the West Ham number sticks out, especially given that they’re this season’s most popular football analytics whipping-boy. They’re actually only a couple of shots better off than they should be, but because they’re facing tougher shots, the distribution is wider:

west-ham-conceded

Near the other end of the spectrum, my model’s pretty confident that things aren’t going to get worse at Aston Villa – they’re currently four goals down from where they likely should be:

aston-villa-conceded

Goals Conceded Likelihood

Expected Saves

As part of a longer-term attempt to deconstruct expected goals into a variety of different, more granular and perhaps slightly more descriptive models, I’ve knocked together an expected save model today, and I thought I’d highlight some of the more interesting results out of it. Below is the data for this year’s Premier League, containing:

  • Total shots on target
  • Shots on target saved
  • Goals
  • Expected saves – the model’s prediction of how many SoT should have been kept out
  • Saves above expected – how a keeper’s actual numbers compare to their expected numbers
  • Difficulty – the average difficulty of shot the keeper faced (this is calculated as sum(1 - xs) / count(shots))
  • Rating – simply saves over expected saves to make it easier to compare keepers

I’ve ordered by saves above expected because it’s a more in your face than the rating.

Season Keeper Shots Saves Goals Expected Saves Saves Above Expected Average Difficulty Rating
2015 Jack Butland 47 37 10 32.76 4.24 30.30% 112.94%
2015 Alex McCarthy 34 29 5 26.28 2.72 22.70% 110.34%
2015 Petr Cech 41 33 8 30.49 2.51 25.63% 108.23%
2015 Hugo Lloris 31 26 5 23.80 2.20 23.24% 109.26%
2015 Heurelho Gomes 38 28 10 25.94 2.06 31.74% 107.94%
2015 Adrián San Miguel del Castillo 30 22 8 20.65 1.35 31.17% 106.53%
2015 Tim Howard 43 32 11 30.97 1.03 27.98% 103.32%
2015 David de Gea 23 17 6 16.05 0.95 30.22% 105.92%
2015 Darren Randolph 12 8 4 7.43 0.57 38.08% 107.67%
2015 Sergio Romero 10 7 3 6.47 0.53 35.33% 108.24%
2015 Thibaut Courtois 21 14 7 13.77 0.23 34.45% 101.71%
2015 Michel Vorm 1 1 0 0.86 0.14 14.06% 116.36%
2015 Kelvin Davis 7 5 2 4.87 0.13 30.43% 102.67%
2015 Lukasz Fabianski 36 27 9 26.91 0.09 25.26% 100.35%
2015 Carl Jenkinson 5 3 2 3.01 -0.01 39.79% 99.65%
2015 Joe Hart 16 12 4 12.18 -0.18 23.86% 98.50%
2015 Adam Federici 11 6 5 6.53 -0.53 40.67% 91.93%
2015 Boaz Myhill 42 31 11 31.61 -0.61 24.73% 98.06%
2015 Robert Elliot 5 3 2 3.81 -0.81 23.82% 78.76%
2015 Simon Mignolet 30 20 10 20.95 -0.95 30.17% 95.47%
2015 Wayne Hennessey 8 5 3 6.04 -1.04 24.48% 82.76%
2015 Tim Krul 49 33 16 34.61 -1.61 29.36% 95.34%
2015 Willy Caballero 8 4 4 5.74 -1.74 28.25% 69.69%
2015 Artur Boruc 24 12 12 14.03 -2.03 41.52% 85.50%
2015 John Ruddy 45 25 20 27.10 -2.10 39.79% 92.26%
2015 Asmir Begovic 32 19 13 21.21 -2.21 33.71% 89.57%
2015 Kasper Schmeichel 42 25 17 27.49 -2.49 34.55% 90.94%
2015 Costel Pantilimon 52 33 19 35.76 -2.76 31.22% 92.27%
2015 Maarten Stekelenburg 20 9 11 12.41 -3.41 37.95% 72.53%
2015 Brad Guzan 46 31 15 34.74 -3.74 24.48% 89.24%

Some brief observations:

  • It’ll be interesting to see who goes to Euro 2016 for England, Jack Butland and Alex McCarthy are both making a good case early in the season.
  • That said, Alex McCarthy has faced the easiest shots on average of any keeper in the league (save Michel Vorm, who has had only one save to make).
  • Hugo Lloris is performing above xS, but not so much that Tottenham’s 5 goals conceded is overly flattering. Lloris is another that’s right down there in the difficulty stakes, and it’ll be interesting to analyse over the coming weeks whether this is tame shot-making, or defensive organisation.
  • The Brad Guzan vs Marrten Stekelenburg comparison at the bottom is fascinating – imagine if Southampton allowed as many shots as Aston Villa.

It’s early in the season, and saves are easier to make than goals (I’m not saying goalkeepers are the bassists of football, just that they save more than they let in, and strikers miss more than they score), so as you’d expect, the model matches reality fairly well so far. We can see this if plot expected saves versus saves – above the line is good, below is bad, further to the top right are the leakiest defences, bottom left are mostly backup, although Darren Randolph and Sergio Romero seem to have done fine when called upon this year.

expected-saves

I’ll be keeping this updated through the season and I’ll surface anything interesting I find in the historical data or across Europe. In the meantime, please enjoy the consistently inconsistent Tim Howard:

Season Shots Saves Goals xS xSdiff Difficulty Rating
2010 141 97 44 99.48 -2.48 29.45% 97.51%
2011 133 94 39 93.39 0.61 29.78% 100.65%
2012 128 89 39 85.00 4.00 33.59% 104.71%
2013 152 115 37 109.83 5.17 27.74% 104.70%
2014 109 65 44 72.81 -7.81 33.20% 89.28%
2015 43 32 11 30.97 1.03 27.98% 103.32%
Expected Saves

The 100% Club

Newcastle’s Georginio Wijnaldum-inspired thrashing of Norwich at the weekend surfaced this tidbit:

6 – Newcastle United scored with all six of their shots on target. Unremitting.

— OptaJoe (@OptaJoe) October 18, 2015

Who are the other members of this 100% on-target conversion club (big-5 leagues, 2012 onwards)?

Date Home Away Shots On Target Penalty Goals Goals
2013‑09‑01 VfB Stuttgart 1899 Hoffenheim 13 6 0 6
2014‑10‑18 FC Bayern München SV Werder Bremen 10 6 1 6
2015‑10‑18 Newcastle United Norwich City 11 6 0 6
2013‑03‑30 Fortuna Düsseldorf Bayer 04 Leverkusen 15 5 1 5
2013‑04‑20 Hannover 96 FC Bayern München 14 5 0 5
2013‑08‑25 Atlético de Madrid Rayo Vallecano 14 5 0 5
2014‑09‑21 Leicester City Manchester United 15 5 2 5

And a whole bunch on 4 or fewer. Something of a Bundesliga speciality it appears, with Newcastle part of an exclusive club to do it without penalties. I had a really good Grexit/currency conversion/conversion rate joke, so I nearly included Costa Rica vs Greece from the last World Cup, as Costa Rica converted one normal time goal and all their penalties to join the 6/6 club, but that felt like cheating.

The 100% Club

Tim Sherwood: “85% of the teams who concede first lose”

Miguel Delaney picked up on a nugget of Tim Sherwood gold after Aston Villa’s defeat to Chelsea today:

Sherwood: “First goal is the most important, 85% of the teams who concede first lose.”

— Miguel Delaney (@MiguelDelaney) October 17, 2015

I thought I’d run the numbers for various leagues I have available:

First to Concede Games Losses Draws Wins
Home 2731 1704 (62.4%) 595 432
Away 3696 2738 (74.1%) 645 313
Total 6427 4442 (69.1%) 1240 745

In the Premier League alone since 2011, away teams have trailed first 946 times and gone on to lose 682 times, 72.1% of time. So he’s not enormously far off, but far enough that it’s clear he’s making it up.

Tim Sherwood, 85% of the time, he’s right every time.

Tim Sherwood: “85% of the teams who concede first lose”