Over 2.5 looks the logical play: the sides combine for 2.56 expected goals, while Lillestrom’s defense is 27% weaker than average and Real Sociedad bring a potent attack rated 43% above average. Even with Lillestrom’s attack less productive, Sociedad’s quality and the hosts’ defensive vulnerability point to an open European contest with goals likely.
Match GoalsOver 2.5
Best Pick
BTTS
54%
Correct Score
Top
1-1
12.4%
8.50
Top
0-1
10%
8.50
1-0
8.9%
17.00
1-2
8.7%
8.50
2-1
7.7%
17.00
0-0
7.2%
15.00
0-2
7%
8.00
2-0
5.5%
29.00
133%
5.50
X26%
4.50
241%
1.53TOP
1X59%
X267%
1274%
Combined Bet Builder
3 legs
Real Sociedad’s stronger attack (+43%) and 1.40 xG give them the edge, while 6.29 expected corners point to sustained pressure. Lillestrom’s 27%-weaker-than-average defence looks vulnerable, yet their 1.16 xG offers a route to score—supporting a Sociedad win, over 2.5 goals and both teams to score.
1Match WinnerReal Sociedad
2Match GoalsOver 2.5
3Both Teams to ScoreYes
Confirmed XI4-4-2
12
Dahlberg
2
Ranger
28
Gabrielsen
47
Kristiansen
26
Paintsil
29
Ibrahimaj
21
Ottosson
14
Nyheim
20
Vá
22
Bassi
8
Gulbrandsen
Impact Player Hot Head
Lillestrom30 players
50.7/ 99Average
Top Rated
1Filip Ottosson MID86
2G. NordhFWD83
3Lucas SvenningsenDEF75
Real Sociedad33 players
52.7/ 99Average
Top Rated
1Gonçalo GuedesFWD78
2Mikel GotiMID75
3Ibai Aguirre BasurcoMID71
Player Rankings
Snipers
Top goal threats
1
Gonçalo GuedesReal Sociedad
99
2
G. NordhLillestrom
87
3
Orri ÓskarssonReal Sociedad
75
4
Ander BarrenetxeaReal Sociedad
69
5
Kparobo ArierhiLillestrom
56
Wall
Top defenders
1
Lucas SvenningsenLillestrom
99
2
Sander Moen FossLillestrom
90
3
Stian KristiansenLillestrom
79
4
Espen GarnåsLillestrom
76
5
Ulrik Yttergård JenssenLillestrom
73
Control
Top playmakers
1
Filip Ottosson Lillestrom
82
2
Harald WoxenLillestrom
78
3
Ibai Aguirre BasurcoReal Sociedad
68
4
Mikel GotiReal Sociedad
67
5
Carlos SolerReal Sociedad
64
Bad Boys
Most likely to get booked
1
Felix VáLillestrom
1
Aggression 71
2
Beñat TurrientesReal Sociedad
4
Aggression 62
3
C. JebaraLillestrom
7
Aggression 60
4
Orri ÓskarssonReal Sociedad
11
Aggression 57
5
Y. IbrahimajLillestrom
13
Aggression 40
Power Rankings
Strongest overall
1
Filip Ottosson Lillestrom
86
2
G. NordhLillestrom
83
3
Gonçalo GuedesReal Sociedad
78
4
Lucas SvenningsenLillestrom
75
5
Mikel GotiReal Sociedad
75
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Lillestrom vs Real Sociedad 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 Real Sociedad 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 Real Sociedad 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.