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
Лучший выбор
ОБЕ ЗАБЬЮТ
54%
Точный счёт
Отличная
1-1
12.4%
8.50
Отличная
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%
Экспресс
3 выборов
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
Lillestrøm30 игроков
50.7/ 99Средний
Лучшие
1Filip Ottosson MID86
2G. NordhFWD83
3Lucas SvenningsenDEF75
Real Sociedad33 игроков
52.7/ 99Средний
Лучшие
1Gonçalo GuedesFWD78
2Mikel GotiMID75
3Ibai Aguirre BasurcoMID71
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Gonçalo GuedesReal Sociedad
99
2
G. NordhLillestrøm
87
3
Orri ÓskarssonReal Sociedad
75
4
Ander BarrenetxeaReal Sociedad
69
5
Kparobo ArierhiLillestrøm
56
Стена
Лучшие защитники
1
Lucas SvenningsenLillestrøm
99
2
Sander Moen FossLillestrøm
90
3
Stian KristiansenLillestrøm
79
4
Espen GarnåsLillestrøm
76
5
Ulrik Yttergård JenssenLillestrøm
73
Контроль
Лучшие плеймейкеры
1
Filip Ottosson Lillestrøm
82
2
Harald WoxenLillestrøm
78
3
Ibai Aguirre BasurcoReal Sociedad
68
4
Mikel GotiReal Sociedad
67
5
Carlos SolerReal Sociedad
64
Хулиганы
Склонны к карточкам
1
Felix VáLillestrøm
1
Агрессия 71
2
Beñat TurrientesReal Sociedad
4
Агрессия 62
3
C. JebaraLillestrøm
7
Агрессия 60
4
Orri ÓskarssonReal Sociedad
11
Агрессия 57
5
Y. IbrahimajLillestrøm
13
Агрессия 40
Рейтинг силы
Сильнейшие в целом
1
Filip Ottosson Lillestrøm
86
2
G. NordhLillestrøm
83
3
Gonçalo GuedesReal Sociedad
78
4
Lucas SvenningsenLillestrøm
75
5
Mikel GotiReal Sociedad
75
Часто задаваемые вопросы
Our Poisson model calculates the probability of every possible scoreline for Lillestrøm 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 Lillestrøm 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 Lillestrøm 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 Lillestrøm 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 Lillestrøm 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.