Augsburg’s home pressure should expose a Union Berlin defense that has looked vulnerable, while both sides’ chance creation points toward an open contest. Even subdued attacks may find openings, and the game appears poised to break beyond a cautious scoreline.
Match GoalsOver 2.5
Best Pick
BTTS
59%
Correct Score
Top
1-1
10.5%
8.00
Top
2-1
9.7%
8.50
1-0
8.6%
11.00
2-0
7.9%
11.00
1-2
6.4%
13.00
3-1
6%
13.00
2-2
5.9%
12.00
0-1
5.7%
17.00
152%
1.85TOP
X23%
3.90
225%
4.00
1X75%
X248%
1277%
Combined Bet Builder
5 legs
Augsburg’s 1.92 xG and 0.99 first-half xG point to early pressure, while Union’s defence is 16% weaker than average—supporting over 2.5 goals and both teams to score. Their combined 10.80 xCorners also favours over 8.5; Gregoritsch is a credible anytime scorer in a lively contest.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3First Half Over/UnderOver 0.5
4Corners Over/UnderOver 8.5
5GoalscorersAnytime
Confirmed XI4-5-1
1
Dahmen
35
Bah
5
Matsima
2
Brackelmann
16
Behrens
27
Wolf
4
Massengo
32
Rieder
19
Fellhauer
21
Ibrahimovic
9
Ribeiro
Impact Player Hot Head
FC Augsburg24 players
49.9/ 99Average
Top Rated
1Calvin BrackelmannDEF72
2Fabian RiederMID64
3Anton KadeMID63
1. FC Union Berlin30 players
48.4/ 99Average
Top Rated
1Marvin FriedrichDEF86
2Leopold QuerfeldDEF83
3Dmytro BogdanovFWD83
Player Rankings
Snipers
Top goal threats
1
Dmytro Bogdanov1. FC Union Berlin
99
2
Rodrigo RibeiroFC Augsburg
68
3
Michael GregoritschFC Augsburg
67
4
Uchenna OgunduFC Augsburg
61
5
Andrej Ilic1. FC Union Berlin
59
Wall
Top defenders
1
Marvin Friedrich1. FC Union Berlin
99
2
Leopold Querfeld1. FC Union Berlin
75
3
Chrislain MatsimaFC Augsburg
70
4
Felix Uduokhai1. FC Union Berlin
68
5
Stanley Nsoki1. FC Union Berlin
67
Control
Top playmakers
1
Aljoscha Kemlein 1. FC Union Berlin
66
2
Arijon IbrahimovicFC Augsburg
65
3
Fabian RiederFC Augsburg
60
4
Janik Haberer1. FC Union Berlin
55
5
Kastriot Imeri1. FC Union Berlin
54
Bad Boys
Most likely to get booked
1
Chrislain MatsimaFC Augsburg
1
Aggression 26
2
Han-Noah MassengoFC Augsburg
1
Aggression 38
3
Kristijan JakicFC Augsburg
1
Aggression 51
4
Michael GregoritschFC Augsburg
1
Aggression 21
5
Rodrigo RibeiroFC Augsburg
1
Aggression 51
Power Rankings
Strongest overall
1
Marvin Friedrich1. FC Union Berlin
86
2
Leopold Querfeld1. FC Union Berlin
83
3
Dmytro Bogdanov1. FC Union Berlin
83
4
Calvin BrackelmannFC Augsburg
72
5
András Schäfer1. FC Union Berlin
67
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for FC Augsburg vs 1. FC Union Berlin 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 FC Augsburg vs 1. FC Union Berlin 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 FC Augsburg 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 FC Augsburg vs 1. FC Union Berlin 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 FC Augsburg 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.