With both contenders carrying substantial scoring threat, Elversberg’s home pressure should keep Darmstadt retreating, while the visitors’ vulnerable defense invites further openings. In this high-stakes cup clash, momentum appears poised to produce a lively, goal-filled finish.
Match GoalsOver 2.5
Best Pick
Over 2.5
75%
Correct Score
Top
2-1
8.1%
8.50
1-1
7.6%
9.50
2-2
7.2%
15.00
1-2
6.8%
19.00
3-1
5.7%
11.00
3-2
5.1%
19.00
2-0
4.5%
9.00
2-3
4.3%
41.00
146%
1.48TOP
X21%
4.50
233%
5.75
1X67%
X254%
1279%
Combined Bet Builder
3 legs
Elversberg’s 1.21 first-half xG points to a strong early push, while Darmstadt’s 0.81 suggests less threat before the break. The hosts’ 1.63 full-match xG and Darmstadt’s leaky defence support Elversberg taking control; with combined xG above 3.3, Over 2.5 fits a likely home-win script.
1Match WinnerSV 07 Elversberg
2Match GoalsOver 3.5
3First Half WinnerSV 07 Elversberg
Confirmed XI4-5-1
20
Kristof
26
Sirch
19
Pinckert
3
Joncour
21
Günther
6
Condé
8
Poreba
25
Petkov
17
Schmahl
39
Campbell
9
Futkeu
Impact Player Hot Head
SV 07 Elversberg17 players
49.9/ 99Average
Top Rated
1Francis OnyekaMID66
2Nicholas MickelsonDEF59
3Luca SchnellbacherFWD54
Darmstadt22 players
52.2/ 99Average
Top Rated
1Isac LidbergFWD67
2Patric PfeifferDEF60
3Florian KleinhanslDEF59
Player Rankings
Snipers
Top goal threats
1
Bartosz BialekDarmstadt
59
2
Isac LidbergDarmstadt
54
3
Luca SchnellbacherSV 07 Elversberg
53
4
Milan SmitDarmstadt
49
5
Lukas PetkovSV 07 Elversberg
47
Wall
Top defenders
1
Matej MaglicaDarmstadt
61
2
Patric PfeifferDarmstadt
58
3
Florian KleinhanslDarmstadt
56
4
Aleksandar VukoticDarmstadt
52
5
Luca SirchSV 07 Elversberg
50
Control
Top playmakers
1
Francis OnyekaSV 07 Elversberg
61
2
Kai KlefischDarmstadt
58
3
Carlo SickingerDarmstadt
55
4
Lukasz PorebaSV 07 Elversberg
52
5
Lance DuijvestijnDarmstadt
52
Bad Boys
Most likely to get booked
1
Lukas PinckertSV 07 Elversberg
31
Aggression 53
2
Matej MaglicaDarmstadt
31
Aggression 48
3
Felix KeidelSV 07 Elversberg
33
Aggression 53
4
Jan GyamerahSV 07 Elversberg
37
Aggression 63
5
Nicolas KristofSV 07 Elversberg
50
Aggression 39
Power Rankings
Strongest overall
1
Isac LidbergDarmstadt
67
2
Francis OnyekaSV 07 Elversberg
66
3
Patric PfeifferDarmstadt
60
4
Nicholas MickelsonSV 07 Elversberg
59
5
Florian KleinhanslDarmstadt
59
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for SV 07 Elversberg vs Darmstadt 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 SV 07 Elversberg vs Darmstadt 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 SV 07 Elversberg 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 SV 07 Elversberg vs Darmstadt 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 SV 07 Elversberg 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.