Player

Evaluating Defenders With PATCH

Thom Lawrence, , , 1 Comment

Today we’ll look at players in Europe through the lens of my PATCH defensive metric. If you can’t be bothered trawling through an entire post to understand the method, you only really need to know this: PATCH measures how much a defender prevents their opponents from advancing the ball through their territory. Clearly that leaves lots of information about defenders and defences in general on the table, but you’ll have enough information by the end of this post to bash me over the head with specific examples of players you think it’s misjudging. In fact, I’ll even help you out along the way and spell out all my worries about the metric, and the things I think it ought to do in the future.

That said, in PATCH’s defence, it has some nice characteristics:

  • A team’s medianish PATCH score correlates pretty well with shot numbers conceded by a team over a season, at around 0.7.
  • It correlates slightly better with xG conceded by teams, at around 0.75.
  • It persists year-on-year for teams, correlated at around 0.6.
  • A player’s median PATCH persists year-on-year at around 0.3.

But putting numbers aside, let’s see how you feel about some individual player values. If you look at the standard deviation of median PATCH values by minutes played, you can see things settle down at around 600 minutes:

patch-stddev

That’s because at very low values you get some weird outlying games, where players haven’t had any opponents in their territory and so score very highly. Just to be safe, we’ll set a cutoff a little higher, so here are the top European centre-backs with more than 900 minutes this season:

Competition Team Player PATCH
Italian Serie A Fiorentina Gonzalo Rodríguez 6.27
French Ligue 1 Lyon Samuel Umtiti 5.79
Spanish La Liga Real Madrid Pepe 5.52
Spanish La Liga Barcelona Gerard Piqué 5.24
German Bundesliga FC Bayern München Jerome Boateng 5.20
Spanish La Liga Barcelona Javier Mascherano 5.20
Spanish La Liga Málaga Raúl Albentosa 5.19
Italian Serie A Roma Kostas Manolas 5.06
Italian Serie A Lazio Wesley Hoedt 4.99
German Bundesliga Borussia Dortmund Sokratis 4.94
Italian Serie A Fiorentina Davide Astori 4.91
French Ligue 1 Paris Saint-Germain Thiago Silva 4.86
English Premier League Liverpool Martin Skrtel 4.72
English Premier League Liverpool Mamadou Sakho 4.72
Italian Serie A Internazionale Jeison Murillo 4.71
Italian Serie A Milan Alex 4.66
Italian Serie A Juventus Andrea Barzagli 4.63
French Ligue 1 St Etienne Loic Perrin 4.59
French Ligue 1 GFC Ajaccio Roderic Filippi 4.57
Italian Serie A Juventus Leonardo Bonucci 4.49

There are some clumps of teams here, so we should immediately be suspicious that we’re measuring team effects as much as player effects – PATCH currently doesn’t adjust for teams average scores, and for that matter nor does it score leagues differently. But these numbers are mostly defensible. It’s fun to note that Raúl Albentosa was a Derby County signing during Steve McClaren’s reign, and he’s recently been targeting Samuel Umtiti at Newcastle, so it’s nice to know I’ve built the perfect metric for him, even if you don’t buy it.

The same metric works for defensive and centre midfielders too:

Competition Team Player PATCH
Spanish La Liga Barcelona Sergio Busquets 5.66
French Ligue 1 Lyon Maxime Gonalons 5.50
Spanish La Liga Barcelona Ivan Rakitic 5.21
Italian Serie A Fiorentina Milan Badelj 5.19
Italian Serie A Roma Daniele De Rossi 4.84
French Ligue 1 Lyon Corentin Tolisso 4.81
Italian Serie A Fiorentina Borja Valero 4.60
Italian Serie A Fiorentina Matias Vecino 4.48
Spanish La Liga Sevilla Grzegorz Krychowiak 4.45
Italian Serie A Lazio Lucas Biglia 4.31
Italian Serie A Internazionale Felipe Melo 4.30
Spanish La Liga Las Palmas Vicente Gómez 4.28
English Premier League Liverpool Emre Can 4.26
Spanish La Liga Sevilla Steven N’Zonzi 4.26
German Bundesliga Borussia Dortmund Ilkay Gündogan 4.26
Italian Serie A Roma Radja Nainggolan 4.25
German Bundesliga FC Bayern München Xabi Alonso 4.24
German Bundesliga FC Bayern München Arturo Vidal 4.11
Spanish La Liga Rayo Vallecano Raúl Baena 4.10
English Premier League Liverpool Jordan Henderson 4.09

Busquets on top, all is right in the world.

Prospects

We can already see some talented youngsters in the tables above, so let’s focus purely on players that were 23 or under at the start of this season. I’ve relaxed the minutes to 600, here’s the top 30, taken from all midfielders and defenders:

Competition Team Player Date of Birth Minutes PATCH
French Ligue 1 Lyon Samuel Umtiti 14/11/1993 1852 5.79
Italian Serie A Lazio Wesley Hoedt 06/03/1994 1507 4.99
French Ligue 1 Lyon Corentin Tolisso 03/08/1994 2188 4.63
German Bundesliga FC Bayern München Joshua Kimmich 08/02/1995 727 4.54
English Premier League Liverpool Emre Can 12/01/1994 2054 4.44
German Bundesliga VfB Stuttgart Timo Baumgartl 04/03/1996 1287 4.30
German Bundesliga Bayer 04 Leverkusen Jonathan Tah 11/02/1996 2076 4.27
French Ligue 1 Lyon Sergi Darder 22/12/1993 696 4.15
German Bundesliga VfL Wolfsburg Maximilian Arnold 27/05/1994 877 4.10
German Bundesliga FC Bayern München Kingsley Coman 13/06/1996 895 3.98
Spanish La Liga Rayo Vallecano Diego Llorente 16/08/1993 2228 3.97
French Ligue 1 Paris Saint-Germain Marquinhos 14/05/1994 988 3.95
Italian Serie A Empoli Federico Barba 01/09/1993 927 3.93
English Premier League Tottenham Hotspur Dele Alli 11/04/1996 730 3.93
English Premier League Tottenham Hotspur Eric Dier 15/01/1994 2375 3.79
English Premier League Arsenal Héctor Bellerín 19/03/1995 2346 3.79
Spanish La Liga Real Sociedad Aritz Elustondo 11/01/1994 1643 3.68
Spanish La Liga Atlético de Madrid Saúl Ñíguez 21/11/1994 1337 3.66
Italian Serie A Lazio Sergej Milinkovic-Savic 27/02/1995 715 3.65
French Ligue 1 Monaco Wallace 14/10/1994 1654 3.63
German Bundesliga Borussia Dortmund Matthias Ginter 19/01/1994 1414 3.62
Italian Serie A Milan Alessio Romagnoli 12/01/1995 2102 3.62
German Bundesliga Borussia Dortmund Julian Weigl 08/09/1995 1575 3.59
Italian Serie A Lazio Danilo Cataldi 06/08/1994 1024 3.55
Italian Serie A Napoli Elseid Hysaj 02/02/1994 2279 3.52
Spanish La Liga Sevilla Sebastián Cristóforo 23/08/1993 614 3.51
English Premier League Chelsea Kurt Zouma 27/10/1994 1949 3.51
French Ligue 1 Nice Olivier Boscagli 18/11/1997 792 3.48
Spanish La Liga Getafe Emiliano Velázquez 30/04/1994 745 3.44
Italian Serie A Sampdoria David Ivan 26/02/1995 785 3.41

Note: the tables above filter to performances at centre-back or in midfield, if the values differ in this last table it’s because it considers their performances in a wider variety of positions.

So, cheer up Timo Baumgartl, PATCH doesn’t count mistakes. You’ll note again big clumps of players from the same teams (kudos, Lyon) – we know by now we’re probably measuring some systematic effects here. It’s also worth pointing out that if a young player is getting this level of minutes, in the big 5 leagues, they’re probably at a certain level without even looking at their numbers. But again, at first glance, this is a decent list.

Liverpool’s First Choice Centre-Backs

At the Opta Pro Forum I blurted out to the Liverpool contingent that my pet defensive metric quite liked their defending, to which they replied “ours doesn’t.” So I was a little crestfallen, but I’ll continue to talk myself out of a job here: Liverpool concede the second fewest shots in the league, so I’m right. They also have the worst save percentage in the league but nevertheless renewed Mignolet’s contract, so they’re wrong. QED. Let’s look more closely:

liverpool-cbs

Here you’ve got all Liverpool’s games this season – Rodgers up to the fateful Merseryside derby on the 4th October, Klopp soon after. The markers show individual PATCH performances, and the lines are five-game moving averages (although Touré isn’t quite there yet). The average PATCH for EPL centre-backs is around 3.3, and you’ll note that Liverpool are regularly exceeding that. You can also see that Skrtel had some insane outliers, but maintained pretty good form for Klopp until his injury (which – if you’re to believe renowned fitness troll Raymond Verheijen – was inevitable). The fight for second place is closer, but even taking Lovren and Sakho side-by-side, I don’t believe you’re left with a terrible pairing.

So, I don’t believe Liverpool’s defence is terrible and I think they have a solid foundation both in defence and midfield for Klopp to build on over the next season. I do believe they’ve been unlucky, as James Yorke points out in this article on Statsbomb. It’s funny to compare Everton and Liverpool’s defences this season – they both sit on 36 goals conceded in the league. Tim Howard has taken a lot of heat this year for his performances, all while facing 4.7 shots on target a game – the fourth worst in the league. Mignolet’s been facing 3.3 – the second least. While Howard has now been unceremoniously dropped and is soon to be shipped off to the MLS, Mignolet gets his contract renewed. Sure, some of of this is luck and not entirely his fault, but I genuinely believe you should not lay the blame on Liverpool’s defence, there’s not a lot more they could do to make Mignolet’s life quieter.

Arsenal’s Midfield

Through injuries, sentimentality or pure stubbornness, it’s hard to tell if Wenger knows his best midfield this season. I asked on Twitter and a lively debate ensued, but excluding the lamentation of Wilshere believers, the most common answer was Coquelin & Cazorla, with some pondering ways to insert Ramsey into the mix. What does PATCH think, purely defensively, of their appearances in the centre of the field?

arsenal-cms

Okay, well, first thoughts are that this is a graph format looking to get retired early, but here you have the four players who have put reasonable minutes into Arsenal’s central midfield, with the markers again showing their PATCH values in each game week, and the lines again showing a five game moving average. The average PATCH for a midfielder in the EPL is basically the same as a defender, around 3.3. This graph seems to imply that Cazorla has very good and very average games, and similar could be said for Flamini. Ramsey doesn’t seem like anything special, but is pretty low-variance. Coquelin seemed to start the season very strongly, but was fairly average in the lead-up to his injury.

Let’s break it down more simply, here are the averages:

Player PATCH
Santiago Cazorla 4.17
Francis Coquelin 3.83
Mathieu Flamini 3.79
Aaron Ramsey 3.60

So in black and white, we seem to more or less agree with Arsenal fans’ instincts.

N’Golo Kanté

What of players whose defending we rave about but who don’t make an appearance high up the PATCH ratings? N’Golo Kanté is way down the list, with a very middling  3.12. What’s happened there? Well, let me reiterate that PATCH measures territory and ball progression, nothing else. As I mentioned on the Analytics FC podcast recently, not all ball progression is bad. Much has been made of Leicester’s “bend, don’t break” defensive scheme this season – they give up territory but their low block often makes up for it, this means their midfield isn’t likely to score highly for repelling the opponent. Kanté himself regularly relies on pace and last ditch tackles (and he is an excellent tackler) to retrieve the ball once it’s in his territory, but if a pass has been completed in that territory, PATCH has already given him a demerit.

So… PATCH is useless because it misses demonstrably good players? Well, I’m not sure I’d go as far as calling Leicester’s defence bad, but it’s certainly well below par for a league leader, as Tim at 7amkickoff recently analysed. That said, I’ll admit I’m a little uncomfortable. I’ve said elsewhere, the essence of PATCH, or really any defensive metric, is this:

  1. Whose fault is it?
  2. How bad is it?

In PATCH, the whose fault part is calculated by territory (and there are lots of ways to do this better) and the how bad bit is done through ball progression. Alternatives to the second might pick Kanté up better – how many moves enter his territory, but never leave? That would be an interesting one to look at, and something I’ll explore soon.

For now, let’s just say that he’s valuable for Leicester inasmuch as his defensive skills turn into attacks very effectively, because it’s Leicester’s attack (and let’s face it, their luck) that is powering their title challenge, and not necessarily their defence. And that, dear reader, is another thing that PATCH doesn’t measure in defenders.

Conclusion

Hopefully if you’ve got this far, you believe there’s value in something like PATCH as a way of measuring defenders. It’s certainly entangled with teams’ systematic effects, and we suspect it has some false negatives. I don’t think looking at these outputs that there are tons of false positives however, but then Flamini rears his head so who knows.

I’m constantly working on PATCH, so I’d love to hear your ideas for places it might fall down, or things you’d like to see it applied to. To that end, I’ve bunged PATCH values for all EPL performances this season on Github. This file contains:

  1. Team
  2. Opposition
  3. Match date
  4. Player
  5. Date of Birth
  6. Nationality
  7. Starting lineup X position
  8. Starting lineup Y position
  9. Minutes played
  10. PATCH

Play, critique, ignore, do what you will. I’ll see if I can get to the point where these are updated for a all players in a bunch of leagues every week, but right now I can’t guarantee the scoring is going to be at all stable with all the changes I’m making.

Evaluating Defenders With PATCH

Mid-Season Goalkeeper Review

Thom Lawrence, , , 6 Comments

Having descended into the quagmire of defensive metrics and never really returned, I thought it was about time to break my 2016 duck and publish something. Given that I occasionally spot people arguing in obscure forums pointing at the last iteration, I thought it was time to update my keeper ratings:

Keeper Mins Shots Saves Goals Save % Expected Saves ± Expected Average Difficulty Rating
Mark Bunn 188 6 5 1 83% 3.87 1.13 35.56 129.32
Fraser Forster 188 1 1 0 100% 0.86 0.14 14.45 116.89
Michel Vorm 94 1 1 0 100% 0.86 0.14 14.06 116.36
Karl Darlow 94 4 3 1 75% 2.62 0.38 34.53 114.56
Paulo Gazzaniga 187 11 8 3 73% 7.07 0.93 35.72 113.14
Alex McCarthy 565 34 29 5 85% 26.28 2.72 22.70 110.34
Sergio Romero 375 9 7 2 78% 6.47 0.53 28.14 108.24
Darren Randolph 286 12 8 4 67% 7.43 0.57 38.08 107.67
Adrián 1790 89 69 20 78% 64.11 4.89 27.96 107.62
Joe Hart 1871 60 46 14 77% 43.38 2.62 27.71 106.05
Hugo Lloris 1963 67 51 16 76% 48.10 2.90 28.20 106.02
Jack Butland 1972 100 78 22 78% 73.66 4.34 26.34 105.89
Declan Rudd 751 40 28 12 70% 26.51 1.49 33.73 105.63
Petr Cech 1967 86 68 18 79% 66.03 1.97 23.22 102.99
Kelvin Davis 95 7 5 2 71% 4.87 0.13 30.43 102.67
David de Gea 1591 64 46 18 72% 44.98 1.02 29.72 102.27
Heurelho Gomes 1948 83 60 23 72% 59.92 0.08 27.80 100.13
Thibaut Courtois 997 52 36 16 69% 35.99 0.01 30.79 100.03
Costel Pantilimon 1599 103 71 32 69% 71.01 -0.01 31.06 99.99
Kasper Schmeichel 2079 86 60 26 70% 60.21 -0.21 29.99 99.66
Artur Boruc 1604 62 38 24 61% 38.25 -0.25 38.31 99.35
Tim Howard 2069 107 75 32 70% 77.08 -2.08 27.96 97.30
John Ruddy 1316 63 38 25 60% 39.45 -1.45 37.37 96.31
Wayne Hennessey 1498 50 33 17 66% 34.45 -1.45 31.11 95.80
Tim Krul 754 49 33 16 67% 34.61 -1.61 29.36 95.34
Lukasz Fabianski 1979 85 56 29 66% 59.45 -3.45 30.06 94.19
Vito Mannone 376 23 15 8 65% 15.94 -0.94 30.69 94.09
Boaz Myhill 2077 92 63 29 68% 66.98 -3.98 27.19 94.05
Simon Mignolet 1889 65 42 23 65% 44.66 -2.66 31.29 94.04
Robert Elliot 1224 63 42 21 67% 44.82 -2.82 28.86 93.72
Willy Caballero 187 17 12 5 71% 12.83 -0.83 24.55 93.55
Asmir Begovic 1079 50 33 17 66% 35.75 -2.75 28.50 92.31
Brad Guzan 1897 99 64 35 65% 71.53 -7.53 27.75 89.48
Maarten Stekelenburg 1599 49 30 19 61% 34.38 -4.38 29.83 87.26
Jordan Pickford 93 11 7 4 64% 8.10 -1.10 26.40 86.47
Adam Federici 422 24 11 13 46% 13.16 -2.16 45.15 83.56
Adam Bogdan 93 5 2 3 40% 2.90 -0.90 42.03 69.00

So many narratives, so little time:

  • If only they’d dropped Guzan sooner – Bunn in his tiny sample has risen to the top of the class. Similarly, Southampton have finally got Forster back again and they too aren’t looking back.
  • Tim Howard isn’t that bad, get over it.
  • Petr Cech isn’t single-handedly winning Arsenal the title, get over it.
  • Jordan Pickford didn’t have the best of times deputising for Costel Pantilimon, the mathematical definition of the average goalkeeper.
  • Artur Boruc has slowly clawed his way back, and Bournemouth are no longer conceding every time their opponents so much as look at the ball.
  • Someone needs to rescue Alex McCarthy, he should have been going to the Euros this Summer.
  • Adrian is a pretty solid number 1 given the minutes under his belt.

Anyway, apologies for the wait. Lots of stuff I can’t talk about is going on behind the scenes, but there will be some cool stuff up here soon enough. Well, hopefully.

Mid-Season Goalkeeper Review

Defence, Territory and Control

Thom Lawrence, , , 5 Comments

There comes a time in your adolescence as a stats writer when your parents rudely awaken you in the middle of the night, bundle you into a car, and drop you in the middle of a dark forest with nothing but a sharp stick. “It’s time you made your own defensive metric,” they tell you before receding into the darkness. It is a rite of passage every statto must endure alone.

Frightened and cold, you look for shelter. xG, you think. I know xG, good old xG, I can use that for something! But others have been here before, the forest has been hunted barren. What about those bright green diagrams everyone claims to like, could I just use those? Carving numbers into trees with your trusty pointy stick, you get to work.

As anyone who follows this blog will know, I’m quite interested in space and how teams use it. Today I’m going to look at the territory claimed by defenders and I’m going to propose a metric based on the actions they allow within that territory. It’s just one metric, it’s not the be-all and end-all of measuring defenders or defences, but I think it’s vaguely interesting, and its opinion on a lot of defenders is defensible. It has some flaws that’ll probably be obvious to you, but I’ll mention them as we go.

What does a defender’s territory look like? Here’s how Arsenal looked against Tottenham in the North-London derby:

defensive-areas-1448157062630

Here’s Aston Villa’s clean sheet against Manchester City:

defensive-areas-1448157097475.png

And here’s Newcastle from their 1-0 smash and grab against Bournemouth:

defensive-areas-1448157225615

These are similar to my shot buildup charts, but for defensive actions. For each defender, we take all their own-half defensive actions (tackles, blocks, interceptions, clearances, aerial challenges and indeed fouls), and draw a line around them. This is their territory, it’s the part of the pitch they seem to want to be responsible for.

Now you could argue we could just split the final third of the pitch into four and assign each slice to a defender, but I think the overlaps are very valuable here – you want to know if a defender drifts inside or out, how far they push forwards etc. Obviously drawing the lines like this leaves gaps – nobody’s taking responsibility right at the edges or corners of the pitch. There’s also the problem of a player that makes a single tackle on the other side of the pitch, stretching their territory, perhaps unfairly. We could add a bit of a buffer zone to these areas, and trim some outliers, but for now I’m happy with them as they are – they are the best way we have of outlining a defender’s territory, based entirely on where the defender tries to defend.

Glancing at the charts above, you’ll notice some players have more territory than others. We want a metric that rewards this, if possible – if a player is bossing the entire danger zone, that’s great, even if you might prefer to see their team-mates step in. So our metric’s first ingredient is the surface area of a defender’s territory.

But the positioning on its own is meaningless, we want to know how much control they exert in that space. For this, we count the number of touches opponents take in the defender’s territory. These aren’t touches as you see them on TV, these are just all the aggregate events we see in the data – passes, dribbles, shots, all the stuff opponents want to do in our half. We have to be careful here to count points inside the territory, and only those that overlap the defender’s time on the pitch.

We combine these by dividing the area by the number of touches, then we weight things by possession and per-ninetify everything. What you should picture is the defender scent-marking their territory (I find it easiest to picture John Terry doing this, for some reason), and then every opposition action diluting that scent more and more. Larger territory will necessarily be exposed to more opposition actions, but good defenders will prevent and repel as much of this as possible. Players that make fewer defensive actions will have tiny territories, but can still score highly by keeping opponents out.

What’s nice about this particular metric is that it doesn’t force you to work out whether tackles or interceptions or aerials or whatever are more important, and it doesn’t require you to look at shots and xG (or expected assists etc). It captures defensive pressure in open play, which is where most defending happens.

So, recapping the algorithm:

Area ÷ Opposition Touches ÷ Possession ÷ Minutes Played × 90

I like to refer to this as ‘Possession-adjusted Territorial Control Held’, or PaTCH. I am not good at acronyms, please suggest more. In the meantime, which defenders have a good PaTCH?

Player Team PaTCH
Gabriel Armando de Abreu Arsenal 596.2
John Terry Chelsea 302.0
Chris Smalling Manchester United 268.2
Cedric Ricardo Alves Soares Southampton 265.4
Nicolás Otamendi Manchester City 261.9
Matteo Darmian Manchester United 256.7
Sylvain Distin Bournemouth 255.1
Eliaquim Mangala Manchester City 250.1
Sebastian Prödl Watford 248.4
Virgil van Dijk Southampton 238.4
Mamadou Sakho Liverpool 237.2
Joleon Lescott Aston Villa 233.2
Allan-Roméo Nyom Watford 226.6
Fabricio Coloccini Newcastle United 226.4
Aleksandar Kolarov Manchester City 209.8
Neil Taylor Swansea City 208.4
Ashley Williams Swansea City 202.1
Laurent Koscielny Arsenal 195.3
Simon Francis Bournemouth 194.4
César Azpilicueta Chelsea 189.2
Luke Shaw Manchester United 187.7
Kurt Zouma Chelsea 185.4
Toby Alderweireld Tottenham Hotspur 185.0
Ryan Bertrand Southampton 182.2
Steven Whittaker Norwich City 176.8
Gareth McAuley West Bromwich Albion 175.1
Micah Richards Aston Villa 174.3
Jose Fonte Southampton 171.8
Russell Martin Norwich City 171.0
Glen Johnson Stoke City 170.9
Jeffrey Schlupp Leicester City 167.3
Daley Blind Manchester United 163.9
Bacary Sagna Manchester City 158.7
Phil Jagielka Everton 158.4
Nathaniel Clyne Liverpool 158.3
Ciaran Clark Aston Villa 155.5
Federico Fernandez Swansea City 150.3
Sebastien Bassong Norwich City 148.9
Vincent Kompany Manchester City 146.4
Per Mertesacker Arsenal 146.0
Martin Kelly Crystal Palace 145.1
Nacho Monreal Arsenal 142.1
Craig Cathcart Watford 139.9
Joel Ward Crystal Palace 138.1
Martin Skrtel Liverpool 138.0
Charlie Daniels Bournemouth 135.7
Ben Davies Tottenham Hotspur 135.2
Joseph Gomez Liverpool 134.9
Robbie Brady Norwich City 134.7
Winston Reid West Ham United 132.8
Jan Vertonghen Tottenham Hotspur 132.1
Alan Hutton Aston Villa 131.9
Jordan Amavi Aston Villa 128.6
Aaron Cresswell West Ham United 128.4
Maya Yoshida Southampton 127.4
Tommy Elphick Bournemouth 126.6
Héctor Bellerín Arsenal 119.5
Philipp Wollscheid Stoke City 119.4
Branislav Ivanovic Chelsea 117.8
Kyle Walker Tottenham Hotspur 116.9
Geoff Cameron Stoke City 116.8
Erik Pieters Stoke City 116.0
Carl Jenkinson West Ham United 115.7
Gary Cahill Chelsea 114.5
Marc Muniesa Stoke City 114.3
Kyle Naughton Swansea City 111.3
James Tomkins West Ham United 110.8
Scott Dann Crystal Palace 107.7
Steve Cook Bournemouth 107.0
John Stones Everton 106.8
Daryl Janmaat Newcastle United 104.4
Jonny Evans West Bromwich Albion 103.7
Chris Brunt West Bromwich Albion 103.1
Ritchie de Laet Leicester City 103.1
Danny Rose Tottenham Hotspur 100.0
Wes Morgan Leicester City 97.4
Robert Huth Leicester City 95.3
Brendan Galloway Everton 95.0
Dejan Lovren Liverpool 92.3
Billy Jones Sunderland 90.7
Nathan Aké Watford 90.2
Pape Souaré Crystal Palace 85.4
Seamus Coleman Everton 84.6
Massadio Haidara Newcastle United 83.2
John O’Shea Sunderland 82.7
Younes Kaboul Sunderland 80.5
Craig Dawson West Bromwich Albion 77.5
Chancel Mbemba Newcastle United 74.0
Damien Delaney Crystal Palace 70.1
Sebastián Coates Sunderland 69.1
Patrick van Aanholt Sunderland 68.4
Brede Hangeland Crystal Palace 64.6

These are filtered for defenders with 450+ mins (all data from before Saturday’s games), and I’m calculating the average PaTCH over those games. Note: as usual, ballsed up a bit, the graphics show territory marked out in the defender’s own half, the numbers are actually calculated for territory in the final third. But it’s cool, cos comparing the two sets of numbers will make for an interesting article in a bit.

Gabriel is such an outlier because of Arsenal’s 1-0 win over Arsenal, in which Mitrovic got sent off and Newcastle had one shot. Look at the territory:

defensive-areas-1448159902841

Now look at the heatmap from the BBC (Newcastle on the left):

85262060_arsenalnewcastleheatmaps

Newcastle had one touch in his territory, as far as I can tell, giving an astronomical match PaTCH (yup) in the three-thousands. Anyway, I will think of some better averaging or thresholding to reduce the impact of stuff like this, but still, he sort of earned it.

Elsewhere, you can see the model doesn’t like Sunderland or Crystal Palace much, but is a little bullish on Aston Villa’s defence. Of course this weekend Lescott was benched against Everton, and Villa decided to sit very deep and let Everton play, with horrific results. Everton themselves seem to have ridden their luck a few times – Galloway, Coleman and Stones bomb forward regularly and rely on Barry and McCarthy to pick up the slack in their territory, something I’d like to capture in the numbers at some point. Terry is still good at some stuff, Smalling’s number is consistent with the hype, Otamendi is predictably up there, and Koscielny is doing fine, though he’d probably benefit from that forever delayed defensive midfield signing for Arsenal. Lovren near the bottom, below every Liverpool and Southampton player.

For now, I’m reasonably happy with who shows up at the top and bottom. Over the next few days I’ll play with some historical data to tell some stories, incorporate this metric with a Christmas Shopping piece about defenders, then make some visualisations to see if there’s a good counterpart to the attacking buildup maps.

Defence, Territory and Control

Vardy’s Scoring Likelihood

Thom Lawrence, , , 1 Comment

Two articles this morning looked at Jamie Vardy’s excellent start to the season to see if it might be sustainable. Adam Bate at SkySports concludes:

Vardy’s start to the season may have been extraordinary but the evidence is overwhelming — it has not been a fluke. Don’t be too shocked if the goals continue to go in.

Mark Thompson is slightly cooler on Vardy, writing at EastBridge:

Is Vardy genuinely good then? Kinda, but not as good as his current total suggests. If I were a betting man, I might even look up the odds of him scoring fewer than 15 in the league this season.

Building on my chance quality and save difficulty models from yesterday, I would take the over on 15 goals, but I do agree Vardy is over-performing. To analyse this, I’ve taken each player in the Premier League with more than 20 non-penalty shots, and simulated their likelihood to score at least their current number of goals given:

  1. They total shot number and their average chance quality.
  2. The number of shots on target and their average save difficulty.

This should show if they’re doing better or worse than you would expect for the chances coming their way, and also whether they’re getting lucky or unlucky with their actual shots on goal. Looking at it this way, there is only one striker luckier than Vardy this year:

Player Shots On Target Goals SoTR Conv % Scoring % SD CQ Likelihood (SD) Likelihood (CQ) Expected (SD) Expected (CQ)
Graziano Pellè 38 11 5 29% 13% 45% 43% 13% 54% 53% 5 5
Sergio Agüero 33 14 6 42% 18% 43% 42% 12% 56% 22% 6 4
Olivier Giroud 23 12 4 52% 17% 33% 39% 14% 76% 43% 5 3
Alexis Sánchez 45 15 6 33% 13% 40% 37% 12% 49% 47% 5 5
Harry Kane 33 12 2 36% 6% 17% 34% 9% 95% 83% 4 3
Jamie Vardy 36 15 7 42% 19% 47% 35% 12% 24% 15% 5 4
Romelu Lukaku 28 12 5 43% 18% 42% 34% 13% 39% 27% 4 4
Diafra Sakho 29 10 3 34% 10% 30% 34% 13% 72% 76% 3 4
Sadio Mané 26 10 2 38% 8% 20% 30% 9% 86% 72% 3 2
Bafétimbi Gomis 22 10 3 45% 14% 30% 32% 11% 66% 46% 3 3
Ross Barkley 28 9 2 32% 7% 22% 25% 5% 69% 44% 2 1
Odion Ighalo 26 9 5 35% 19% 56% 28% 10% 7% 10% 2 2
Theo Walcott 26 12 2 46% 8% 17% 33% 15% 94% 91% 4 4
Rudy Gestede 22 7 3 32% 14% 43% 25% 11% 25% 44% 2 2
Memphis Depay 25 8 1 32% 4% 13% 22% 8% 87% 88% 2 2
Riyad Mahrez 23 9 3 39% 13% 33% 21% 8% 30% 27% 2 2
Aaron Ramsey 27 8 1 30% 4% 13% 23% 10% 88% 94% 2 3
Philippe Coutinho 39 11 1 28% 3% 9% 20% 7% 92% 95% 2 3
Yaya Touré 26 8 1 31% 4% 13% 21% 9% 85% 92% 2 2
Jonjo Shelvey 21 7 0 33% 0% 0% 9% 5% 100% 100% 1 1
Troy Deeney 23 4 0 17% 0% 0% 9% 8% 100% 100% 0 2

Vardy’s 24% chance of 7 goals from his shots shows he’s probably getting lucky (only Odion Ighalo at Watford is luckier, and by quite a margin). But his most likely haul is still somewhere between 4-5 goals, which isn’t bad at all. The question of whether he and more importantly whether Leicester can continue their scoring shenanigans remains to be seen – if they suddenly learn how to defend, are we still going to see these weird Vardy-inspired fightbacks?

Vardy’s Scoring Likelihood