· Premier League
Al İttihad Al Sakandary
ATT
6/10
DEF
6/10
vs
El Gaish
3/10
ATT
4/10
DEF
Planlandı· Fri 30 Oct · 18:00
En iyi tahmin
Ev sahibi kazanır
65%
Doğru skor
Mükemmel
1-0
16.6%
6.01
Mükemmel
2-0
14.5%
6.89
1-1
10.1%
9.91
0-0
9.6%
10.47
2-1
8.8%
11.37
3-0
8.4%
11.86
0-1
5.8%
17.28
3-1
5.1%
19.58
165%
1.54TOP
X23%
4.42
212%
8.05
1X88%
X235%
1277%

Kombine

3 seçim

AL Ittihad’s stronger attack and home advantage make defeat unlikely, while El Gaish’s leaky defence offers a clear route to goals. The visitors’ threat may be limited, but they should find a way through as the contest opens up—making both teams to score and over one and a half goals a compelling fit.

1Double ChanceAL Ittihad or Draw
2Match GoalsOver 1.5
3Both Teams to ScoreYes
Confirmed XI4-3-3
1
Soliman
24
Awad
7
Mostafa
5
Ibrahim
30
Mosaad
23
Saviour
14
Fakhri
8
Tony
80
Arumala
22
Alaa
10
Hassan
Impact Player Hot Head
Al İttihad Al Sakandary22 oyuncu
53.6/ 99Orta
En İyiler
1Mohamed TonyMID74
2Bibo Ahmed HassanMID68
3Mahmoud EmadMID66
El Gaish28 oyuncu
50.2/ 99Orta
En İyiler
1Ismaïl Ouro-AgoroFWD82
2Houssem Eddine SouissiMID71
3Ayman Amir AbdelazizFWD67
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Ismaïl Ouro-AgoroEl Gaish
79
2
John EbukaAl İttihad Al Sakandary
66
3
Mohamed AtefEl Gaish
65
4
Ayman Amir AbdelazizEl Gaish
59
5
Kareem TarekEl Gaish
57
Duvar
En iyi savunmacılar
1
Moataz MohamedAl İttihad Al Sakandary
79
2
Mostafa IbrahimAl İttihad Al Sakandary
73
3
Mohamed CamachoEl Gaish
73
4
Islam AbdellahAl İttihad Al Sakandary
72
5
Mido MostafaAl İttihad Al Sakandary
68
Kontrol
En iyi oyun kurucular
1
Mohamed Ashraf RoqaEl Gaish
78
2
Mahmoud EmadAl İttihad Al Sakandary
76
3
Bibo Ahmed HassanAl İttihad Al Sakandary
70
4
Mohamed FakhriAl İttihad Al Sakandary
70
5
Mohamed TonyAl İttihad Al Sakandary
65
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Khaled SotohiEl Gaish
1
2
Mostafa IbrahimAl İttihad Al Sakandary
2
3
Mohamed FakhriAl İttihad Al Sakandary
3
4
Yehia MostafaEl Gaish
7
5
Ahmed TarekEl Gaish
14
Güç sıralaması
Genel olarak en güçlüler
1
Ismaïl Ouro-AgoroEl Gaish
82
2
Mohamed TonyAl İttihad Al Sakandary
74
3
Houssem Eddine SouissiEl Gaish
71
4
Bibo Ahmed HassanAl İttihad Al Sakandary
68
5
Ayman Amir AbdelazizEl Gaish
67

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Al İttihad Al Sakandary vs El Gaish 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 Al İttihad Al Sakandary vs El Gaish 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 Al İttihad Al Sakandary 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 Al İttihad Al Sakandary vs El Gaish 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 Al İttihad Al Sakandary 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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