Both attacks may lack polish, but porous defenses and a lively chance-creation profile point toward an open contest. Union’s home pressure and Cologne’s threat should expose enough space for goals to carry the match beyond the cautious start.
Match GoalsOver 2.5
Лучший выбор
ОБЕ ЗАБЬЮТ
60%
Точный счёт
Отличная
1-1
10.8%
7.00
Отличная
2-1
9.4%
10.00
1-0
8.3%
11.00
2-0
7.3%
15.00
1-2
7%
10.00
0-1
6.2%
11.00
2-2
6.1%
12.00
3-1
5.5%
19.00
148%
2.50TOP
X23%
3.60
229%
2.60
1X71%
X252%
1277%
Экспресс
2 выборов
Union’s home advantage gives them the steadier platform, while both sides’ porous defending leaves the door open to a lively, chance-filled contest. Their attacking records may be modest, but the combined threat should produce goals; Union look best placed to avoid defeat, making the home-or-draw and over-goals combination the compelling script.
1Match GoalsOver 2.5
2Double Chance1. FC Union Berlin or Draw
Confirmed XI3-5-2
1
Rönnow
4
Bosch
14
Querfeld
15
Rothe
19
Haberer
13
Schäfer
8
Khedira
20
Aebischer
21
Skarke
11
Jeong
9
Burcu
Impact Player Hot Head
FC Union Berlin30 игроков
48.1/ 99Средний
Лучшие
1Marvin FriedrichDEF86
2Dmytro BogdanovFWD83
3Leopold QuerfeldDEF82
FC Köln25 игроков
48.1/ 99Средний
Лучшие
1Luca WaldschmidtFWD78
2Rav van den BergDEF66
3Thijs DallingaFWD66
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Dmytro BogdanovFC Union Berlin
99
2
Luca WaldschmidtFC Köln
99
3
Thijs DallingaFC Köln
70
4
Andrej IlicFC Union Berlin
58
5
Ragnar AcheFC Köln
58
Стена
Лучшие защитники
1
Marvin FriedrichFC Union Berlin
99
2
Leopold QuerfeldFC Union Berlin
76
3
Felix UduokhaiFC Union Berlin
71
4
Stanley NsokiFC Union Berlin
68
5
Jahmai Simpson-Pusey FC Köln
64
Контроль
Лучшие плеймейкеры
1
Aljoscha Kemlein FC Union Berlin
67
2
Ellyes Skhiri FC Köln
58
3
Ísak JóhannessonFC Köln
55
4
Kastriot ImeriFC Union Berlin
54
5
Janik HabererFC Union Berlin
53
Хулиганы
Склонны к карточкам
1
Janik HabererFC Union Berlin
1
Агрессия 68
2
Livan BurcuFC Union Berlin
1
Агрессия 53
3
Oliver BurkeFC Union Berlin
1
Агрессия 26
4
Tim SkarkeFC Union Berlin
1
Агрессия 54
5
Jahmai Simpson-Pusey FC Köln
1
Агрессия 21
Рейтинг силы
Сильнейшие в целом
1
Marvin FriedrichFC Union Berlin
86
2
Dmytro BogdanovFC Union Berlin
83
3
Leopold QuerfeldFC Union Berlin
82
4
Luca WaldschmidtFC Köln
78
5
Rav van den BergFC Köln
66
Часто задаваемые вопросы
Our Poisson model calculates the probability of every possible scoreline for FC Union Berlin vs FC Köln 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 FC Union Berlin vs FC Köln 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 FC Union Berlin 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 FC Union Berlin vs FC Köln 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 FC Union Berlin 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.