Over 2.5 looks the most logical outcome: Crvena Zvezda’s attack is rated +62% above average, while Copenhagen’s is +34%, giving both sides clear scoring potential. Copenhagen’s defense is 1% weaker than average, and the hosts’ stronger defense may slow the start, but the combined attacking threat should open the game toward three goals.
Match GoalsOver 2.5
Best Pick
Home Win
63%
Correct Score
Top
2-0
10.3%
12.00
Top
2-1
9.9%
9.00
1-0
9.9%
10.00
1-1
9.5%
7.00
3-0
7.2%
21.00
3-1
6.9%
17.00
2-2
4.8%
12.00
0-0
4.7%
13.00
163%
2.25TOP
X20%
3.60
217%
2.90
1X83%
X237%
1280%
Combined Bet Builder
3 legs
Crvena Zvezda’s 1.57 xG and +62% attack power point to sustained home threat, while Copenhagen’s 1.35 xG gives the match a clear route to three goals. Zvezda’s defense is 34% stronger than average, strengthening the home-win case; with Arnautović leading the line, he looks a strong anytime-scorer option.
1Match WinnerCrvena Zvezda
2Match GoalsOver 2.5
3GoalscorersAnytime
Confirmed XI3-6-1
1
Matheus
45
Gudelj
3
Nakayama
23
Ristic
60
Gashtarov
20
Händel
21
Elsnik
33
Krunic
22
Kostov
37
Lucic
9
Dieng
Impact Player Hot Head
Crvena Zvezda48 players
53.0/ 99Average
Top Rated
1Mahmudu BajoMID86
2J. EnemFWD76
3Marko ArnautovicFWD76
FC Copenhagen33 players
50.9/ 99Average
Top Rated
1Asger Sörensen DEF72
2Munashe GaranangaDEF72
3Youssoufa MoukokoFWD71
Player Rankings
Snipers
Top goal threats
1
Youssoufa MoukokoFC Copenhagen
99
2
Bruno DuarteCrvena Zvezda
82
3
J. EnemCrvena Zvezda
82
4
Marko ArnautovicCrvena Zvezda
72
5
Maher Carrizo FC Copenhagen
65
Wall
Top defenders
1
Mihailo RisticCrvena Zvezda
74
2
Munashe GaranangaFC Copenhagen
74
3
Stefan GudeljCrvena Zvezda
72
4
Gabriel PereiraFC Copenhagen
64
5
Leon IvanovicCrvena Zvezda
63
Control
Top playmakers
1
Mohammed Abu FaniCrvena Zvezda
86
2
Mahmudu BajoCrvena Zvezda
80
3
Timi Max ElsnikCrvena Zvezda
67
4
Rade KrunicCrvena Zvezda
66
5
Tomás HändelCrvena Zvezda
66
Bad Boys
Most likely to get booked
1
Matej StrikaCrvena Zvezda
1
Aggression 52
2
Mohammed Abu FaniCrvena Zvezda
1
Aggression 26
3
ZankaFC Copenhagen
1
Aggression 35
4
Stefan LekovićCrvena Zvezda
4
Aggression 59
5
Amir RichardsonFC Copenhagen
7
Aggression 80
Power Rankings
Strongest overall
1
Mahmudu BajoCrvena Zvezda
86
2
J. EnemCrvena Zvezda
76
3
Marko ArnautovicCrvena Zvezda
76
4
Asger Sörensen FC Copenhagen
72
5
Munashe GaranangaFC Copenhagen
72
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Crvena Zvezda vs FC Copenhagen 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 Crvena Zvezda vs FC Copenhagen 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 Crvena Zvezda 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 Crvena Zvezda vs FC Copenhagen 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 Crvena Zvezda 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.