Stuttgart’s attacking pressure should set the match’s tempo, with sustained chances and corner threat likely to force openings. Bremen’s resistance may delay the breakthrough, but the hosts’ expected firepower points toward a lively, high-scoring contest.
Match GoalsOver 2.5
Best Pick
BTTS
61%
Correct Score
Top
1-1
10.2%
9.00
Top
2-1
9.7%
9.00
1-0
8.1%
15.00
2-0
7.7%
13.00
1-2
6.4%
13.00
2-2
6.1%
11.00
3-1
6.1%
13.00
0-1
5.4%
21.00
152%
1.90TOP
X22%
4.10
225%
3.50
1X74%
X247%
1277%
Combined Bet Builder
2 legs
Stuttgart’s strong home chance creation gives this contest a clear route to a goal-rich finish, while Bremen’s weaker attack makes the hosts-or-draw cushion especially appealing. Both defenses can resist, but the attacking momentum points toward an open, dramatic game with Stuttgart poised to avoid defeat as the scoring climbs.
1Match GoalsOver 3.5
2Double ChanceVfB Stuttgart or Draw
Confirmed XI4-5-1
41
Seimen
22
Assignon
29
Jeltsch
24
Chabot
7
Mittelstädt
21
Prömel
47
Spalt
18
Leweling
11
Khannouss
10
Führich
26
Undav
Impact Player Hot Head
VfB Stuttgart31 players
50.7/ 99Average
Top Rated
1Deniz UndavFWD91
2Jeremy ArévaloFWD79
3Jeff ChabotDEF77
SV Werder Bremen34 players
52.4/ 99Average
Top Rated
1Niclas Füllkrug FWD94
2Maximilian WöberDEF93
3Mick SchmetgensDEF72
Player Rankings
Snipers
Top goal threats
1
Jeremy ArévaloVfB Stuttgart
99
2
Niclas Füllkrug SV Werder Bremen
93
3
Deniz UndavVfB Stuttgart
92
4
Leo Sauer VfB Stuttgart
79
5
Ermedin DemirovicVfB Stuttgart
76
Wall
Top defenders
1
Maximilian WöberSV Werder Bremen
99
2
Jeff ChabotVfB Stuttgart
79
3
Niklas StarkSV Werder Bremen
71
4
Julián MalatiniSV Werder Bremen
70
5
Oskar WojcikSV Werder Bremen
65
Control
Top playmakers
1
Angelo StillerVfB Stuttgart
86
2
Romano SchmidSV Werder Bremen
62
3
Chema AndrésVfB Stuttgart
59
4
Jens Stage SV Werder Bremen
59
5
Patrice CovicSV Werder Bremen
59
Bad Boys
Most likely to get booked
1
Chema AndrésVfB Stuttgart
1
Aggression 65
2
Jovan MilosevicVfB Stuttgart
1
Aggression 52
3
Tiago TomásVfB Stuttgart
1
Aggression 75
4
Niklas StarkSV Werder Bremen
1
Aggression 62
5
Ermedin DemirovicVfB Stuttgart
2
Aggression 66
Power Rankings
Strongest overall
1
Niclas Füllkrug SV Werder Bremen
94
2
Maximilian WöberSV Werder Bremen
93
3
Deniz UndavVfB Stuttgart
91
4
Jeremy ArévaloVfB Stuttgart
79
5
Jeff ChabotVfB Stuttgart
77
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for VfB Stuttgart vs SV Werder Bremen based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.
The Bet Builder combines multiple selections from the VfB Stuttgart vs SV Werder Bremen match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.
xG (expected goals) measures the quality of scoring chances. If VfB Stuttgart has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).
Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for VfB Stuttgart vs SV Werder Bremen without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.
A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives VfB Stuttgart a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.
AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.