On Saturday, Bundesliga surprise package and 2nd place Borussia Mönchengladbach travelled to the Rhein-Necker Arena to play against 6th place TSG Hoffenheim. On the surface, this match was a bit of a dud, as the two teams played out a 0-0 draw. However, soccer analytics folks were amazed by the nature of this. TSG Hoffenheim finished the match with an expected goals total of 3.4, while Mönchengladbach finished with 0.6.

Image Credits: Michael Caley (@MC_of_A)

For those that are unfamiliar with how expected goals works, each shot (whether it ends up as a goal, the goalie saves it, or it misses the net and goes into row Z) is given a value between 0 and 1 on whether the shot “should” have gone in. This value is based on a number of factors, but the key ones are a) the x and y coordinates from where the ball was shot, b) whether the shot was taken with the stronger foot, weaker foot or with the head and c) what the previous action was (cross, dribble, pass etc.). This metric is generally used to measure the “quality of chances” that a team had during a match, but is also widely used in the soccer analytics community to determine if a team are over or underperforming.



This Hoffenheim result piqued my interest because I had never seen a team create so many good chances to score, but end up empty-handed. As a result, I decided to measure what the most improbable match in (recent) history has been.



To do this, I used Understat XG data from matches in the six major European Leagues (England, Spain, Germany, Italy, Russia, France) from the 2014-15 season until November 24th of this season. For each match, I took the expected goal value of each shot, and simulated each shot 10,000 times to determine a distribution for the number of goals a team should have scored in a match, given the opportunities in a match. For example, if Barcelona defeated Real Madrid 2-0, I calculated the probability that Barcelona scored exactly 2 goals, and the probability that Real Madrid scored exactly 0 goals. I then multiplied these 2 probabilities together to determine “how likely” the eventual final score was.



Without further ado, let’s take a look at the most unlikely matches across Europe since the 2014/15 season.



Results:



Top 20 Most Unlikely Matches:

Top 15 Most Unlikely Premier League Matches:

Top 10 Most Unlikely La Liga Matches:

Top 10 Most Unlikely Bundesliga Matches:

Top 10 Most Unlikely Series A Matches:

In addition, given that we were originally motivated by looking at the likelihood of 0-0 matches, I figured I’d show the 10 most “unlikely” 0-0’s, to see where the Hoffenheim-Gladbach match would shape up.



10 Most Unlikely 0-0’s:

Based on this, there is a very good chance that the Hoffenheim match from this weekend was the most unlikely 0-0 since 2014/15.



Before concluding, there are a couple of potential flaws with this methodology. The biggest one is the assumption that all the shots are independent. This is not entirely true, as it is often the case that the chances a team gets are dependent on the current score of the matches. The other major potential flaw with this analysis is that own goals are given 0 XG, but still contribute to the final result.



Anyways, I hope you all enjoyed reading this article, and some of these matches brought back some fun memories.



If you have any questions about this article, please feel free to reach out to Andrew on Twitter @andrew_puopolo or reach out to him via email at andrewpuopolo@college.harvard.edu

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