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Reading those pesky football radars

They’re all over Twitter — but are they reliable? Accurate? Useful?

If you have spent 12 seconds on football Twitter, you’ve probably seen one of these:

These radars are quite common, and a great tool for assessing a player’s ability. They’ve become the preeminent statistical analysis snapshot of choice over the last couple of years, but do fans really know how to read them?

Their prevalence has reached levels that I think are not equivalent to the general understanding of what these radars actually mean. So, I want to take a look at exactly what a radar like this says (and doesn’t say), to help those who might not have a ton of experience with these tools better comprehend what they really tell us.

I want to use this Ings radar as an example, largely because it’s pretty hilarious — but also because it illustrates a couple of important points. Let’s go step by step through the process of reading one of these bad boys.

  1. Make sure you know what player it’s for, the season it’s from, and the club the player was playing for during the period. You’ll sometimes see radars that cover multiple seasons or a single season with multiple teams — these can obviously provide longer-term data, but there is a risk of muddying the numbers because the player may have been in multiple different systems in that time.
  2. If a table that lists the player’s raw data from the period in question is made available, definitely take a look at it before trying to parse the radar itself. Primarily, you’re going to want to answer a simple question — does this radar cover enough minutes played to be statistically relevant? In this case, you see Ings has only played 2.3 90s, which isn’t much — though in his Statsbomb piece that used this radar, Mike Goodman is pretty explicit in acknowledging that this is a very early season take.
  3. Next, look at the template used to create the radar.

Okay, at this point, we need to stop and explain exactly what these radars represent statistically. The radar is a visual representation of how the player rates among similar players in the statistics on the radar.

So, let’s use Ings as an example. In the Shots category, the radar is filled up all the way out to the end of the circle — at 2.8. This means that Ings averages at least 2.8 shots per 90 minutes, putting him in the very highest echelon in this category for the players in the comparison group.

In fact, the table at the bottom of the graphic indicates that Ings is averaging 4.75 shots per 90, so he’s blowing the competition out of the water in that category.

Before we focus on the template though, let’s clarify one more time what the visual representation of this data means. The radar shows how well a player compares in the given statistic relative to other players in the pre-defined comparison group.

The closer to the outside of the circle the colored area is for a statistic, the better that player stacks up in that category relative to his peers. So, Ings is doing comparatively very well in shots and touches in the box, but less so in passing percentage and fouls won.

With that cleared up, let’s talk about how the template fits into this picture.

The template determines two things:

  1. The statistics chosen to make up the radar. We aren’t really interested in how many tackles a striker has or what a center-back’s xA is, so the (smarter than me) folks who design these things pick the stats they’ve found to be most reflective of a player’s overall ability at a position.
  2. The group of players that the player’s statistics are compared to. Saying that a striker has a higher xG per 90 than 80% of all players isn’t very instructive — you’d expect a striker to generate more chances than pretty much all center-backs and holding midfielders. Saying that a striker has a higher xG per 90 than 80% of all strikers is a very different proposition. That would indicate you might be looking at a top-tier player.

At the open of this piece, I said that I wanted to use the Ings radar as an example because it illustrated a couple of important points about reading radars overall. The issue of sample size was one, but even more pressing is the issue of the selected template.

Earlier this summer, Statsbomb made some changes to the way it presents data, including splitting the old forward/attacking midfielder template into two separate templates — striker and winger/attacking midfielder.

This Ings radar is on the attacking midfielder/winger template, while Ings has played pretty much exclusively as an out-and-out striker this season.

To me, this could invalidate the radar to a significant extent. I’m not particularly interested in how Ings’ xG per shot or touches in the box compare to attacking midfielders and wingers — he’s a true striker, and you’d expect him to be superior in those statistics, even if he wasn’t playing particularly well.

In the post I pulled the radar from, Goodman provides plenty of other evidence of Ings’ promising start, so I’m not looking to create an actual counter-argument to that conclusion. But, that particular radar, in isolation, could be quite misleading — and that’s one of the major things you’ll need to look out for when looking at these things.

With that very long sidebar complete, let’s go back to the steps of reading a radar.

4. If the aspects discussed in points 1-3 above seem sensible, it’s time to actually look at the radar itself! In what category does the player compare well to his peers? In what categories is he struggling?

5. The last part is the most difficult — and the hardest to simply explain as part of a process. If you really want to make sense of a radar, you need to also consider why a player might be good in one area or bad in another. The answer isn’t always simply “this player isn’t good at this thing.”

Let’s use an example to clarify this.

Here’s an Ousmane Dembele radar from his 2017/18 season at Barcelona.

His shots and xG are pretty low for a player of his caliber, but let’s think about that for a second. He’s playing on a Barcelona team with Lionel Messi, Luis Suarez, and Philippe Coutinho — the first two among the most gifted goal scorers in the world, the third a frequent taker of long shots.

For Barcelona to be successful, does Dembele need to be shooting and scoring? Probably not.

This radar tells us he’s getting on the ball in dangerous areas (touches in the box) and moving the ball efficiently (passing percentage) and effectively (xA) when he does. His xA is among the very best in La Liga, so there really isn’t any cause for concern related to his xG, as long as he keeps feeding Messi and Suarez and the South American duo keeps scoring.

Radars are a fun, visually appealing, and informative way to understand footballers. Ted Knutson and Statsbomb deserve tremendous credit for developing this way to talk about the sport, and accounts like @FussballRadars have done well to build on that work as well.

But the devil’s really in the detail when it comes to these things, and as I hope I’ve laid out here, you’ve got to understand all the small details before you can make sense of the radar visual itself.