With combined xG of 3.45, this Champions League clash has the attacking quality to clear 2.5 goals. Bodø/Glimt’s attack is firing at +58%, while Dortmund bring a solid +24% threat; both sides should find chances despite strong defensive ratings. Qualification pressure adds urgency, and the tempo appears set for goals.
Match GoalsOver 2.5
Best Pick
Over 2.5
66%
Correct Score
Top
2-1
9.7%
12.00
Top
1-1
9%
8.50
2-0
7.8%
21.00
1-0
7.2%
19.00
3-1
7%
21.00
2-2
6%
11.00
1-2
5.6%
9.50
3-0
5.6%
34.00
158%
3.00TOP
X20%
4.10
221%
2.10
1X78%
X241%
1279%
Combined Bet Builder
5 legs
Bodø/Glimt’s 1.70 xG and Dortmund’s 1.76 point to an open game, supporting over 2.5 goals and both teams to score. Their 1.77 combined first-half xG favors an early goal, while 9.31 expected corners backs over 8.5; Dortmund’s attack also gives Guirassy a strong anytime-scoring case.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3First Half Over/UnderOver 0.5
4Corners Over/UnderOver 8.5
5GoalscorersAnytime
Confirmed XI4-3-3
12
Haikin
20
Sjøvold
6
Gundersen
2
Nielsen
15
Bjørkan
8
Auklend
7
Berg
19
Fet
11
Blomberg
21
Helmersen
10
Hauge
Impact Player Hot Head
Bodø/Glimt26 players
55.6/ 99Strong
Top Rated
1Andreas HelmersenFWD96
2Mikkel Bro HansenFWD87
3Mathias JørgensenFWD81
Borussia Dortmund25 players
51.8/ 99Average
Top Rated
1Felix NmechaMID74
2Serhou GuirassyFWD72
3Waldemar AntonDEF66
Player Rankings
Snipers
Top goal threats
1
Mathias JørgensenBodø/Glimt
99
2
Mikkel Bro HansenBodø/Glimt
99
3
Andreas HelmersenBodø/Glimt
98
4
Ola BrynhildsenBodø/Glimt
87
5
Serhou GuirassyBorussia Dortmund
70
Wall
Top defenders
1
Villads NielsenBodø/Glimt
69
2
Julian RyersonBorussia Dortmund
59
3
Emre Can Borussia Dortmund
55
4
Haitam AleesamiBodø/Glimt
54
5
Waldemar AntonBorussia Dortmund
52
Control
Top playmakers
1
Patrick BergBodø/Glimt
73
2
Joey VeermanBorussia Dortmund
72
3
Jobe BellinghamBorussia Dortmund
63
4
Konstantinos KaretsasBorussia Dortmund
60
5
Felix NmechaBorussia Dortmund
57
Bad Boys
Most likely to get booked
1
Luca ReggianiBorussia Dortmund
1
Aggression 58
2
Samuele InacioBorussia Dortmund
1
Aggression 78
3
Jens Petter HaugeBodø/Glimt
11
Aggression 42
4
Carney ChukwuemekaBorussia Dortmund
15
Aggression 72
5
Joey VeermanBorussia Dortmund
20
Aggression 81
Power Rankings
Strongest overall
1
Andreas HelmersenBodø/Glimt
96
2
Mikkel Bro HansenBodø/Glimt
87
3
Mathias JørgensenBodø/Glimt
81
4
Ola BrynhildsenBodø/Glimt
78
5
Felix NmechaBorussia Dortmund
74
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Bodø/Glimt vs Borussia Dortmund 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 Bodø/Glimt vs Borussia Dortmund 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 Bodø/Glimt 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 Bodø/Glimt vs Borussia Dortmund 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 Bodø/Glimt 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.