Darmstadt’s solid attacking edge should set an assertive tone, while St Pauli’s forward threat adds danger at both ends. With each side carrying credible scoring intent, an open contest appears likely to clear the threshold.
Match GoalsOver 2.5
Best Pick
BTTS
58%
Correct Score
Top
1-1
10.9%
7.50
Top
2-1
9.7%
11.00
1-0
9.1%
11.00
2-0
8.1%
17.00
1-2
6.5%
9.50
0-1
6.1%
10.00
2-2
5.8%
13.00
3-1
5.7%
21.00
151%
2.75TOP
X23%
3.60
226%
2.20
1X74%
X249%
1277%
Combined Bet Builder
5 legs
Darmstadt’s 1.59 xG and St Pauli’s 1.13 support both teams to score and an over-2.5 contest; combined first-half xG of 1.59 points to an early goal. Their 10.18 projected corners clear 8.5, while Martijn Kaars is the anytime scorer pick as the game opens up.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3First Half Over/UnderOver 0.5
4Corners Over/UnderOver 8.5
5GoalscorersAnytime
Confirmed XI3-5-2
1
Schuhen
6
Pfeiffer
20
Vukotic
18
Petretta
25
Mikelbrencis
41
Duijvestijn
17
Klefisch
16
Akiyama
5
Richter
9
Lee
36
Smit
Impact Player Hot Head
Darmstadt33 players
49.7/ 99Average
Top Rated
1Yosuke FurukawaFWD84
2Matej MaglicaDEF78
3Lance DuijvestijnMID66
FC St Pauli24 players
53.6/ 99Average
Top Rated
1Branimir Hrgota MID76
2Marcus MathisenDEF72
3Abdoulie CeesayFWD70
Player Rankings
Snipers
Top goal threats
1
Yosuke FurukawaDarmstadt
85
2
Ricky-Jade Jones FC St Pauli
74
3
Abdoulie CeesayFC St Pauli
67
4
Martijn KaarsFC St Pauli
63
5
Isac LidbergDarmstadt
57
Wall
Top defenders
1
Aleksandar VukoticDarmstadt
84
2
Matej MaglicaDarmstadt
78
3
Patric PfeifferDarmstadt
76
4
Yannik LührsDarmstadt
76
5
Marcus MathisenFC St Pauli
67
Control
Top playmakers
1
Mathias RasmussenFC St Pauli
67
2
Erik AhlstrandFC St Pauli
65
3
Joel Chima FujitaFC St Pauli
65
4
Marco RichterDarmstadt
62
5
Lance DuijvestijnDarmstadt
59
Bad Boys
Most likely to get booked
1
Aleksandar VukoticDarmstadt
1
Aggression 54
2
Merveille PapelaDarmstadt
1
Aggression 53
3
Ricky-Jade Jones FC St Pauli
10
Aggression 47
4
T. AndoFC St Pauli
15
Aggression 54
5
Milan SmitDarmstadt
22
Aggression 58
Power Rankings
Strongest overall
1
Yosuke FurukawaDarmstadt
84
2
Matej MaglicaDarmstadt
78
3
Branimir Hrgota FC St Pauli
76
4
Marcus MathisenFC St Pauli
72
5
Abdoulie CeesayFC St Pauli
70
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Darmstadt vs FC St Pauli 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 Darmstadt vs FC St Pauli 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 Darmstadt 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 Darmstadt vs FC St Pauli 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 Darmstadt 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.