Defence, Territory and Control

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 barrenWhat 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

5 thoughts on “Defence, Territory and Control

  1. I feel like this work has a lot of potential, but it has one flaw. It seems that when drawing the area around all actions made, actions made in unusual situations (like a tackle made after a cleared corner etc) will distort your shape. I would guess that a better picture of where a defender’s territory is could be defined more tightly, either by doing something like drawing the smallest possible regular shape around 90% of the defender’s actions, or just doing something simpler like excluding all actions of some distance away from all other actions.

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