· Champions League
FC Shakhtar Donetsk
ATT
8/10
DEF
7/10
3:1
AEK
6/10
ATT
7/10
DEF
Bitti
En iyi tahmin
Üst 2.5
67%
Doğru skor95′
Mükemmel
2-1
9.6%
10.00
Mükemmel
1-1
8.7%
6.00
2-0
7.9%
13.00
3-1
7.2%
21.00
1-0
7.1%
8.50
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%

Kombine

3 seçim

Shakhtar’s supercharged attack against AEK’s lively front line points toward a chaotic, end‑to‑end Champions League night. The hosts carry greater cutting edge but AEK’s solid offense looks too sharp to be silenced, making both teams to score and a goal‑heavy script likely, while Shakhtar’s superiority and home energy suggest defeat remains unlikely.

1Double ChanceFC Shakhtar Donetsk or Draw
2Match GoalsOver 2.5
3Both Teams to ScoreYes
xG — Beklenen goller
FC Shakhtar Donetsk
FC Shakhtar Donetsk bekl.
AEK
AEK bekl.
goal
15 dk'da bir
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
FC Shakhtar Donetsk
AEK
Confirmed XI4-5-1
31
Riznyk
17
Tobias
5
Bondar
22
Matvienko
13
Henrique
27
Ocheretko
30
Alisson
10
Pedrinho
6
Gomes
11
Newerton
9
Elias
Impact Player Hot Head
FC Shakhtar Donetsk34 oyuncu
52.5/ 99Orta
En İyiler
1Kauã EliasFWD83
2Gleiker MendozaFWD72
3Dmytro RiznykGK69
AEK26 oyuncu
52.3/ 99Orta
En İyiler
1Petros Mantalos MID82
2Zini FWD71
3Răzvan MarinMID70
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
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
Duvar
En iyi savunmacılar
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
Kontrol
En iyi oyun kurucular
1
Petros Mantalos AEK
93
2
Răzvan MarinAEK
67
3
Lovro MajerAEK
62
4
IsaqueFC Shakhtar Donetsk
59
5
João Mário AEK
49
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Mykola Matvienko FC Shakhtar Donetsk
1
2
Lovro MajerAEK
3
3
Milán VitálisAEK
3
4
IsaqueFC Shakhtar Donetsk
14
5
Filipe RelvasAEK
20
Güç sıralaması
Genel olarak en güçlüler
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

Sıkça Sorulan Sorular

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.

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