Over 8.5 corners looks the logical call: AEK win corners very often, backed by a strong +33% attack edge, while Shakhtar also create corners frequently with +12% power. Shakhtar’s tougher corner defence is a hurdle, but AEK concede occasionally, giving both sides routes to the flag. In a high-stakes Champions League clash, attacking pressure should keep the corner count climbing.
Corners Over/UnderOver 8.5
Best Pick
Over 2.5
67%
Correct Score
Top
2-1
9.6%
10.00
Top
1-1
8.7%
6.50
2-0
7.9%
13.00
3-1
7.2%
21.00
1-0
7.1%
9.50
2-2
5.9%
13.00
3-0
5.8%
26.00
1-2
5.3%
10.00
160%
1.67TOP
X20%
5.03
220%
4.94
1X80%
X240%
1280%
Combined Bet Builder
3 legs
Shakhtar’s strong +61% attack power gives the hosts a clear route to goals, while AEK’s +24% attack threat supports the Over 1.5. Their combined 8.20 xCorners (4.41 + 3.79) points toward a busy corner count, and Shakhtar or Draw looks the logical cover at home.
1Double ChanceFC Shakhtar Donetsk or Draw
2Match GoalsOver 1.5
3Corners Over/UnderOver 7.5
Confirmed XI4-5-1
31
Riznyk
17
Tobias
5
Bondar
22
Matvienko
13
Henrique
27
Ocheretko
30
Alisson
6
Gomes
10
Pedrinho
77
Mendoza
19
Elias
Impact Player Hot Head
FC Shakhtar Donetsk34 players
52.5/ 99Average
Top Rated
1Kauã EliasFWD83
2Gleiker MendozaFWD72
3Dmytro RiznykGK69
AEK26 players
52.3/ 99Average
Top Rated
1Petros Mantalos MID82
2Zini FWD71
3Răzvan MarinMID70
Player Rankings
Snipers
Top goal threats
1
Kauã EliasFC Shakhtar Donetsk
87
2
Gleiker MendozaFC Shakhtar Donetsk
83
3
Zini AEK
71
4
Luca MeirellesFC Shakhtar Donetsk
68
5
Luka JovićAEK
67
Wall
Top defenders
1
Filipe RelvasAEK
70
2
Diego Arroyo FC Shakhtar Donetsk
67
3
Domagoj VidaAEK
59
4
Martin GeorgievAEK
59
5
Alan MatturroFC Shakhtar Donetsk
58
Control
Top playmakers
1
Petros Mantalos AEK
93
2
Răzvan MarinAEK
67
3
Lovro MajerAEK
62
4
IsaqueFC Shakhtar Donetsk
59
5
João Mário AEK
49
Bad Boys
Most likely to get booked
1
Mykola Matvienko FC Shakhtar Donetsk
1
Aggression 19
2
Lovro MajerAEK
3
Aggression 58
3
Milán VitálisAEK
3
Aggression 50
4
IsaqueFC Shakhtar Donetsk
14
Aggression 41
5
Filipe RelvasAEK
20
Aggression 24
Power Rankings
Strongest overall
1
Kauã EliasFC Shakhtar Donetsk
83
2
Petros Mantalos AEK
82
3
Gleiker MendozaFC Shakhtar Donetsk
72
4
Zini AEK
71
5
Răzvan MarinAEK
70
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for FC Shakhtar Donetsk vs AEK 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 Shakhtar Donetsk vs AEK 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 Shakhtar Donetsk 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 Shakhtar Donetsk vs AEK 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 Shakhtar Donetsk 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.