In soccer, there are legendary coaches who have asserted that upon losing the ball, teams that regain it within five, six, or even eight seconds have a higher chance of keeping the ball, and indeed, scoring. This is the foundation of what Jurgen Klopp called “Gegenpressing” and led to the rise of the RB Leipzig team in the Bundesliga, whose coach Ralf Rangnick stated that goals were most often scored within eight seconds of winning the ball from the opposition. This seems like an amazing statistic, but is it data driven or is it merely legend?
In the 2021 paper “Data-driven detection of counterpressing in professional football” (link) the authors, Pascal Bauer and Gabriel Anzer, describe a method for using supervised machine learning to detect counterpressing in video. If automated detection was possible then they hoped to be able to better evaluate some of these counterpressing rules of thumb.
History of the Research into Pressing Tactics
Much of the data behind the counterpressing strategies started with a man named Charles Reep. He was one of the first who studied the game for the purpose of collecting data that might be able to reveal new insights. He captured piles and piles of data — many of these in hand-written notes — to better understand the game. There is much that can be said about Charles, but this is too short of a post to discuss his successes and miscues. To our question about transition successes, however, in one paper that he authored in 1968 he found that 30% of the time that a team forced a transition and gained possession they were able to make a shot on goal and indeed, 25% of all goals came from regained possessions in the attacking quarter. This data wasn’t much used outside of Reep’s circle but in 1999 A.J. Grant collected data from the 1998 World Cup and confirmed these numbers. This relationship between transition “recaptures” and goals has been confirmed in papers from 2014 and 2018 as well. There have also been studies that learned that teams relied on counterpressing more often when behind in the score than when ahead. This would indicate to me that some teams know of the power of counterpressing, but don’t structure their main strategy around it, much like the press in basketball. Additionally, studies have discovered that teams that recover the ball more quickly after losing it tend to win more games. All of these things seem intuitive, but it’s helpful to see that there are measurements and data behind the notions.
The paper concludes a few things that I find valuable.
- First off, researchers have been able to discern counterpressing strategies using machine learning. This is very important, because it reduces the labor required to classify significant events and approaches in soccer.
- Using these automated detection methods, these same researchers also found that counterpressing is more likely be successful near the sidelines and that numerical superiority near the ball when it is turned over increases the chance of winning it back. Both of these, of course, makes good sense to me.
- Within the German Bundesliga, teams follow very different transition strategies and these differences could be detected by the machine learning. Each of these approaches had different levels of success regarding turnover recovery and goal scoring.
- Successful teams—measured against their final ranking—tend to use the counterpressing strategy more efficiently, providing credibility to the coaches that use it as a major offensive counter-attacking strategy.
Conclusion
Though there seems to be data that ties a fast recovery during transition with a higher probabiity of actually scoring, I was actually unable to find any data that actually quantified the number of seconds after a turnover where a transition was more likely to lead to a goal. Perhaps the number is somewhere in the mountains of tablets where Charles Reep recorded his data, or perhaps its just legend. But the data seems very clear that pursuing a counterpressing strategy with players who are highly fit and can fly all over the field (people like Tyler Adams??) allows teams to have a higher probability of scoring in games than teams who do not. Of course if Lionel Messi plays for the non-counterpressing team, all bets are off.
LINKS to Other Soccer Analytics Entries
- Soccer Analytics Series Intro
- MLS and Premier League Comparison
- Home and Away Luck Metric
- Does Counterpressing Work? Evidence.
- Evaluation of Outcomes using the Luck Metric
- More Analysis using the Luck Metric
- Soccer Analytics in Practice – Youth Soccer Example
- xG and Luck update on recent MLS season
