Bundesliga Through the Prism of xT Metric: The Phenomenal Wirtz and Bynoe-Gittens. Bayern’s Growing Problems. Mainz’s Misfortune.
In previous articles, we explored the Premier League and La Liga. Today, it’s the Bundesliga’s turn. As primary analytical tool, we will once again utilize the xT metric. First, we will look at individual assessments and player ratings, then move on to the team level and analyze their performance in terms of the threat posed over the current season.
For those who missed the previous analyses, let’s briefly recap that the xT metric allows us to assess the change in the probability of scoring a goal as the ball moves from one area of the football field to another.
If the xG metric helps us evaluate the efficiency of final actions in attack, xT opens up the opportunity to assess actions such as passes and carries. Thus, xT complements the analyst’s toolkit and provides a broader perspective for evaluating the gameplay.
Let’s look at the Top 25 Bundesliga players in terms of the effectiveness of moments created through passes and carries.
Bayer 04 is represented by 8 players:
- Florian Wirtz — occupies the first place in the overall ranking and shares the top spot in the league’s best passers ranking by xT with his teammate Granit Xhaka. Currently, he has scored 5 goals and made 10 assists, being the second-highest assist provider in the league.
- Jeremie Frimpong — the second-best dribbler in the Bundesliga in terms of danger created through ball carries. He has accumulated 13 points by goal plus assist. There is a lot of information about the significant interest from Premier League clubs in the young winger, with ongoing speculation about moves to Manchester United, Arsenal, and Liverpool.
- Granit Xhaka — ranks first in efficiency for moments created through passes.
- Exequiel Palacios — ranks fourth among the best passers in the league. Together with Xhaka and Wirtz, they take three of the top four spots.
- Also ranked are: Amine Adli, Edmond Tapsoba, Josip Stanisic, and Victor Boniface — Bayer’s current top scorer with 10 goals.
Bayern Munich also has 8 players represented:
- Kingsley Coman — third in the league in carrying efficiency and sixth in passing, which collectively places him second in the overall xT ranking. He will be unavailable to help the Munich team for the next 2–3 months. A significant loss for Bayern.
- Leroy Sane — ranks fifth overall and is the Bundesliga’s top assist provider with 11 goal assists.
- Mathis Tel — the 18-year-old Frenchman ranks fifth among the best dribblers and closes the top ten in the overall ranking.
- Joshua Kimmich — third in the ranking for best passers with 0.4 xT on average per game.
- Other Bayern players in the ranking include: Jamal Musiala, Thomas Müller, Raphaël Guerreiro, and Noussair Mazraoui.
Borussia Dortmund is represented by 2 players:
- Bynoe-Gittens — the England youth team winger with 15 games and 4 points according to the goal plus assist ranks third overall and is the undisputed leader in danger created through carrying for players with more than 500 minutes of playtime in the league.
- Donyell Malen — ranks eighth in xT through carries.
Stuttgart is also represented by 2 players:
- Chris Führich — fourth in the overall ranking, demonstrates almost an ideal balance in the distribution of created danger between passing and carrying. In the current Bundesliga season, the German has scored 6 goals and provided 7 assists.
- Silas Wamangituka — ranks ninth in danger created through ball carries.
Bochum is represented in the ranking by Kevin Stöger, who shows quite a high xT through passes — 0.38 on average per game, placing him fifth in the respective best passers ranking.
Mainz — the team whose performance deviates most from the expected values by xG in the current season, is represented in the xT ranking by Brajan Gruda. The young midfielder, only 19 years old, stands out among his teammates with a high 0.34 xT through ball carries, ranking fourth in the respective category.
RB Leipzig is represented by only one player. Xavi Simons — the best assistant of RB in the current season with 7 assists and 6 goals.
The ranking also includes one player each from Werder and Wolfsburg.
The same ranking can be transferred to two axes to get a more visual picture of how the considered players are distributed relative to the league averages and other footballers.
In this chart, each point represents a Bundesliga player in terms of his performance by xT. The Top 25 players from the earlier discussed ranking are highlighted in bold. Names are labeled for the Top 10 and for players who, in one of the metrics, have lower values compared to the league averages.
In similar charts for the Premier League and La Liga, it could be observed that the overwhelming majority of the Top 25 players were concentrated in the upper right quadrant and colored green.
For the Bundesliga, 6 players out of the Top 25 in terms of danger created through ball carries find themselves slightly below the league average. In the Premier League, there were two such players, and in La Liga — one. This is partly explained by the fact that in the German championship, the average xT value for carries is the highest among the leagues considered, equating to 0.088 xT per 90 minutes. In other words, in Germany, players on average create more danger through ball carries than in the Premier League and La Liga, for which the averages are 0.084 and 0.08, respectively.
Also, I would like to highlight Wirtz again and note that his assessments are quite balanced. In similar rankings for the Premier League and La Liga, players who accumulate most of their xT through carrying dominate.
Comparing the German’s values with the best players from England and Spain, he ranks third with 0.77 xT after Doku and Vinicius with 1.12 and 0.79 xT per 90 minutes, respectively. Moreover, Florian typically plays as a central attacking midfielder with some leaning towards the left half-flank, unlike Doku and Vinicius, who create the main threat directly from the flanks.
Among players of a similar role, Wirtz demonstrates phenomenal statistics. Florian is usually compared positionally with Musiala and Xavi Simons. However, if one tries to broaden the list of potential candidates slightly and find players most similar in the frequency of successful passes and ball carries in various areas of the football field, the following picture emerges.
Below are activity maps for the German and 9 Bundesliga players who are closest to Florian’s pattern of action (here, only successful passes and ball carries are considered).
Closest in “style” to Florian turns out to be Xavi Simons, who, though he makes it into the Top 25, loses quite significantly in xT to the German — creating 40% less threat on average per game.
The other players fall short even more significantly. It’s evident that not only attacking midfielders but also nominal forwards turn out to be similar in style by cosine distance. The method used to assess style proximity is quite primitive, however, it allows for the identification of similar patterns.
Below is a ranking that corresponds to consideration of only the horizontal axis of the chart with the average values. Here, the best footballers of the German championship are ranked by the danger created through passes.
Once again, we note Bayer’s dominance in the ability to efficiently move the ball into areas with an increased probability of a goal. Xhaka and Wirtz have a slight advantage of ~0.05 xT relative to the rest of the leader group, which is distributed quite densely.
We also highlight Stöger, who has reached a quite high fifth place with Bochum, which is on the brink of the relegation zone.
If players are separately placed on the vertical axis of the same chart, it results in a ranking of the best footballers by the danger created through ball carries.
Bynoe-Gittens confidently leads the Bundesliga and is in fifth place in Europe by this indicator. The Englishman, as expected, falls behind Doku and Vinicius with their stellar 0.84 and 0.61 xT for carries. However, his lag behind the next in line, Martinelli (0.47) and Rodrigo (0.46) from Real Madrid, is quite conditional. One or two unsuccessful games by one of the players, and the distribution in this trio could change.
Now, let’s consider the performance of players by xT from another angle. We’ll examine the map of the best football players by xT in relation to specific areas of the football field.
Before analyzing, it should be stated that this visualization contains areas prone to high instability in terms of the probability of scoring from them. These can easily be identified by paying attention to the average number of successful actions a player makes from a given zone, which ultimately contributes to their xT score.
If you see action values significantly below 1, this indicates that a very small number of actions were required to enter this zone. Typically, there are two such zones — the central part near one’s own goal and the opponent’s goal.
For the zone near the opponent’s goal, this is explained by the fact that players often take shots in it and less frequently improve their position by moving the ball into an area with an even higher probability of scoring. In the example of La Liga in the previous article, we looked at the Top 5 contenders for entering this zone and the passes they made, which earned them high xT values.
Typically, players perform 2–5 successful actions to become the best for a given area. Omar Marmoush did four — 2 passes and 2 carries.
For the zone near one’s own goal, the reason is different. Goalkeepers are excluded from our ranking, so we only consider outfield players, who average significantly fewer actions from this area. It’s also noteworthy that the area in the initial third has very similar xT values for each of the cells, plus the xT values for the goalkeeper’s area are slightly higher than the adjacent zones.
As a result, a few successful long passes from this zone or carrying the ball into a more dangerous area can make one the best in xT. Xhaka made 3 effective actions to take first place in the respective zone.
Below for clarity, I’ve included the transition matrix, based on which all xT scores in my articles are calculated. This map can help better understand the mechanics of calculating the xT metric and the existing limitations described above.
Now, let’s dive directly into the analysis.
In the visualization presented, 7 zones are attributed to Bayer and Bayern, collectively accounting for almost 50% of the entire field for the used zone breakdown.
Stuttgart is present in three zones, with Chris Führich almost entirely occupying a large part of the left flank on the opponent’s half. Below is an action map that earned him the corresponding xT scores. On the left are all actions with xT > 0, in the middle are filtered actions only from zones where the German was the best, and on the right are the most dangerous passes and carries, each exceeding 0.03 xT.
Eintracht also occupies three zones. Two on their penalty line and one in the central part of the opponent’s penalty area.
Two zones are occupied by the flank defenders of Heidenheim and Mainz. Below is a map of passes and carries for Tim Siersleben.
It is evident that the German creates the most xT through medium and long vertical passes to the flank and half-flank area.
For comparison, here’s a similar picture for Fernandes. Edimilson can play multiple roles on the field. The Swiss footballer can act both in defense (central/right defender) and in a defensive midfield role, performing the duties of a number six.
The map below visually demonstrates how his zone of active actions on the field differs from the work performed by Siersleben. However, Fernandes makes it into our ranking specifically because of his activity on the right defensive flank, from where he creates the most danger in the league by xT per 90 minutes of playtime.
It can also be noted that key actions by xT are on passes directed towards the area in front of the opponent’s penalty box.
Additionally, two zones are occupied by Werder players — Anthony Jung and Niklas Schmidt.
RB Leipzig is represented by Klostermann, who effectively finds partners in the opponent’s penalty area through medium and long passes.
For comparison, let’s look at the passing patterns of Xhaka and Edmond Tapsoba, who create the most danger in the league from adjacent central field zones.
It’s apparent that Xhaka serves as a dispatcher, changing the directions of attacks. However, he periodically makes progressive passes into the penalty area, for which he receives high xT values.
Tapsoba, in the considered trio of players, performs the most actions, accumulating his 0.084 xT per match predominantly through short passes to adjacent zones.
I’d also like to take a closer look at the two best players in terms of ball carrying. In the original ranking, a softer filter of 500 played minutes is used. This is what allowed Bynoe-Gittens to be included. The Englishman has 647 minutes excluding added time and does not pass the stricter filter for the map with zones, which uses a limit of 900 minutes to operate with players having at least 40% of the maximum possible playing time on the field.
If the filter is softened, then the Englishman would oust Chris Führich and take first place for one of the zones on the left flank with a total xT = 0.217 against 0.205 for the German. Below is an action map of Bynoe-Gittens, which clearly shows how carries predominate over passes.
Below is an action map for Frimpong, who creates more danger from the right flank than any other player in the championship when considering any field zone. The Dutch footballer averages 0.56 xT per game, with a significant portion of the danger generated from the highlighted zone — 0.233 xT, making about nine effective actions on average per match.
Finally, let’s consider a similar visualization for Wirtz. The zone from which the German creates more dangerous moments than others is located on the left half-flank directly in front of the opponent’s penalty area. In the image below, you can see how Florian moves the ball into the penalty area.
Above, we evaluated integral assessments for the entire season over the past 24 rounds. However, a similar estimation over a shorter period for the last few matches may present separate interest.
Below for comparison are two images. The first shows the distribution of players by total score throughout the season (previously discussed). The second shows the distribution for the last 7 rounds, starting from 2024.
It’s immediately noticeable that almost all Bayern Munich players have disappeared from the ranking, except for Thomas Müller. The new map shows total dominance by Dortmund. Only four players transitioned from the old map to the new one — Xhaka in the central part of the field, Führich and Frimpong on the flanks, and Paco from Eintracht in the penalty area on their own half.
An analysis of the xT dynamics at the team level might offer answers. Below for the Top 5 clubs in the Bundesliga, we consider the change in xT gained at the opponent’s goal versus xT allowed at their own goal over the course of the current season.
It can be observed that both Bayern Munich and Bayer Leverkusen show a trend of decreasing total moments created through xT in the last 5–6 rounds.
Additionally, let’s examine a similar picture for the xG metric from Opta to assess the dynamics of chances teams create throughout the season directly through shots on goal and compare it with the observed picture for xT.
This chart is an average of fact values with a moving window of 3 matches, a standard technique in time series data analysis.
Bayer Leverkusen created a difference in xG > 1 for most of the season. However, in the last 7 rounds (starting in January), the team shows a decrease in efficiency in actions at the opponent’s goal compared to the performance they reached in December. Consequently, xG drops almost by one unit.
Bayern Munich also sees a decline in xG. However, the picture indicates a long stable trend, which in January was compounded by problems with allowing moments at their own goal — an increase in xGC. The fall of the Munich team is significant — in the last 13 rounds, they dropped from a mark of more than 2.5 goals in xGD to a level corresponding to a difference of less than one goal.
Thus, both metrics point to issues with creation and realization for Bayern Munich, which in turn is reflected in the absence of players in the updated zonal map.
Borussia Dortmund, judging by the dynamics of both metrics, has emerged from the crisis. Dortmund has reduced the number of moments created at their own goal by xT by 2.5 times and by xG by 3 times. Edin Terzic’s team, also like Stuttgart, shows a stable level of moments by xT over the last 8 rounds, but holds slightly higher in absolute terms.
For Stuttgart, accumulating problems with defensive efficiency are observed. By xG and xT, the team shows a minimal positive difference between expected goals scored and conceded. According to the charts, this is due to an increase in metric values at their own goal while maintaining offensive efficiency at the previous level.
Now, let’s consider all Bundesliga teams sorted by the average danger created by xT per match.
This ranking, similar to the previous ones, can also be transferred to two axes.
Bayern Munich falls behind Bayer Leverkusen in xT for carries, earned by moving the ball into more dangerous areas near the opponent’s goal.
RB Leipzig creates less than the league average through carries, falling behind Mainz, which is in 17th place.
Comparing this distribution of teams with how teams are distributed by xT in the Premier League and La Liga, a sharp difference can be identified. The performance of the majority of clubs is concentrated closely in the lower left quadrant.
Calculating the distance between the center of the resulting cluster and the center of the cluster of leading groups, the difference obtained should be somewhat larger compared to the leagues previously considered. Such an assessment can be used as an indicator of the degree of imbalance in the level of clubs in the league.
Below is another ranking, this time by the difference in xT at their own and opponents’ goals.
For a more convenient analysis of the results obtained and their comparison with the real positions of teams in the league table, the following summary information is provided:
- R — current position in the league table
- xTD_R — position by the difference in xT (how much more danger a team creates at the opponent’s goal through passes and carries)
- xGD_R — position by the difference in xG (how much more moments a team creates after shots)
- R — xTD_R — deviation of the ranking by xT from the actual position in the championship
- R — xGD_R — deviation of the ranking by xG from the actual position in the championship
It can be observed that:
- At the top of the league table, the ranking based on xTD aligns better with the actual team positions.
- Mainz shows a strong deviation from its “expected” positions in the hypothetical rankings for both metrics.
- Heidenheim is the opposite of Mainz. According to the rankings of both metrics, the team should be in the lower part of the table, but in reality, they are in the top block of teams.
Cases of strong asymmetry between positions in the rankings by xTD and xGD can also be highlighted:
- Köln has +6 from its actual place by the xT difference. This indicates that, compared to other teams of similar performance, the team move the ball well into dangerous areas but cannot convert moments into high chances shots (the place by xG aligns well with the real one). It would be premature to draw further conclusions purely on numbers. Further analysis should undoubtedly be supplemented by actual match viewing.
- The anomaly with Freiburg can perhaps be explained more easily. The team has scored the most penalties in the Bundesliga, which also constitute a significant part of all goals scored — 25%. This characteristic is immediately reflected in both metrics. For a penalty, a player and the team receive high base 0.79 xG. However, this assessment is not taken into account in xT.
The table previously considered is further expanded below. I did not immediately present this version to facilitate easier and sequential reading of the information. Its main purpose is to reinforce conclusions and assumptions made earlier based on the assessment of actual efficiency demonstrated by the difference in goals scored — GD.
First and foremost, we turn our attention to Mainz. The team is underperforming by 0.76 in goal difference per match, the worst indicator in Germany at the moment.
Next, we examine Heidenheim, which ranks third in the league behind Bayer Leverkusen and Eintracht Frankfurt in overperforming their actual results relative to expectations. The team shows a difference in goals scored and conceded that is 0.5 goals higher than expected according to the xG model from Opta (available on fbref.com).
A crucial observation is the negative discrepancy between actual performance by xGD and the real statistics of goals scored and conceded. Typically, teams from the top part of the league table have a higher actual realization compared to model estimates, explained by the fact that the model estimate is averaged.
Looking at all the top 5 leagues, only three clubs show the opposite picture:
- Barcelona falls short by nearly 0.3 to the expected xGD.
- Bayern Munich and Stuttgart are underperforming by approximately 0.1 in xGD.
This fact could be viewed as an additional marker reflecting issues with Bayern Munich at the current point in the championship.
P.S. I plan to publish the details of the visualization construction from the article on GitHub soon. Stay tuned.
