We live in fortunate times. As football fans we’ve got all sorts of information about our stars at just a mouse click away. Any moment in any day can be filled with watching football, reading about football, or checking football stats.
How different were things we things when I fell in love with football. In the summer of ’86, when I was nearly eight years old, I studied players’ clubs, birth dates and positions, simply because that was all my Panini album had to offer. I tricked my parents into letting me watch some first halves, despite kick-off times far beyond my usual bedtime.
What I lacked in information, I compensated in fantasy. I created my own truth about Andoni Zubizarreta, the Spanish goalkeeper with that magnificent surname and that fascinating look in his eyes. And about Diego Armando Maradona, of whom I knew little else than Lanus, 30-10-1960, Napoli.
Some thirty years later, things are so very different. My constant hunger that made even the most basic stats taste good, has been traded for a stats overload that makes it hard to get a sense of what’s really going on.
In this age of information overload, value lies in cropping data to bite-size proportions, without losing its relevance. In assessing a football player, one might be able to scan through some individual stats like non penalty goals, key passes, dribbles and dispossessions. But after three players, I’m kind of full.
Comparing a league full of players just by looking at stats is an impossible task for the human mind. However, comparing different shapes is a task we are – by evolution – much better at. So, back in January, when I saw Ted Knutson’s magnificent work on player radars, the fun in individual player stats was back, immediately!
Looking at shapes, one can easily get a feel for strikers that offer great link-up play, or midfielders that offer little else besides defensive protection. The radars offer a fantastic link between player traits and cold stats.
Just like the ExpG model, the player radars on 11tegen11 have had a huge summer upgrade. I’ve decided to apply nearly the same format that Ted uses on StatsBomb. The reason behind this is quite simple: I think radars have a huge potential in opening up the stats world to a big audience. When each analyst uses their own radar versions, wider adaptation will be slowed down a lot. We shouldn’t niggle about subtle differences when it’s clearly better to just step over those details and show the world what we can do.
Like Ted described in one of his introductory pieces on StatsBomb, there are different templates for different positions.
– AM/FWD for strikers, wide attackers in front threes, and the three men band in a 4-2-3-1.
– CM/DM for central and defensive midfielders
– Fullback for eehhh… fullbacks.
Goalkeepers and central defenders don’t have templates yet, since we don’t exactly know what stats to judge them by.
The outside boundaries of the chart represent 95% percentiles to prevent players like Messi and Ronaldo from dwarving the rest. The inside boundaries, likewise, represent the 5% percentiles. Negative axes, like fouls, are inverted so that bigger coloured areas are always indicative of better performances. As a reference database, to compile the axis limits, I’ve used the 2012/13 and 2013/14 seasons of the top-5 leagues (EPL, Bundesliga, La Liga, Serie A and Ligue 1).
For a nice example of the CM/DM template, meet midfielder Kamohelo Mokotjo, recently transferred to Twente, but leading PEC Zwolle to last season’s Cup victory.
Mokotjo tore the Eredivisie apart last season. Without reaching the 95% mark in any category, he scored high on nearly every axis, while playing nearly almost three quarters of all possible minutes. If we would scout for spectacular stats in certain categories, chances are we’d miss Mokotjo. There’s not one thing he does so good that he reaches the outer boundary that is the 95% percentile. It’s the combination of doing everything very good that makes him a fantastic player.
It’s pretty obvious from this radar to see that Mokotjo had a magnificent season, but wouldn’t it be great if we could somehow quantify player radars?
Well, the good news is, we can.
I’ve computed the surface of Mokotjo’s radar and compared it to the same database that I’ve used to find the size of the axes. The surface of Mokotjo’s radar is compared to the reference database, and value is scaled on 0 to 5 stars.
To find a central midfielder with a radar surface this large is very rare, so Mokotjo is awarded the full 5 out of 5 gold stars. It’s like Football Manager’s player ranking system brought to real life. Small caveat: it’s easier to score high stats in the Eredivisie than in the EPL. One day, when we’ve learned how stats translate between leagues, we may know how to adjust for league differences.
Knowing how good a player’s season was is one thing, it’s another thing to know something about potential. This time, look at 17-year old new Ajax signing Richairo Zivkovic.
Playing for Groningen, Zivkovic had a hugely impressive debut season, for a 17 year old. His performance in front of goal was elite, his performance in terms of passing, dribbling and defensive contribution was, well, nearly absent.
In terms of radar surface, Zivkovic earned 3.5 stars, and should he add more passing to his game, or more dribbling, or some defensive work, he should rise in terms of stars. Still, for a 17-year old, this was an elite season. Now, how to put this ‘ for-a-17-year-old’ thing in our stars ranking?
Well, quite simple actually, by comparing a player to his peers, rather than to the full reference database.
The silver stars compare a player not to all other players, but only to players of the same age, or younger. There are just a handful of 17 year olds in the database, so I’ve set the lower limit to 18. When comparing Zivkovic to other forwards aged 18 years or younger, he turned in an elite performance, earning him 5 out of 5 silver stars. So, there we’ve got Football Manager’s second star rating: potential!
In the end
I’ve only just finished scripting these new player radars, but I still find myself playing around with them. Finally, we’ve got a tool that makes individual player statistics fun.
We’ll use them a lot this season, both on this site and on Twitter. The radars now allow for true player scouting, both in terms of actual quality, and in terms of future potential.