Tag Archives: Radar charts

Radar Love – Capturing Players in a Single Picture

Comparing football clubs is one thing, comparing football players is yet another. It lies at the heart of many pub debates, where passionate fans try to convince each other that their beloved star is better, often to settle the subtle disagreement by concluding that the players are different. And indeed, different positions, skills and tactical roles make it hard to rank individual players. The first step should be to picture them correctly, and that is where this post will step in.

A lot has been written recently about football analytics and the use of numbers in the beautiful game in general. Some claim it’s an enrichment, some claim it ruins the magic of the game. I don’t see it as such a clear separation. Whether we want it or not, stats are there.

It’s up to each of us to decide for himself how much of it we prefer to add to our football match experience.  And if the analytics community sees anything as its task to lower the threshold for people to start using stats, it should be making stats more accessible. I’m fairly confident that the addition of radar plots will do just that.



‘Standing on the shoulders of giants’ is an apt way to put what I’m doing right here. The conception of many excellent analytical and visualization ideas lies outside football, and radar plots in sports started with basketball, where they appeared in 2009. A few weeks ago, it was @StatsBomb’s own Ted Knutson who introduced them in football. Unsurprisingly, they were quite well received for the many advantages they have.

I’ve given my own twist to the radar plots and I should perhaps mention that the design of these plots is very much a work in progress. Along the way we may decide some elements are missing and others should better be omitted from the chart. For a start, here it is. Click on it for a full-size version.

Radar chart - Dusan Tadic vs Lucas Piazon Eredivisie 2013-14Which better players to give the honor of the first radar plot on 11tegen11 than the two most creative attacking midfielders of the Eredivisie, Twente’s Dusan Tadic and Vitesse’s (or actually Chelsea’s) Lucas Piazón.

My version of the radar plot has nine axes and I’ve spent a considerable amount of time thinking about which parameters to include, as well as how to order the axes. All parameters are presented as per 90 minutes. The decision not to present any actual numbers is a conscious one, as I felt it would distract from the goal of the plot, which is to compare players. If you wish to see the underlying numbers, I’m fairly sure you’ll be able to find them within minutes. The scales of the axes represent the minimum and maximum values found in the league.

Let’s go over the axes one by one.



On top, on the twelve o’clock position is the amount of passes per match. Players who are more involved score higher. I have not yet corrected for total team passes, as I’m unsure whether it provides a true benefit, and what would be the best way to correct for it. Feel free to voice out, as with this concept, there’s no best design yet.

The passing axis is placed in between ‘Incomplete Passes’ and ‘Expected Goals’. The order of the axes is very important, since they determine the surface created for the player. A high score on two, or even three axes leads to a significant area within the plot, creating the image of a high quality player. In this case, it’s combining lots of passes with a low incomplete passes count and a high ExpG.

Usually, more passes contribute to more incomplete passes, and more passes are the domain of players playing further away from the opposing goal. This should provide a balance that gives our radar plot value.


Incomplete passes

We make a counter clockwise trip to the ‘IP’ axis, that stands for ‘Incomplete Passes’. As with all negative traits, this axis is inverted, so that a better performances, in this case less incomplete passes, gives a bigger area on the plot.

Incomplete Passes is flanked by ‘Passes’ and ‘Interceptions’. This should be the area where defensive midfielders excel. Each position in the field should have an area where they can express themselves, otherwise certain positions on the pitch will be underestimated by the plot.



Interceptions are presented as ‘per 400 opposition passes’, as I’ve found raw interceptions per 90 minutes to give too much bias towards players on poor and defensive sides. This correction allows for players on ball possession teams to have a fair shot.

It’s flanked by ‘Incomplete Passes’ and ‘Dribbled by’. The latter represents how often the player is getting dribbled by, which you’d definitely not want for a defensive player. This allows good defensive players a nice area where tidy passers, with intercepting qualities that stand their ground will shine.


Dribbled by

Dribbled by is another inverted axis, as it’s considered better to have less of it. It is flanked by ‘Interceptions’ and ‘Tackles’ and this lower left side is the defensive player’s domain. Expect central defenders and defensive midfielders in this zone.



This is pretty self explanatory really, other than the fact that, like ‘Interceptions’ I like to express it as per 400 opposition passes. It is flanked by ‘Dribbled by’ and ‘Fouls’, since these are two stats you would not like a defending player to have. The fouls axes should provide another balancing act for players making more tackles.



Another self explanatory, and inverted axis. Less fouls, bigger area. It is flanked by ‘Tackles’ and ‘Dribbles’, as I felt is makes the best switch to the offensive player’s side of the chart.



Dribbles gets its own axis so that offensive players get enough room to shine. Also, I think it’s an under appreciated domain in stat use in general, where players add a dimension of unpredictability to the team. A good dribbler provides a threat that influences the style of defense of the opposition. It is flanked by ‘Fouls’ and ‘Expected Assists’.

Particularly the link with ExpA is a valuable one, since it allows wide players to express themselves in this lower right hand part of the chart.



On a team basis, this may be one of the most important axes, yet on an individual player basis it should just be one of nine. Expected Assists represents the passes leading to a goal scoring attempt, where each of those attempts is weighed according to the odds to score from it.

ExpA is flanked by ‘Dribbles’ to give attention to players that should be hard to defend against: those players with enough skill to dribble and to deliver the final ball. Also, it is flanked by ‘Expected Goals’, to allow players with multiple offensive dimensions to claim a bigger part of the chart.



Expected Goals is the final axes of our circle. There’s hardly a need for explaining this terms anymore. Suffice to say it represents all goal scoring chances a player takes, which are weighted according to the odds to score from it.

ExpG is flanked by ‘Expected Assists’ and ‘Passes’. The latter connection is very powerful and opposition would never want a goal scorer to see a lot of the ball, so those goal scorers that do just that should be rewarded with a bigger piece of the chart.


In the end

This concludes our trip around the chart. In my view, it provides a fair balance between different elements of the game, and the ordering of the axis makes it difficult to claim a lot of ‘area’ without having serious underlying qualities. This balancing act also ensures that I will use the same chart layout for all players, so that learning to use them is as straightforward as possible.

Some of you may notice that traditional metrics like ‘Goals’ and ‘Assists’ are missing. My recent work on the ‘unrepeatability’ of scoring once the quality of the goal scoring attempt has been corrected for, leads me to believe that both ‘Goals’ and ‘Assists’ are inferior to ‘Expected Goals’ and ‘Expected Assists’. Scoring or assisting without the underlying ExpG or ExpA won’t last, so why credit a player for doing it. Or, to use a quote that is mostly linked to Jonathan Wilson, the writer who inspired me to football blogging in the first place, “goals are overrated.

I’ll leave you with some bonus charts.

Radar chart - Daley Blind vs Felipe Gutierrez Eredivisie 2013-14The two best defensive midfielders of the Eredivisie! You can see Blind gets the nod in the defensive department of tackles, dribbled by and interceptions. Gutierrez is a bit more tidy in his passing, but that’s probably related to making less passes overall. Blind is more of an assisting threat, while Gutierrez gets a tiny advantage in terms of goal scoring.

Radar chart - Jeffrey Bruma vs Joel Veltman Eredivisie 2013-14Two young Dutch center backs. Ajax’ Joël Veltman does better on nearly every single axis compared to PSV’s Jeffrey Bruma. It’s Veltman’s passing accuracy that could be improved on.

Radar chart - Memphis Depay vs Viktor Fischer Eredivisie 2013-14Another Ajax v PSV meeting, with players playing in the same left wing position, but in very different interpretations of that role. Depay is much better in assisting and scoring, whereas Fisher gets the nod in dribbling, passing tidiness and interceptions. Both players add a significant amount of ‘area inside the plot’ with their dribbling skill, which is why I put this chart up. I feel it’s important to recognize that element of the game.

Radar chart - Graziano Pelle vs Luc Castaignos Eredivisie 2013-14Two players who are more similar that I would have expected. Both Pellè and Castaignos do little else both contributing to ExpG and ExpA, where the Feyenoord striker puts in an unreal amount of goal scoring threat. He touches the border of the chart, so no player beats him in the ExpG category.