Leipzig's results look like two different seasons
RB Leipzig have scored 18 goals without conceding in four home Bundesliga matches. In four away fixtures, they have scored just four and conceded eleven. It is an extraordinary early-season contrast, and the expected-goals figures suggest the difference is more than a quirk of finishing.
Yet Leipzig are not the only club whose profile changes with venue. Bayern Munich have maintained strong chance creation in both settings, Dortmund have defended far more effectively at home, and Mainz have generated more attacking xG away.
This analysis uses **72 completed Bundesliga 2026/27 matches** in SmartMatchData's database, all with home and away xG values. The latest completed fixture in this snapshot is dated **1 November 2026 (UTC)**. Every team has played eight league games, although home and away counts are not always equal. These are early-season observations, not opponent-adjusted ratings or forecasts.
Our existing Bundesliga 2026/27 team-form analysis covers the broader league picture. Here the focus is on the specific difference between playing at home and travelling.
RB Leipzig: a dramatic change at both ends
Leipzig have generated **12.53 xG in four home matches**, or **3.13 per game**. They have scored 18 goals from those chances while allowing **4.29 xGA**, approximately **1.07 per match**. Their home goals-against column remains at zero.
Away, Leipzig have produced **6.55 xG in four games**, or **1.64 per match**, and scored only four goals. Their away xGA is **7.11**, approximately **1.78 per match**, while opponents have scored eleven.
The home expected-goal difference is **+8.24**, compared with **−0.56 away**. The underlying balance therefore changes sharply even before accounting for the unusually large gap between goals and xG.
The zero home goals conceded do not mean Leipzig allowed zero danger: opponents generated 4.29 xG in those fixtures. Similarly, the eleven away goals conceded exceed the 7.11 xGA they allowed. Both distinctions matter when separating outcomes from chance prevention.
Bayern Munich: strong attacking numbers in both venues
Bayern Munich have created **15.35 xG across four home matches**, an average of **3.84 per game**, and scored 20 goals. They have allowed only **2.60 xGA**, or **0.65 per match**, while conceding four.
Their away attack is also powerful: **12.80 xG in four games**, equivalent to **3.20 per match**, with seven actual goals scored. Away xGA is **4.42**, or **1.11 per game**, and Bayern have conceded four goals.
Bayern's expected-goal difference remains strongly positive in both settings: **+12.75 at home** and **+8.38 away**. Their chance creation is higher at home, but unlike Leipzig, their away attack still generates more than three expected goals per game in this sample.
Bayern's seven away goals from 12.80 xG show why attacking performance should not be judged solely by the scoreboard. The scoring efficiency guide explains how actual goals can diverge from chance-quality estimates over short runs.
Dortmund: defensive control changes on the road
Borussia Dortmund have allowed **3.53 xGA in four home fixtures**, or **0.88 per match**, conceding two actual goals. Their attack has generated **7.14 xG**, approximately **1.79 per game**, and scored eight.
In four away games, Dortmund have created **5.09 xG**, or **1.27 per match**, and allowed **7.89 xGA**, nearly **1.97 per game**. They have scored six and conceded four away.
Dortmund's away goals conceded are relatively modest, but their opponents have generated much more estimated chance quality than at home. The expected-goal difference shifts from **+3.61 at home** to **−2.80 away**.
That is a meaningful defensive warning sign in the data, although four away matches are not enough to identify a permanent weakness. For why xGA differs from goals conceded, see xG vs xGA Explained.
Mainz: an unusual advantage in away chance balance
Mainz have generated **7.91 xG in four home games**, or **1.98 per match**, while allowing **7.02 xGA**, or **1.76**. Their actual home record in these fixtures includes six goals scored and ten conceded.
Away, Mainz have generated **8.68 xG in four matches**, approximately **2.17 per game**, and scored eleven goals. They have allowed **4.79 xGA**, or **1.20 per match**, conceding seven.
The expected-goal difference is **+0.89 at home** but **+3.89 away**. Mainz have therefore combined higher attacking xG with lower defensive xGA on the road in this snapshot.
Their results offer a useful counterexample to a blanket assumption that Bundesliga clubs necessarily create better chances at home. Opponent strength and match circumstances could explain part of the difference, so the trend needs more fixtures before it becomes a reliable long-term conclusion.
Union Berlin and Werder Bremen: contrasting defensive concerns
Union Berlin have played **five home matches** and **three away**, making raw totals particularly misleading. At home, they have created **6.97 xG**, or **1.39 per game**, and allowed **6.51 xGA**, or **1.30**.
Across three away games, Union have generated only **1.91 xG**, approximately **0.64 per match**, while allowing **8.64 xGA**, or **2.88 per game**. They have scored just one away goal and conceded twelve.
Werder Bremen's change is less extreme but still substantial. In four home matches, they have generated **8.10 xG** and allowed **4.90 xGA**. Away, their four fixtures have produced **5.53 attacking xG** and **8.02 xGA**.
Werder's expected-goal difference moves from **+3.20 at home** to **−2.49 away**. Both clubs have allowed significantly more chance quality per away game, although Union's three-match away sample warrants particular caution.
Freiburg and Hoffenheim show different patterns
SC Freiburg have generated almost identical attacking xG totals in four home and four away matches: **6.86 at home** and **6.93 away**. Their defence, however, has allowed **3.97 xGA at home** compared with **5.74 away**.
That suggests Freiburg's attacking chance creation has been stable across venues, while home chance prevention has been stronger. Their actual scoring is less even: twelve home goals compared with six away.
Hoffenheim's home attack has generated **8.82 xG in four matches**, or **2.21 per game**, compared with **6.31 away**, or **1.58**. But their defensive xGA is lower away: **4.26** versus **6.16 at home**.
These examples show that attacking and defensive venue effects need not move in the same direction. A club may create fewer chances away while also restricting its opponents more effectively.
Why eight games are not enough for a fixed ranking
The current Bundesliga sample contains eight completed league fixtures per club, with some playing three home and five away or the reverse. Dividing totals by matches played is essential, but even per-match averages remain vulnerable to one unusual result.
Penalties, red cards, opponent strength and game state can shift xG significantly. An early zero in the goals-conceded column does not imply zero xGA, and goals above xG should not automatically be treated as repeatable finishing skill.
For a practical approach to these comparisons, see Home and Away Football Statistics and Small Sample Size in Football Statistics.
What the Bundesliga venue splits reveal
Leipzig have displayed the most dramatic contrast among the teams examined: dominant home results and a much weaker away chance balance. Bayern's underlying attack remains strong in both environments. Dortmund's defensive xGA has risen sharply away, while Mainz have created a better chance-quality balance on the road.
Union Berlin's away figures deserve attention, but their sample contains just three matches. Freiburg's attacking xG is notably consistent between venues, illustrating that home advantage does not affect every team in the same way.
These findings describe **72 completed Bundesliga matches**, not probabilities for the next round. The appropriate next step is to monitor whether the differences persist as each club faces more opponents.
Explore the Bundesliga league page, Statistics and Predictions for available match data and separately generated model forecasts.