Brann’s sharper attack should test a Lillestrøm defense prone to cracks, while the hosts carry enough attacking threat to answer rather than retreat. With both sides offering routes to goal, the match appears set to open up, making over two and a half the compelling expectation.
Match GoalsOver 2.5
Best Pick
BTTS
62%
Correct Score
Top
1-1
10.7%
8.00
Top
2-1
8.8%
9.50
1-2
7.9%
11.00
1-0
7.3%
13.00
2-2
6.5%
11.00
0-1
6.5%
15.00
2-0
6%
15.00
3-1
4.8%
15.00
142%
2.20TOP
X24%
3.70
234%
2.75
1X66%
X258%
1276%
Combined Bet Builder
3 legs
Lillestrom’s 1.69 xG at home gives them a strong platform to edge Brann, while the visitors’ 1.51 xG and Lillestrom’s leaky defence point toward an open game above 2.5 goals. In that scoring script, Thomas Olsen is a credible anytime threat as Lillestrom’s attack finds a way to deliver the win.
1Match WinnerLillestrom
2Match GoalsOver 2.5
3GoalscorersAnytime
Confirmed XI4-5-1
12
Dahlberg
3
Ottesen
5
Foss
47
Kristiansen
11
Elkær
29
Ibrahimaj
21
Ottosson
16
Melland
17
Kitolano
20
Vá
10
Olsen
Impact Player Hot Head
Lillestrom32 players
51.9/ 99Average
Top Rated
1Filip Ottosson MID93
2G. NordhFWD91
3Lucas SvenningsenDEF75
Brann37 players
48.3/ 99Average
Top Rated
1Bård FinneFWD77
2Fredrik Pallesen Knudsen DEF73
3Lars RemmemMID71
Player Rankings
Snipers
Top goal threats
1
G. NordhLillestrom
99
2
Bård FinneBrann
74
3
Kparobo ArierhiLillestrom
64
4
Thomas Lehne OlsenLillestrom
59
5
Noah HolmBrann
57
Wall
Top defenders
1
Lucas SvenningsenLillestrom
99
2
Cheikh MbackeBrann
85
3
Sander Moen FossLillestrom
78
4
Stian KristiansenLillestrom
69
5
Espen GarnåsLillestrom
66
Control
Top playmakers
1
Filip Ottosson Lillestrom
94
2
Harald WoxenLillestrom
89
3
Jacob Lungi SørensenBrann
87
4
Lars RemmemBrann
63
5
Kevin KrygårdLillestrom
62
Bad Boys
Most likely to get booked
1
Felix VáLillestrom
1
Aggression 71
2
Neraysho KasanwirjoBrann
1
Aggression 46
3
Jón Dagur ThorsteinssonBrann
1
Aggression 57
4
C. JebaraLillestrom
7
Aggression 60
5
Thore PedersenBrann
7
Aggression 20
Power Rankings
Strongest overall
1
Filip Ottosson Lillestrom
93
2
G. NordhLillestrom
91
3
Bård FinneBrann
77
4
Lucas SvenningsenLillestrom
75
5
Fredrik Pallesen Knudsen Brann
73
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Lillestrom vs Brann 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 Lillestrom vs Brann 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 Lillestrom 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 Lillestrom vs Brann 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 Lillestrom 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.