Quantifying ‘Gegenpressing’

For a moment, I thought I’d call this post ‘Just how weird a team do we think Bayern is?’. A catchy title helps to draw in a crowd, which you can then try and sell a smart point about footy analytics. Indeed, we will touch upon Bayern and see just how weird they are, but this post probably still won’t be one for the masses. We’ll dive into two advanced defensive metrics that have been set out in the analytics community recently and combine them to identify different defensive team styles.

 

Gegenpressing

Gegenpressing, or counter pressing, refers to what teams do when they don’t have the ball.

One extreme would be to run back to their penalty box and park the bus there, wait for the opponent to arrive and try to limit space, and thereby the amount of damage done. This may results in quite a few shots conceded, but the idea would be to limit the quality of those attempts and thereby limit the odds of conceding.

The other extreme would be to put aggressive pressure on the opposing player in possession of the ball, and try and knick it from him before any decent offensive move can be started. It will result in a lower amount of shots conceded, but once the opponent gets into scoring position, most defenders will probably be out of place, and high quality attempts could arise.

To quantify this, we will use two advanced defensive metrics, put out originally by Gerry Gelade and Colin Trainor. Beware of some acronyms now.

 

Average Defensive Distance – ADD

This metric computes the average distance up the pitch where a team performs its defensive actions. For all tackles (failed and completed), interceptions and fouls we use the distance between event and the goal line of the defending team. The average of all of those is the Average Defensive Distance. Teams that perform their defensive actions high up the pitch (i.e. far away from their own goal), have a high ADD, those that defend primarily close to their own goal have a low ADD.

In his original definition, Gerry only included own half events, but I’ve not applied this selection. I believe this limitation won’t change the outcome all that much, and it’s probably just easier to reproduce including all defensive actions.

The highest ADD in the dataset for the 2013-14 season (Brazil, Bundesliga, EPL, Eredivisie, La Liga, Ligue 1, Mexico, MLS, Russia, Championship and A-League) is Bayern’s 46.7. This means that Bayern makes its defensive actions just over 17% further away from goal than the average team does. The average ADD is 39.9.

The lowest ADD in the dataset is Crystal Palace, who under Tony Pulis had an ADD of just 36.6. On an average pitch of say 100 meters, this mean Palace defends 3.3 meters deeper than the average team, and 10.1 meters deeper than Bayern.

Obviously, not all of this metric represents a conscious tactical choice. Poor teams will generally be playing more in their own half, as their superior opponents lay their will on them. Therefore, this metric needs to be interpreted with care, and in the light of team strength. The extremes like Bayern and Palace are easy, but the less extreme ADD’s are more difficult to interpret. I tend to think of it more as representing a certain style, and not so much as a performance metric.

 

Passes allowed Per Defensive Action – PPDA

The second metric we’ll use is Colin Trainor’s Passes allowed per Defensive Action. Again, getting used to the acronym probably takes more time than understanding the metric, as it’s quite straightforward actually.

To compute PPDA we divide the amount of passes that a team allows (i.e. passes that the opponent attempts), and divide that number by the amount of defensive actions made. By convenience, we compute this metric over the passes and defensive actions made at least 40 meters from the goal line (OPTA’s 40 coordinate on the x-axis). Colin has explained the reasoning behind this choice very well, so I’ll just refer to his original work here.

Teams that sit back and allow their opponent possession of the ball in their own half and around the halfway line, will note a high PPDA. Lots of opponent passes will be divided by a low number of defensive actions that far from the own goal line. In reverse, teams that aggressively pressure their opponent will note a low PPDA. A low amount of opponent passes will be divided by a high number of defensive actions high up the pitch.

The lowest PPDA (i.e. the highest amount of pressure) in the dataset is again noted by Bayern at 6.9. So, for every 6.9 passes that their opponents make in that zone further than 40 meters from the Bayern goal, Bayern make one defensive action.

The highest PPDA (i.e. the lowest amount of pressure) in the dataset is noted by Mexico’s Atlante for 16.6. Within the top-5 leagues, the highest PPDA was for… Crystal Palace.

I hear you thinking. Doesn’t this mean that ADD and PPDA are essentially the same thing?

 

Combining ADD and PPDA

Both metrics share common ground, but I’d make the case they are different enough to be valuable. What’s more, they can bring even more insight when combined. ADD tells you where teams performed their defense, PPDA tells you how much defense away from goal they performed. ADD is how high their defensive line was, PPDA is how intense the press was.

In part, both go hand in hand. Generally, teams that play high defensive lines also use intense press (Bayern), and teams that play deep defensive lines use low press (Palace). The regression line runs in inverse direction.

The R-squared is just 0.46 though, so there is significant variation: teams play high defensive lines with only moderate pressure (Twente), teams that play low defensive lines with high pressure (Cruzeiro), teams that play very deep with moderately low pressure (Queretaro, Montreal), teams that play average defensive lines without defensive pressure (Morelia, Lorient). Oh, and don’t forget to note Bayern stretching the plot in the upper left corner with their absurdly high line and intense press.

There is a lot of work to be done with these metrics. We’ll need to check repeatability (but from face value this should be okay), study teams that get a new manager (to separate player effects from tactical choices), assess potential league effect (cultural differences in defending style), etc.

For now, I’ll leave you with the big plot, where teams further than 1.5 standard deviation from the regression line have been tagged. Click for the full version.

Advanced defensive metrics by team - new layout

19 thoughts on “Quantifying ‘Gegenpressing’

  1. mpv

    Searching for a way to quantify pressing seems like a great idea to me 🙂

    So far, your attempt seems to apply to pressing in general, though. It does not apply to gegenpressing in particular, contrary to what the title claims.

    The description “what teams do when they don’t have the ball” describes pressing in general, too. Gegenpressing. by contrast, means that, if a team has just lost the ball, they try to take it back immediately. I.e. gegenpressing means applying intense pressure during the transition period just after having lost the ball. It is pressing under special circumstances, so to speak.

    Reply
  2. ameeruszkai

    Clicked on the graph but can still only see the same teams on it as before I clicked it. Would be interested to know who the dots in the bottom right corner represent?
    Great article though, as always.

    Reply
    1. 11tegen11 Post author

      Perhaps I should have said ‘high res version’ rather than ‘full version’.
      I’ll try and include a link to a version with more team names later.

      Reply
  3. Parth Swaroop

    Would dividing into quadrant would make it more effective read?
    And if we could assign a different color for different league it can be a good comparative study for pressing metric.

    Reply
    1. 11tegen11 Post author

      Yeah, quadrants would be easy to add in if most people would want that.

      League differences are better shown in a different graph altogether, like a box plot. I’ve saved that exploration for a follow-up post. Seeing how well this one is received, I’ll put it out here soon!

      Reply
  4. Roo

    Was going to point out the same thing as mvp, Gegenpressing is about what you do during transition.

    Still an informative and very interesting article. Out of curiousity, was the choice of leagues determined by anything else than who would/could provide the relevant datasets?

    Reply
  5. Till

    I really like the idea of analyzing managerial changes, particularly during the season. How did the numbers change for example for Schalke with the move from Keller to Di Mateo? Also how does this correlate to distance covered by players? In Germany, Dortmund is notorious for running a lot and pressing, whereas Guardiola just recently emphasized more playing, less running. Two thoughts of many… Thanks a lot, great analysis!

    Reply
  6. Rai

    Hi,

    Really enjoyed the article. I just have two basic questions.

    1) Is the grey bordering the regression line a type of a funnel plot, and if yes, why did you use it?

    2) How do you calculate a team being 1.5 standard deviations away from the regression line? I’m familiar with the Gaussian distribution, but I’ve never encountered it being applied in such a way.

    Reply
    1. 11tegen11 Post author

      Thanks!

      You’re correct, it is a kind of funnel plot. The grey zone shows the area one standard deviation from the non linear regression line. I chose non linear regression because that would make the best fit the the data on ADD and PPDA, and there are enough data points to do so. In hindsight, a 2 SD zone could’ve done too (the famous 95% confidence interval), but that’s for next time.
      I’ve created this plot with R, using the package ggplot2. The regression was done with geom_smooth, a part of ggplot2.

      Reply
  7. Brian

    Agree with mpv and Roo that Gegenpressing is rather limited to the defensive transition phase. And it’s one of several options how to behave in this phase rather then the overall category.

    Regardless of this classification/definition issue, great analysis and good read. I’d also be very interested in seeing more teams (particularly more of the big ones like Barcelona and Real – and how are the weirdos Rayo and Swansea doing) and comparision of league vs. league.

    Reply
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