Author: Ted Knutson

5 Easy Ways Data Can Give Football Clubs an Edge

Posted on by Ted Knutson

In something a little different today, I'm going to discuss five simple ways data can help football teams gain an advantage. There's this idea among football's old guard that data is complicated and difficult, but the reality is, we try and provide useful insight that is easy to understand and interpret.

1) Corner Touch Maps

This is what we call a corner touch map. Marek and I designed it back in 2014 to help out with the set piece program, and it's probably the dumbest, simplest vis we'll ever build.

 

 

What it shows is the first touch by either team after a corner is taken.

Why?

Because I can show you a shot map of where teams have had shots off corners, but that only tells you about when they have been successful. These maps more clearly show their plan and - generally - their intended delivery zones.

Check out the map from right-sided corners from Manchester City last season.

 

 

This immediately tells you two things. First, they take a lot of short corners, and you need to be ready for those. Second...

 

 

City apparently only took outswingers from that side last season, and as a result, neither team had a touch in the box on the left HALF of the six yard box.

And honestly, if I am an opposing coach facing City, my life is nearly impossible as it is, so I am thanking little baby jesus for making my life much easier by allowing me to generally ignore marking that zone (unless there are runners) and overload the zones along the curve. This is just a tiny glimpse of how we use data to help execute set pieces at both ends of the pitch.

2) Arsenal's Left Lane

 

 

This is what we call a Defensive Activity Map. Teams are attacking from left to right. The vis attempts to profile where teams are making defensive actions (including pressures), and then compares their defensive activity in each area to the rest of the teams in the league. Zones where they make more actions than average are hotter, and zones where they have fewer actions are greyer or blue.

Arsenal this season are slanted right, possibly because of personnel issues (left back injuries), but maybe as part of a plan? This type of vis doesn't deliver a magical recipe for how to solve/attack tactical issues, but it does help coaches and analysts ask interesting questions. As a coach, I go to the video and try to figure out what is weird. If I am an analyst, maybe I compare the success of attacks down Arsenal's left compared to the right/center and see if there is a vulnerability that way.

3) Similarity Scores

We use these a lot in recruitment, largely because it's easy to talk to coaches about who their ideal player for a position is as opposed to all of the precise things they need that player to do on the pitch.

Once you know which players fill their ideal archetypes, you can then dig into the data for what those players do on metrics you care about, and then plonk down a list of players to scout in the leagues you can afford.

Coach, who is your ideal wide forward?

"I want Lionel Messi."

(Seriously - this always happens. Every coach says this exact same joke.)

And because we are indulgent number wonks who have this already set up in StatsBomb IQ, we can answer the question honestly.

The most similar players to Messi 17-18 in our current data set are:

Neymar Messi (18-19 edition)

Eden Hazard

Raheem Sterling

and Nicolas Pepe, who has been on fire so far this year.

But the fun part of this is that you can actually narrow down the data to the leagues you can afford to buy players in and still have the exact same conversation.

Who is the Lionel Messi of League One? 2017-18 Bradley Dack, maybe? Or Conor Chaplin?

How about in Austria Bundesliga? Uh... Andrei Ivan?

Look, I'm not saying the data is always right in these situations, but shopping for the poor man's Messi apparently comes with serious limitations.

4) Evaluating Goalkeepers

On Monday, we released phase 1 of the Goalkeeper Module into StatsBomb IQ. It allows teams to profile goalkeepers statistically across a broad range of metrics that haven't really been available before because in other data sets, we never knew where the keeper was when a shot took place.

We were messing around with some of the visualisations in testing and came across this fun one for last year. David De Gea and Joe Hart faced almost exactly the same amount of xG in shots on target last season, but how that xG came about and what happened after that was dramatically different.

 

 

The vis above is broken into xG buckets, and you'll notice that the shots Hart had to content with were generally much higher quality than those De Gea dealt with. Sadly, nearly every high xG shot Hart faced also made it into the back of the goal.

When it comes to analysing and evaluating GKs with stats, we're just getting started. Expect to see a lot more from us on this topic in the coming weeks.

5) Passing Tendencies at the Team and Player Level

 

 

TL;DR

Stats don't have to be complicated to deliver powerful, useful insight. And often the simple stuff is the most effective IF you know where to find it.

Ted Knutson ted@statsbomb.com @mixedknuts

Radar Wars

Posted on by Ted Knutson

In August 2018, I was invited to be a keynote speaker at the CASSIS sports analytics conference in Vancouver. These are my slides from the keynote and I did a voiceover that is as close to what I said at the time as I can remember.

The first time I did a version of this talk was at Verily/Google around Sloan time in Boston. I didn't expect Evil Luke Bornn to be in the room, while I knew Seth Partnow would be there ahead of time.

This time, I knew Luke would be at CASSIS, but so was Sam Ventura, and there was a rumour that Daryl Morey was in Vancouver at the time (later confirmed), and could randomly show up as well. That created an odd, but amusing vibe. All of these people make appearances in this talk.

--Ted Knutson @mixedknuts ted@statsbomb.com

Explaining xGChain Passing Networks

Posted on by Ted Knutson

(Editor's Note: This was originally published on the StatsBomb Services blog, but the URL was lost in a server move. We have re-published it here so it can be referenced in future work.)

Some of the work we need to do on the StatsBomb Services side involves teaching people how to use what we create. If it’s not practically applicable and/or can’t be taught, then it’s just a piece of art, not analytics.Today I’m going to discuss passing networks, with a specific emphasis on the xGChain passing networks you’ll find on the StatsBomb IQ platform and also on our Twitter feed.

What is a Passing Network?

It’s the application of network theory and social network analysis to passing data in football. Each player is a node, and the passes between them are connections.

The first time I saw them used in football was either a presentation by Pedro Marques of Man City at the first OptaPro Forum, or Devin Pleuler’s work at Central Winger on the MLS site.

We also used them at Brentford to do opposition analysis, specifically to find which players we might want to aggressively press whenever they get the ball, or looking at valuable connections between players we wanted to break.

The application is simple.

  1. Look at a bunch of recent matches for a club and you will often start to see patterns of play and interesting details you care about.
  2. Investigate a little further in the data to find usage information
  3. Go to the video and see what shakes out.

In many cases, analysts only have time to watch and analyse the last 3 matches of opposition on video. Using the passing networks gives them quick info in an easily digestible format that doesn’t cost them an extra 10-20 hours of video time.

Before we go any further though, I think it’s important to speak about the limitations of passing networks. These are a tool and meant to be part of an analytics suite to help you analyse games, but like any tool, you need to understand their weaknesses.

First, each node consists of the average location of a player’s touches. If they switch sides of the pitch regularly, their average will look central, even if they never touch the ball in that area. This is a limitation of the vis and why we ALWAYS use video to back stuff up.

On the other hand, if you want to stay data-based, you could use things like heat maps, or even dot touch maps for every place a single player touched on the pitch to get more accuracy. This is a bit like using shot maps to supplement aggregate data in player radars to get a clearer picture.

The second limitation is that this info is an extrapolation of what actually happened. Did the fullback pass 15 times to the left wing, exactly along the path in the vis? No, of course not. That information is also easily visualized, but it’s just not contained here.

The third limitation is that these don’t actually explain that much by themselves. They take snapshots of actions through a match and combine them into a bigger picture. It’s like a movie where you only see 20 of 50 scenes without seeing the whole thing. Sometimes, you’ll end up with a clear idea of the plot. Other times, you are going to be really surprised when your friends start talking about the whole Verbal Kint/Kaiser Soze thing. They are still useful, but this is another reason why - in practice - we almost always pair this analysis with video work to complete the picture.

Design Stuff

Right, so we have passing networks. Some people do them vertically. We do them horizontally.

Why?

For starters, most humans are accustomed to looking at football matches left to right. High angle tactical cam footage from behind the goal is quite useful if you can get it, but the vast majority of the audience views football in a left to right perspective.

The next thing you notice is that we stack ours on top of each other. This happened as a bit of a happy accident where I noticed a pressing team had a map very high up the pitch. I then put the map from their opponent underneath, and voila! we had a fairly clear view of territoriality in the touch maps.

If you take a step back, it seems fairly obvious, right? There are two teams on the pitch, and each of their actions impacts the other one, so visualize both together. However, actions between two teams aren’t always linked. The shot locations of one team don’t have any impact on the locations of the opponent. Passes do though, so at least in my opinion, pairing them as part of this vis makes sense.

We also have them both going the same direction, which seems to strike some people as odd. All I can tell you is I think the territory element is much clearer if they go in the same direction, but people are welcome to test their own implementations and judge for themselves.

What else do we have… ah yes, the big difference: colour.

With passing networks, there is a real danger of adding so much information that your vis basically becomes unusable. It’s an incredibly info-dense visualization to begin with, so adding more elements is likely to make understanding what you are trying to display harder instead of easier. I think Thom walked this tightrope perfectly, adding the extra xGChain layer of data while still leaving it interpretable, and to be honest, totally gorgeous.

That said, it may take looking at these a number of times before you become comfortable with what they are trying to display. The same caveat was true of radars and shot maps, and is another reason why analysis blends elements of art with data science.

The xGChain Layer

First you need to understand what the xGChain metric is. So, any time a player is involved in a pass in the possession, they get xGC credit, and then we sum up their involvement over the course of a match and colour their node based on that.

Why?

Because this allows us to take the network vis beyond basic counting stats and starts to examine the value of a player’s contribution to the match. Because the colour scales are tied to the 5%/95% cutoffs I started back with the radars, you also get an easy reference for whether a player’s attacking contribution was pretty great (RED), pretty poor (GREEN), or somewhere in between.

We also start to get a sense of how non-attacking players are contributing to valuable build-up play in a way that just makes sense (at least to me).

Quick Reference

  • Size of node = number of touches
  • Thickness of line = number of passes between two nodes
  • Colour of node = linear scale from green to red (.6-1.4 xGCh based on 5%/95% cutoffs)
  • Colour of line = the total xGChain of possessions featuring a pass from A->B (0-.5 values based on 5%/95% cutoffs)

We Still Use Numbers

On Twitter, you will generally see just the visualization. This is mostly due to the limited, bite-size nature of the format. However, on the StatsBomb IQ app, Passing Networks also include all the individual and combination numbers you see below.

The combination of the vis and the numbers represents the whole of the analysis. The vis gives you basics, the numbers specifics, but both are still constrained by the limitations of this visualization format.

Examples

In this one you see Liverpool pushed quite far forward and had massive amounts of possession and created reasonable chances. Pretty much everyone is involved, but Coutinho and Lallana only put up good, not great xGChain numbers for the match. On the Swansea side, Llorente is the only guy up high most of the time, while he and Wayne Routledge both put up big numbers for the game, and Swansea came away with a vital win.

Just a single plot this time from Liverpool’s trip to Bournemouth earlier in the season, mostly to compare same team performance. Here Firmino is posted out wide instead of central, and had comparatively little impact in creating big scoring chances for LFC that match. Normally he’s a fiery red circle, but for this match he’s ineffective green. That’s another cool element these plots allow. Instead of focusing on the full match, you can isolate one player across a number of positions and games and see what it does to their performance.

I posted this one because both team’s maps are pretty incredible. City’s front three have average touches nearly on the 18, and nearly everyone except Claudio Bravo is red or orange. Meanwhile Boro had almost none of the ball and created almost nothing as well. The match ended 1-1, with Boro scoring a very late equalizer. 90% of the time our simulations think City win that match.

It’s always fascinating to see what happens to these maps when two elite teams square off. This is from the 1-0 Dortmund home win earlier this season. Bayern dominated the touches, but Dortmund just edged then in xG, 1.40 to 1.24. Aubameyang was rampant the entire game, and every time Dortmund touched the ball, they felt dangerous while doing a pretty good job of stymying Bayern’s great attackers.

How Do You Use This Inside a Professional Football Club?

Typically what I would do would be take passing networks for the last 10 matches from the next opposition and divide them into home and away games. Stick the numbers next to each of them for reference, and start to look for patterns.

Which players provide the engine for plan A when this team attacks?

Which players have the most valuable touches?

Does their fullback tend to get really high in possession and can we play behind them?

Which players should we look at for potential pressing triggers?

If we have a choice, which center back would we allow to play the ball forward?

Conclusion

This is already long, so I will wrap it up here. We view passing networks as an integral part of data-based football analysis. Provided you understand their limitations, they can provide a huge productivity boost to opposition and own team analysis. We also think the addition of our xGChain metric adds a layer of value to a visualization that previously only contained counting stats.

If you work in football and want to see what else the StatsBomb IQ platform has to offer, please get in touch.

--Ted Knutson

ted@statsbomb.com

@mixedknuts

I Think We Broke Denmark

Posted on by Ted Knutson

I was mucking around with an analysis for a customer this week when I ran across something I hadn't looked at in a really long time - the set piece table for Danish Superliga 14-15. That was the season FC Midtjylland (FCM) won their first ever Danish title, largely on the back of scoring tons of set piece goals. Brian Priske was the set piece and defensive coach that season and he and the players probably deserve 99% of the credit for those goals, but a tiny portion of what's left should probably be apportioned to Matthew Benham for the idea that this phase of the game was exploitable, and to my own work in designing the set piece program.

Anyway, the reason why I mention it is not to break my arm patting myself on the back, but because after this nostalgic instance of stumbling across the 14-15 stats, I wondered what the 17-18 set piece table looked like.

That's when things got weird...

Background

When FCM first started having success on set pieces, we discussed how to talk about this in a few internal meetings, especially with regard to questions from the press. I distinctly remember the message we landed on being one of happily crediting player skill and a bit of luck, but under no circumstances should anyone say that we worked on these more than normal.

Set piece goals to outsiders would hopefully be written off as things that magically happened, which was just fine by me.

(This splash image is from Daniel Taylor's scathing piece on Championship owners.)

That's why I thought it was weird when pieces like Sean Ingle's one from February 2015 started appearing in the press. Why was this thing that we knew was hugely important to us and driving a lot of our success, suddenly public knowledge? I still don't actually know, to be honest. My guess was that it provided a counterpoint of positivity to the ongoing Warburton mess at Brentford, but even acknowledging the edge existed - and one that would likely be sustainable long term - seemed incredibly dumb.

One of the big rules of conducting sports analytics inside a team is that when you find an edge, you exploit the hell out of it.

And you never talk about it in public.

Why not? Because professional sport is competitive and you don't want to make your competition any smarter. Plenty of them will ignore the information or not be able figure out how to successfully exploit your edge directly, but even one team copying an edge for free is too many.

In many cases, the edge only exists because people don't know it's there to begin with. That's why many coaches and general managers/directors of football will outright lie when reporters start asking questions in these areas.

The Fallout

In a way, the public discussion created a fascinating economics question. How do actors in competitive economies adapt behaviour to new information over time? Or to put this in more obvious sports terms, what happens when a comparative league minnow wins a title on the back of scoring a lot of set piece goals, and then tells the entire world what they did?

Welcome to Denmark!

That is a lot of set piece goals. Like... a LOT. And from a whole bunch of non-Midtjylland teams. Brian Priske would later spend some time helping giants FC Kopenhagn crush the league (partly also via dominating from set pieces), and his expertise may have dispersed into greater Denmark a bit, but this whole "we too can score lots of set piece goals" idea has clearly caught on up North.

In 14-15, FCM were the only team in the league to crush this particular phase of the game, scoring 25 goals, while three other teams barely cracked 10. Three years later, eleven of fourteen teams were in double digits.

I Now Have SO MANY QUESTIONS

One of the things I used to argue about with my long-time collaborator Marek Kwiatkowski was whether working on set pieces more in training forces trade-offs in other areas. I was firmly in the "you can score more goals, period" camp (Marek is a natural uber-skeptic), but it was mostly just theory. However, now we get a chance to look at exactly that.

Are the total goal outputs largely fixed and you just shuffle between open play and set pieces, or can you just plain score more goals by adding set piece expertise? To put it another way, can you create a bigger pie or are you just carving out different sized pieces?

Set piece goals per game, 14-15: .55
Set piece goals per game, 17-18: .75
Total goals per game, 14-15: 2.41
Total goals per game, 17-18: 2.91

Set piece goals per game have gone up by .20, while overall scoring is up half a goal a game. This lends weight to the bigger pie hypothesis, and not merely different sized pieces.

Note: The Danish league changed structure between 14-15 and 17-18 by adding additional teams and the world's most complicated playoff structure, so it's not as clean as an analysis as it might otherwise be. Professional sports...

But wait, you say... some of that increase can be explained by competitive reasons, right? By increasing the league size, they probably brought in some weaker teams that were more likely to get blown out.

A fair point. Going to the first year of the 14-team league, we see... .63 goals from set pieces and 2.65 goals a game. Again, more set piece goals and more goals overall.

Shouldn't there be an equilibrium, though? Teams are scoring more set pieces, and they presumably know how to defend better against them as well, right? So why are we seeing so many more goals?

This is where we get to a bit of theory. Back when I was at FCM, someone asked our striker Duncan about defending set pieces, and his reply was that if the timing and delivery were right, the goals were basically unstoppable.

Now part of this comes back to creating complexity in your delivery and route patterns, and who you are targeting on all your different set pieces. You can't just do what England did in the World Cup and run the same play over and over again and succeed. You might be able to get away with that for a few games, but you'll struggle mightily through a league season. Defenses will adjust to that type of basic plan. However, if you are smart about your planning... well, maybe the goals actually are unstoppable.

Given the fact that the fewest set pieces goals conceded in the entire league was nine (and three seasons earlier, it was just four), either everyone in Denmark suddenly became really bad at defending set pieces or everyone became much better at executing them in ways that were difficult to stop.

I lean toward the latter.

The analysis above isn't scientific or conclusive. There are confounding factors, and football is an inherently complex game that often defies simple explanations anyway. However, I find the dramatic increase in set piece goals across the entire league here fascinating, and if we were building a case that you can increase set piece goal production at the cost of basically nothing else, we now have some evidence that perspective may be correct.

My Own Work

One of the things I am happiest about regarding set pieces is that what we built at Midtjylland was sustainable, despite the fact the coach initially responsible for the success was poached by a bigger club. Well done to Mads Buttgeireit for continuing to innovate in this area, and well done to FCM for listening when I said, "You HAVE to get Priske a fucking set piece assistant or you'll lose tens of millions of euros in value if he ever leaves."

Listening is underrated.

We also know for a fact that data analysis has dramatically changed the way that both baseball and basketball are played now. It's not just about finding better players, it's often about finding fundamentally superior styles of play before your competition and then beating them with it over and over again until they adopt your style.

In light of the above, I still find it amusing that this summer, no one on the club side came and talked to us about set pieces. The World Cup of Set Pieces was great. I broke down a lot of things, both on the site and on Twitter. Still, zero interaction.

¯\_(ツ)_/¯

In a way, this was really good, because I honestly did not have the bandwidth to spare while also launching StatsBomb Data. (I probably still don't, but football is a siren's call.) In another way, it's just continuing evidence that football is glacial when it comes to adopting new ideas from outsiders. Lest you think things are progressing behind the scenes in England, the Premier League scored 214 set piece goals in 2017-18... and 216 in 14-15. Fair dos to Bournemouth and Eddie Howe/Tom Webber for leading the league in this area last year though.

Our price for consulting on this is not cheap. We don't need to be. We still get you goals at a huge discount vs what you pay at the player or manager level without cannibalising anything else. And we teach your club personnel how to sustain this edge. The value you get at the club level is stupidly large.

And like I said above, it's not like teams were put off by the price... no one even had the conversation. *

One thing I do want to note is that if you are a national team and want help with set pieces for the Women's World Cup, definitely get in touch because like with our data, we will offer a deep discount to support the women's side of the game.

Conclusion

I will always be a nerd at heart, so finding data on how the Danish Superliga ecosystem changed after we shocked it in 2014-15 was super exciting to me and I had to write about it. While it doesn't offer conclusive proof of anything, it certainly allows you to ask interesting questions about what would happen if the rest of the football world starts to adopt advice on better ways to play the game that were reached largely via data and analytics.

Thank you for listening!

Ted Knutson
CEO, Founder StatsBomb
ted@statsbomb.com
@mixedknuts

*And I also know that plenty of you are in clubs already and listening, and you'll take what you learn from us and do it on your own and probably succeed at least somewhat, because you are smart and it's not that hard to do better than what you have now. It's probably pretty hard to score 25 every season like FCM though.

Other Writing

Changing How the World Thinks About Set Pieces Set Pieces and Market Inefficiency.

Historic data used in this piece was licensed from Opta

The World Cup of Set Pieces

Posted on by Ted Knutson

The World Cup is upon us. FIFA's glorious international smorgasbord, where every day throughout the group stage, fans are treated to an incredible array of matches. Some, like Spain vs Portugal, are incredible. Others, like Croatia vs. Nigeria are mostly inedible, but you don't know that until you have already eaten it, so it is part of the fun. Plus, there's always more to gorge on tomorrow!

Long-time StatsBomb fans know that we are advocates of set pieces.

I have written two major pieces on this, the first discussing why it seems to be a big hole in the game, and the second with more a focus on market dynamics, and how much cheaper it would be to generate additional goals via improved set piece coaching than trying to buy the same improvement in a forward. We also developed and maintain an ongoing Set Piece Program as part of our consultancy work for teams at the club level and for international federations.

Usually, set pieces account for 25-33% of all goals scored in a league. They almost never account for that amount of training time or resources. That percentage seems to jump a bit in international tournaments because teams are more risk averse, leading to fewer open play goals, but similar numbers of set pieces. Thus far in the 2018 World Cup, set pieces account for right around half of the goals. Presumably this will slow down a bit, but I thought it would be fun to break down certain elements that stood out for me thus far.

Ronaldo's Changing Technique

Ronaldo's generally used a knuckleball technique on free kicks for years now, to very mixed success.

This technique is useful for flighting the ball from range with very little spin. It makes it easier to hit it hard, but depending on the characteristics of the ball and the wind, not always that easy to hit the target because the ball can move in unexpected ways. However, when it comes down, it can descend hard and fast. And "unexpected" isn't always bad, because it causes problems for GKs as well. Prior to his goal against Spain that made it 3-3, he'd apparently had zero success in his last 45 attempts from direct free kicks.

As Ronaldo was lining up the kick, Danny Murphy said on English commentary that he didn't think he could get the ball over the wall and back down again. That seemed a bit silly, because good takers can easily get the ball back down from 22-23 yards, but maybe less so with the knuckler? Let's face it, Ronaldo just hasn't been great at free kicks for a while.

Hence I was surprised to see this.

Side foot, gorgeous top and side spin, perfect placement. BOOM! 3-3. Portugal didn't even set up a screen to help block the goalkeeper's sightlines - Ronaldo's free kick was just that good. However, the difference in technique is notable. Professional golfers constantly work on their swing to keep adjusting to the changing reality of their body, and also to work through current problems.

In this case, it's like Ronaldo put away his driver for once and picked out the 8-iron instead. There is no reason this can't and shouldn't happen in football, and it was really cool to see Ronaldo change up technique based on circumstances. Speaking of free kicks...

GK Screens

I don't know how Kolarov hits that so hard and with so much spin in what looks like an easy motion, but that's his gift. Unlike on Ronaldo's kick, Serbia do send two players out to the wall to help mess with the keeper's vision, and it looks like it may have had a small effect. Check out the double hop from the GK as he sets his feet to save (admittedly easier to see in the video highlights).

Now the extra screen may or may not have had an impact on when the GK saw the ball, but there's a good chance the keeper was unsighted until the very moment that ball came over the wall, which is a big edge. This is what you'd expect to see and why you do it. GK reaction time = space to hit the target where he can't make the save. He has to reset his feet a second time before he picks up the flight of the ball and make a leap. Maybe it was unsaveable, but good process is good process and deserves noting. (Note: I discussed GK screens a lot more here.)

Read Plays

Diego Costa's second for Spain was very interesting. First of all, this is normally (and correctly) a direct shot on goal.

Usually you line a bunch of guys up with the wall, someone whacks the bejesus out of it, and a small percent of the time, it's a goal, and once in a very rare while you get a lucky rebound. Spain didn't do any of that.

Look at the formation of the Spanish players above.

They have split two players very wide here on a free kick from dead central. Why? David Silva does what American sports generally call a "read", analysing who is matched up against each of the wide players, looking for a potential mismatch. In this case, he sees Busquets at 1.89m matches against Guedes at 1.78.

(Watch the video just before he takes the kick and you can see him glance again to look toward Busi.)

This triggers the floated wide ball, where Busquets is more likely to beat his man and win a header back down into the center of the box, which can then be cleaned up by whichever runners manage to get clear of their man (in this case it was Diego Costa and Gerard Pique). If neither wide player is mis-marked, Spain can either take the DFK, or restart the possession normally.

All of this is based on the kick taker's analysis of the situation. It's a clever way of testing the opposition understanding of the strengths and weaknesses of your team, and building variation into identical formation setups. All it takes is one screw up to yield a goal, and the vast majority of football matches are decided by one goal alone. Thus far in this World Cup, a remarkable amount of those goals have come from set piece situations.

Now if you'll excuse me, I have so many more games to watch.

Ted Knutson @mixedknuts ted@statsbomb.com (Header image courtesy of the Press Association)

Let's Talk About Press, Baybee

Posted on by Ted Knutson

I have two major things on my agenda for today’s piece:

  1. Get a positively ancient Salt n’ Pepa song stuck in the heads of most of our readers simply by reading the title.
  2. To deliver a foundational piece discussing why we here at StatsBomb seem to care a great deal about pressing, both at the team and the player level.

However, in order to examine the press, we first have to discuss an often-overlooked topic in online football discourse: defending.

Defensive Choices and Principles

All tactical choices in football start with defending. Choices for how to defend strongly impact what types of players are needed across all the positions in the squad. The defensive structure of the team determines what is possible and necessary in the transition to attack. Regardless of what style of defending you choose, every system has inherent trade offs. High pressing leaves you vulnerable to high quality shots when opponents break the press. Deep blocks rarely restrict volume of opposition chances, but instead try to restrict the quality of those chances. Middle blocks try to strike a balance between restricting quantity and quality, but take high levels of organization and can concede too much space to dangerous players out wide. It’s worth noting that even inside these categories there remains a huge amount of variation.

Deep Block – Traditional English

This is the style usually employed by teams managed by Tony Pulis, Sam Allardyce, and weirdly enough, Arsenal. (Arsenal’s defense has been coached by Steve Bould, though Arsenal tend to include some mild gegenpressing.) It continues to see moderate success in the English lower leagues. Deep Block teams sacrifice control of the defensive half of the pitch in order to better control the area in and around the box. Defense of this style is typically happy to concede higher volumes of shots from distance on the assumption that few of these will be scored.

Middle Block (Portuguese and Italian Variations)

This style of defending is focused on jamming the middle of the pitch 20-40m away from goal with bodies, thus forcing opponents wide if they want to progress the ball. Progression through the middle is heavily contested, and progression wide often gets trapped, or results in easily cleaned up crosses. They will allow the opponent a ton of possession in “safe” areas, far from goal, but get aggressive as the ball approaches danger areas. Strengths of this style are that it blends some of the benefits of low block and high press together. Good middle blocks are excellent at constraining shot volume, while also leaving opponents with poor quality shots when they do get them. Antonio Conte’s teams are a good example of elite middle block teams, but it’s fairly common among well-trained Italian and Portuguese head coaches.

High Press – Many Variations

Pressing styles have gained popularity in the last decade, largely because of how successful they have been. Guardiola’s style is an elite positional press that almost no one in football really replicates. Thomas Tuchel’s press from his Dortmund days is probably the closest anyone has come, but still differs in execution. Jurgen Klopp’s press is also different from Guardiola’s, but has also been very successful over the years. It comes out of the Ralf Rangnick/Hoffenheim school of zonal pressure, but has morphed enough over the years from its roots that it’s probably unique. Then you get to the Salzburg/Roger Schmidt press, which shares common roots in the Hoffenheim school, but is now probably the most aggressive of the lot.

Those teams typically run a 4-2-2-2 formation, and are defined by a narrow, extremely intense pressing style that focuses keeping the ball high up the pitch in wide areas, and winning the ball back early to then immediately transition against the goal. Adi Hutter has also successfully used this style to dominate the Swiss Super League this past season, despite a fairly massive budget disparity when compared with Basel. The benefits of high pressing come from severe shot constraints to the opposition.

Elite high pressure teams can give up as few as 6.5 shots per match across a season, which is an absurd number. The flip side of this is giving up very good chances when opponents do break the press, which happens more against teams with talent or when your pressing side lacks pace. Additionally, this type of defending takes very high physical output, which can wear a squad down over a season, especially across multiple competitions. Finally, it requires serious athletes, with pace in nearly every position and some amount of tactical smarts, because the tactical concept of “covering shadows” is damned important. Note: Man-marking presses like the Dutch style are probably outmoded and tend to constrain the attack too much to function well in the modern day, especially in better leagues.

Traditional Defensive Stats

Historically, the defensive stats we had access to were tackles, interceptions, fouls, clearances, blocks, aerial wins, and fouls, plus a few minor additions. They are useful, but inherently limited in helping you evaluate team tactics and player output via data (and believe me, I have tried). Pressures as a type of event don’t exist in other event data sets, but they do in StatsBomb Data, and we think this is a Big Deal(™). Let me walk through some examples to better illustrate why. Take two teams from the English Premier League 17-18. These are their traditional defensive stats per game:

On the surface, there is very little separating these teams, and that includes when you look at their defensive activity maps. These visualisations plot where defensive actions occur on the pitch in each zone, and then colour them hot or cold compared to league averages.

So it looks like Team A is a little more active defensively each match based on the stats, and they defend in very similar areas. But what happens when you add pressure data into the mix?

Team A looks basically the same compared to league average, while Team B looks complete and utterly different with the incorporation of pressure data. Now, instead of a traditional deep block, Team B looks absolutely miserable to play against. You simply do not get time on the ball against Team B almost anywhere on the pitch. Team A = West Brom Team B = Burnley These are two teams you could easily describe as Deep Block in style, but boy do they vary in execution when you play against them, and from a data perspective, you only see that when you layer in pressing information.

Pressure Players

Let’s drill down a little further into analysing individual players. Like many sports, football is an invasion sport. As in basketball and hockey, each team attempts to defend a goal, and possession is traded back and forth regularly between both sides. The two obvious questions about gaining an edge in this system then become:

  1. Can we get an advantage over our opponent in scoring chances or difference per possession, or trip up and down the pitch?
  2. Can we create more possessions?

Traditional defensive stats usually reflect a possession change. Defenders can tackle or intercept the ball, they can get a ball recovery off a mistake, they can win headers, they can block/clear the ball, the ball can be claimed/saved by a Goalkeeper, and that’s about it. But there’s also one other thing they do more than anything else, that hasn’t really been recorded before: they can force mistakes. And that is where pressure comes into play. Take Roberto Firmino from this season.

 

77 total tackles and interceptions... 723 pressures. The broad definition of a pressure is to close down an opponent in a way that forces them to make a decision. And sometimes - actually, LOTS of times - those decisions* fail and result in a change of possession. *Decisions, or actions under pressure, or inevitably, because Twitter shortens everything, AUPs Here’s Firmino’s map again, but this time with a focus on just those actions that resulted in an obvious change of possession (tackle, interception), or when his pressure caused the opponent’s action to fail (usually a failed pass).

76 tackles and interceptions… 100 forced failures of actions under pressure. Nabil Fekir is an excellent player, and I’ve been high on his potential for years, but in addition to his attacking skills, he had a monster season pressing the opponent at Lyon. This extra layer of information reinforces why Liverpool should be interested in Fekir on any number of levels.

And it’s not just the hard-working, sometimes underappreciated forwards that benefit from this info, it’s also hard-working, often underappreciated defensive midfielders as well.

 

Kante is everywhere, but so is Ndidi, at least when it comes to defensive output. Kante's numbers are probably more exciting here because Chelsea tended to dominate possession more than Leicester this season, but both of these gentlemen Do Work.

Further Research

  • The visualisations above only show failures caused immediately after/by the pressure, but sometimes a press is a destabilising factor for future actions in a possession. We can continue the filtering process to find possessions that broke down 1, 2, X actions after the pressure from a player to better attribute credit.
  • For any particular match, we can look at who is being pressed, and specifically which actions are being put under pressure, and where. This can help in building game plans, but also in better evaluating where your own attacks are breaking down.
  • How does pressing change over game time? With fatigue? After subs? Based on game state?

Thank you for listening to me ramble on about why we think pressure events are such a big deal when it comes to analysing football. We're admittedly just scratching the surface on the information from the new data set, but the new avenues for research opened by StatsBomb Data are incredibly exciting.

Ted Knutson

ted@statsbomb.com

@mixedknuts

Photo courtesy of the Press Association

The Dual Life of Expected Goals (Part 1)

Posted on by Ted Knutson

“Let me explain... No, there is too much. Let me sum up”  --Inigo Montoya

The great thing about running a football stats website is that you get to do things like devote thousands of words entirely to a single statistic, and there’s nobody to tell you not to. So, let’s get into expected goals, what it is, where it came from, and most importantly where it’s going from here. Lots of football fans have only experienced the good ol’ xG as a single game number, either included on the bottom of a TV scroll, next to shots, fouls and assorted other stats, or on twitter as a pretty little shot map. That wasn’t what it was designed for though. Single game xG is a useful tool (and one we here at International StatsBomb Headquarters are committed to making more useful) but it was originally developed for something entirely different.

Goals: The Only Stat that Matters

In the beginning there were goals. Just goals. That was the only thing that was counted. Whoever had the most goals won the most games. You play to win the games. Therefore, the only thing that mattered was counting goals. There were some exceptions of course. Charles Reep notably counted passes by hand well before the rest of the world decided to do the same. But, for the most part, people watched football and counted goals, and the years went by. Eventually, somebody decided to count the passes leading to goals as well. And voila, there were assists.

At that moment, at the dawn of statistical time, a schism was born. On a team level, the statistic of goals gives you more information, than if you didn’t have it. Not only is it the way in which we keep score, but also the knowledge of a team’s goal difference helps observers determine how good they are with more accuracy than if they knew whether they had won or lost. On the other hand, knowing about a team’s assists doesn’t give an outside any more information about how good the team is. There’s a reason that goal differential is a thing and assist differential isn’t.

Statistics, at their heart, serve two purposes. The first is predictive. What do knowing these numbers tell us about the future? Knowing how many goals a team scored and conceded makes people better able to predict how likely a team is to win future games. The second is descriptive. What do these numbers tell observers about how things happened? Assists are a descriptive statistics, and a useful one, but they aren't especially predictive. If assists were zapped out of existence overnight there'd be very little impact on the world's ability to predict the outcome of football matches.

That’s a tension that has always existed, and it’s one that remains at the heart of how the football world is increasingly using xG.

Shoot Your Shots

Before getting to modern statistical times, there’s one more stop to make. One of the first things that statisticians began regularly counting was the number of shots teams were taking. It’s an obvious statistic to look at, and it turns out that it’s pretty important. You cannot score (for the most part) if you do not shoot. This is not rocket science. It’s not even bottle rocket science.

As Ted and James talked about on the last StatsBomb podcast the groundwork for looking at shots in football was laid in hockey. In hockey shots served both a clear descriptive purpose and provided predictive utility. Shots in hockey are a pretty good way of describing who has possession. Descriptively, by saying teams have a lot of shots, you can also say that teams have a lot of the puck. Predictively they also have a lot of value. In hockey the best teams reliably take a lot more shots than their opponents, but it’s very hard to control how often the shots a team takes are scored. By measuring how many more shots a hockey team takes than their opponents, it gets easier to predict which hockey teams will do well in the future.

Those findings were applicable to football, but only in a limited way. The first major problem is that descriptively comparing shots is not a particularly good way to measure possession. The relationship between possession and shooting is a lot looser in football than hockey (this will surprise nobody who has watched either sport for more than ten minutes. It’s mostly down to one sport being played with feet on grass and the other one being played with sticks on ice. Small things like that.). Using shots as a proxy for possession doesn’t really work. Broadly speaking football uses passes played to measure possession, which is better, but not perfect.

Despite that, measuring shots is still pretty good as a predictive tool. Knowing how many shots a team has taken and conceded makes you even more able to predict how they’ll do in the future than if you only knew about their goals scored and conceded. That’s great. It’s also frustrating. The gap between shots’ predictive power and descriptive power makes it impossible to turn the information we get from shot differentials into anything resembling insight.

The information those stats contains does a pretty good job of explaining what will probably happen next, and a terrible job of explaining why. If a team is scoring a particularly high percentage of their shots, or on a particularly cold run, looking at shot numbers doesn’t offer any answers as to why it’s happening. All that they have to offer is an assurance that it probably won’t continue.

One thing that’s important to note here is that just because these stats can’t provide a reason for the divergence between shooting and scoring doesn’t mean there isn’t one (or many), it just means that those reasons are incidental to predicting what comes next. That’s an answer that’s useful to only a very small group of people (mostly the ones looking to put a bet down). It doesn’t help people interested in understanding what’s going on, people like, say, coaches who have to make the hundreds of daily decisions which go into running a team.

And now, finally, we get to the good stuff.

What to Expect When You’re Expecting Goals

Using past shots to predict how will teams will do in the future is good. Further modifying that to factor in what type of shots teams are taking is even better. That’s, in effect, what xG does. Notably what xG was not developed to do is accurately describe a single shot or a single game. Rather, it was designed to take lots of information, thousands and thousands of shots, synthesize it, and use that information to represent how many goals a team might reasonably be expected to score or concede given the types of shots they’ve taken and given up.

This is good and useful information. There are ample studies showing how this process is better at predicting how a team will do in the future than pretty much anything else out there. It takes the old information, based purely on the volume of shots and improves it. It turns out that sometimes when a team is shooting better or worse than average it’s because on average they’re taking better or worse shots.

There are two problems with xG as currently constituted. The first is that just like with a basic shot based metric teams frequently spend stretches of time doing better or worse than where the metric thinks they’ll end up. And, just like with shots, xG doesn’t offer many answers other than the (quite good) prediction that eventually that will stop. It explains part of what shots miss, but there’s still plenty of room left blank.

The problem of what xG might be missing in the short term is encapsulated by how it’s used for single games. It’s important to start off by saying, that xG maps contain more information than pretty much any other form of quick glance game recap. But it’s not what it was designed for. The total goals a team score will often differ wildly from what xG predicts. Frequently this is by design. If a player misses a sitter, xG and actual goals should differ. That’s the point. The model is crediting the team for creating the chance, understanding that in the future creating those chances will lead to goals.

So, there’s a way in which single game xG totals differing from the result is a direct sign that the model is working. But, there’s another reason they can differ as well. The value that an xG model assigns to any given specific shot is based on an average of past similar shots. So,  it takes into account things like location, whether or not it’s a header, the kind of pass that led to the shot, etc etc, mixes them all together and spits out a value.

The problem with averages is that they’re averages. Any single chance can differ significantly from that average. Because we know that xG works, and is quite predictive, we know that over the long run the ways those individual shots differentiate from average more or less cancel each other out. But, during a single game, that definitely doesn’t happen. A team with a high xG total but no goals might have missed a bunch of good chances, or the chances they had might have been harder than the model predicts. Single game xG totals don't differentiate between the two.

Luckily StatsBomb can help with that problem. To find out how, stay tuned for part two.

StatsBomb Data Launch - Finding Better Goalkeepers

Posted on by Ted Knutson

Embedded below is the second of our presentations from the StatsBomb Data Launch event. The presentation is given by data scientist Derrick Yam and is called Beyond Save Percentage, to pair with my presentation called Beyond xG. However, Derrick's ambition here is actually much greater. He asks:

  • Can we use the new information in StatsBomb Data to help find the best GK in the world?
  • At the same time, can we start to find up and coming GK to keep an eye on?
  • Can we help quantify GK value through data?
  • and Can we build a better framework for data analysis of GK to help do all of this?

Check it out...

On Burnley and Expected Goals

Posted on by Ted Knutson

Expected goals has found itself outside the confines of the analytics community this season, which has brought renewed questions regarding its flaws, foibles and failures. The poster-child for expected goals flaws has been Burnley and their over-performing defence, even finding themselves in the New York Times courtesy of Rory Smith. Smith's article is a fine piece blending comments and insights from the analytics community and Sean Dyche himself. Smith quotes Dyche describing Burnley's game-plan when defending:

The way it is designed is to put a player in a position that it is statistically, visually and from experience, harder to score from.

Several analyses have dug deeper into Burnley's defence last season, including an excellent piece by Mark Thompson for StatsBomb. In his article, Mark used data from Stratagem to describe how Burnley put more men between the shooter and the goal than their peers, which may go some way to explaining their over-performance compared with expected goals.

Further work on the EightyFivePoints blog quantified how the number of intervening defenders affected chance quality. The author found that when comparing an expected goal model with and without information on the number of intervening defenders and a rating for defensive pressure, Burnley saw the largest absolute difference between these models (approximately 4 goals over the season). If there is a quibble with Smith's article it is that it mainly focuses on this season, which was only 12 games old at the time of writing. Much can happen in small samples where variance often reigns, so perhaps expanding the analysis to more seasons would be prudent. The table below summarises Burnley's goals and expected goals conceded over the past three and a bit seasons.

Burnley's non-penalty expected goals and goals conceded over the past four seasons. Figures for first 13 games of 2017/18 season.

Each season is marked by over-performance, with fewer goals conceded than expected. This ranges from 5 goals last season to a scarcely believable 15 goals during their promotion season in the Championship. The above table and cited articles paint a picture of a team that has developed a game-plan that somewhat flummoxes expected goals and gains Burnley an edge when defending. However, if we dig a little deeper, the story isn't quite as neat as would perhaps be expected. Below are cumulative totals for goals and expected goals as well as the cumulative difference over each season.

Burnley's cumulative non-penalty goals and expected goals conceded (left) and the cumulative difference between them (right) over the past four seasons. Figures for first 13 games of 2017/18 season.

In their 2014/15 season, Burnley were actually conceding more goals than expected for the majority of the season until a run of clean sheets at the end of the season saw them out-perform expected goals. After the 12 game mark in their Championship season, they steadily out-performed expected goals, yielding a huge over-performance. This continued into their 2016/17 season in the Premier League over the first 10 games where they conceded only 12 goals compared to an expected 19. However, over the remainder of the season, they slightly under-performed as they conceded 39 goals compared with 36 expected goals.

The above illustrates that Burnley's over-performance in previous Premier League seasons is actually driven by just a handful of games, rather than a systematic edge when examining expected goals. This leaves us needing to put a lot of weight on their Championship season if we're going to believe that Burnley are magical when it comes to expected goals. While the descriptive and predictive qualities of expected goals in the Premier League is well-established, there is less supporting evidence for the Championship. Consequently it may be wise to take their Championship figures with a few grains of salt.

This season and last has seen Burnley get off to hot starts, with what looks like a stone-cold classic example of regression to the mean last season. If we ignore positive variance for a moment, perhaps their opponents got wise to their defensive tactics and adapted last season but then you have to assume that they've either forgotten the lessons learned this season or Dyche has instigated a new and improved game-plan. The cumulative timelines paint a different picture to the season aggregated numbers, which might lead us to conclude that Burnley's tactics don't give them quite the edge that we've been led to believe. In truth we'll never be able to pin down exactly how much is positive variance and how much is driven by their game-plan. However, we can state that given our knowledge of the greater predictive qualities of expected goals when compared to actual goals, we would expect Burnley's goals against column to be closer to their expected rate (1.3 goals per game) than their current rate (0.6 goals per game) over the rest of the season.

Time will tell.

Set Pieces and Market Efficiency

Posted on by Ted Knutson

At this point, the world transfer market for strikers is fairly efficient. Guys who can get you goals command high fees, are highly paid, and very few good ones get overlooked. They might also get overpaid, but a lot of that has to do with scarcity of elite forwards, and a preponderance of a lot of clubs with a lot of money who need the best. One of the reasons for general efficiency in this area is that the value of forwards is obvious.

  • Goals are valuable.
  • Lots of goals are very valuable.
  • We need to score goals in order to win.

Conclusion: Forwards who score lots of goals are very valuable to us.

It helps that they are also easy to identify.

Why are we talking about forwards and transfers when this piece is about set pieces? Because the market for coaching expertise is laughably inefficient, and better set piece attack and defense comes from coaches.

A couple of weeks ago I was on a panel at the Royal Statistical Society conference in Glasgow when Omar Chaudhuri made exactly this point. “Premier League clubs barely blink at spending £10-20 million on a player, but almost none of them are willing to pay even a few million in compensation for a top coach.”

This is weird when top coaches actually have the most impact of what happens on the pitch of any single person at a football club. Granted, many coaches are average and average coaches seem to have very little impact overall, but coaching impact is a U curve - the good ones and the bad ones cause huge swings in team performance.

Ironically, one of the few managers where I can remember hearing a compensation fee was paid was Alan Pardew’s move from Newcastle to Crystal Palace. Handsome Pards may be many things, but he’s probably one of the last managers you would want to shell out extra money for. Good process, bad result?

It’s even more ironic because there was a strong rumour that Thomas Tuchel was also part of the extensive search that lead to Pardew being hired. He was on sabbatical then after leaving Mainz, would not have required a fee, but is one of the few coaches in the world you’d be happy to pay for. Ouch.

*Looks at where Palace currently are in the table.*

Ouch again.

I wrote two different pieces on hiring coaches last week, so if you want more information about it, please read these.

What You Really Need to Know About Manager Recruitment

The Right Way To Hire Football Coaches

Talking to Clubs About Set Pieces

As you may or may not know, I own StatsBomb, which has a cutting edge analytics platform called StatsBomb IQ, and offers consulting for clubs across a variety of topics including player recruitment, manager hiring, club valuations for potential buyers, and… set pieces.

Why set pieces? Based on research conducted in summer 2014, I discovered that set piece execution was one of the great overlooked edges in football. As a result, I developed a set piece program that played a tiny part in helping FC Midtjylland win their first Superliga crown (FCM scored 3 goals in every 4 games from set pieces alone before clinching the title). After leaving Brentford and Midtjylland, we continued developing the program, and now offer expertise in this area to clubs.

Now this is the type of edge that people usually don’t talk about once they find it. You don’t want clubs keying on what you are doing in this phase of the game, and certainly don’t want anyone else to find it either. However, owning a consulting business that offers this as a product means you need to tell people about it, which is exactly what I did in detail here.

Changing How the World Thinks About Set Pieces

I understand some general scepticism, and at this point I’m pretty used to initial disbelief [Yo, Alvaro], but justifying investment in this area is incredibly easy.

  • A single goal in the Premier League is worth about £2M
  • Players who can score you goals are very expensive.
  • Players who obviously prevent goals are also very expensive. (Top centre backs command fees of 50M and big wages. Manchester City spent about 150M on fullbacks alone this summer.)

Conclusion: Finding additional ways to score and prevent goals has tremendous value.

Now combine that with my point above

  • The coaching market is currently very inefficient

Cross it with these...

  • Set piece expertise is rare
  • Getting better at set pieces in attack directly leads to more goals without a transfer fee or massive player wages
  • Getting better at defending set pieces directly leads to fewer goals, again without a transfer fee or massive player wages

And you wind up with: Set pieces are both horribly misunderstood and undervalued.

BUT!

Don’t just take my word for it! There’s a strong case to be made that set piece dominance helped power Chelsea to the title last season.

Chelsea were +15 in this phase of the game.

Man City were +2.

They scored nearly identical amounts of goals from open play, but the goal difference in the table between Chelsea and City was +11 in favour of the champions.

There’s also a decent case to be made that set pieces helped save Swansea as well. Swansea were abysmal for years in this phase of the game, but I know for a fact it was a weakness discussed with the owners before the season started. Last season the Swans finished 3rd in the league in set piece goals scored behind only Chelsea and West Brom. That's quite a departure from the 8 they scored in 15-16 and the 4 - yes, FOUR - they scored in 14-15.

Outside England, Monaco beat PSG to the Ligue 1 title last season on the back of 23 set piece goals (PSG scored 13), and they’ve already scored 8 in 7 games this year, despite selling what felt like half their squad in the summer transfer window.

So the first edge comes from knowing you can move the needle significantly in this area if you start to focus on set pieces and give them training time.

Just so we're clear, this has not been common sense or common knowledge in football. A few years ago the analyst community thought performance in this area was largely random, and it’s still treated as an afterthought at the vast majority of football clubs.

A Story

Last year I was invited to talk to a big club outside of England about using data and competitive edges. The discussion rolled around to set pieces, and I gave a presentation similar to what I linked in my intro piece above, highlighting different areas you can focus on with corners, free kicks, etc At the time I was there, the club was doing terribly on attacking set pieces, so I flagged a few quick things they might want to work on to improve.

One of the easy ones was discussing the use of screens on direct free kicks. Allow me to explain…

For goalkeepers, reaction time equates to distance.

The quicker your reactions are, the further you/your hands/your body can travel in order to make saves. Additionally, the further away a goalkeeper sees a shot, the more time they have to react and move to make a save. We even mix up the jargon here when describing what happened in commentary. “He saw it early, so he was able to make the save.” Early is a word associated with time, but what we really care about with GKs is how far they can travel before to intersect that ball before a shot gets past them.

So yeah, time equals distance. Goalkeepers and quantum mechanics for the win.

With direct free kicks, this is quite a big deal. Walls tend to impair goalkeeper sight at least a little bit, and one of the things I learned from Gianni Vio is that attacking teams can make sightlines absolutely miserable for keepers if they want to.

Why would they want to?

Because the later a goalkeeper picks up the ball in flight, the less distance they can move to make a save, and the bigger the target the free kick taker has in order to score a goal.

Translation: it makes scoring a goal from a free kick a lot easier.

I was reminded of this when watching Liverpool face Leicester this weekend. Coutinho’s goal Saturday might have been unsaveable, but he had a goal against Arsenal last season from a free kick that I broke down at the time in a frustrated email to friends at [redacted].

Notice the two-man Liverpool screen in Arsenal's wall. They are standing there to help obstruct Cech's view of the kick as it's taken. There are actually better things they could do to cause problem, but it's a solid start.

Also note how far out this kick is taken. Cech should have ages to see the ball and make the save, even though he's probably a bit too far over in positioning to the far post.

But this is approximately where Cech finally sees the ball. It's hit pretty hard, which creates huge problems (remember, time = distance).

And finally, it's a bit fuzzy, but this is how much Cech missed the ball by. It's a tiny amount.

The reason I bring this up is because of the reaction I got from the coach I was presenting to at the time.

“Well that’s very simple and obvious,” he said.

And he’s right. It’s a gobsmackingly obvious thing to do…

IF YOU KNOW ABOUT IT.

In fact, it's one of the very basic tells Nikos Overheul and I use to see if teams are paying a lot (or even a little) attention to set pieces when we scout them for tactics.

“You’re probably correct,” I agreed. “On the other hand, before travelling here, I watched every direct free kick your team has taken for the last year and your players never screen the keeper.”

*silence*

“I'm with you though, this one is easy to fix once you know about it. Thankfully there are a lot of less obvious things we do to help in this phase of the game as well.”

Sadly, we did not get a chance to help them on set pieces last season.

And despite how obvious my point may have been, I checked their video for this season before writing today - they still don’t screen DFKs in any reasonable way.

For those of you looking to learn a little more about the game, you now have one of our dirty little secrets: always add screens to attacking free kicks.

Wrapping Up

I don’t talk about set pieces much because I don’t want to give away edges to competitors of our clients or ruin the value we offer to future customers. That said, even with more awareness around the value of this phase of the game, most clubs place little or no emphasis on it, despite the fact that it's clearly a cheap way to get extra goals and can both help keep clubs up and win them titles.

We’re currently determining our schedule for the winter break and next spring regarding tutelage for coaches and analysis of set pieces for teams. If you work for a professional club who could use help in this area, please get in touch.

Ted Knutson
@mixedknuts
ted@statsbomb.com