· AFC Champions League Elite
Al Sadd
ATT
5/10
DEF
2/10
vs
Al Ahli Saudi FC
8/10
ATT
7/10
DEF
Planlandı· Mon 2 Nov · 16:00
En iyi tahmin
KG VAR
55%
Doğru skor
Mükemmel
1-1
12.2%
8.23
Mükemmel
0-1
9.5%
10.53
1-2
8.7%
11.45
1-0
8.5%
11.82
2-1
7.8%
12.85
0-2
6.8%
14.66
0-0
6.6%
15.13
2-2
5.6%
17.89
134%
2.98
X26%
3.90
241%
2.45TOP
1X60%
X267%
1275%
Probable XI4-4-2
Yahia
Bounacer
Al-Yazidi
Al-Ameen
Suhail
Soria
Abweny
Eid
Claudinho
Afif
González
Impact Player Hot Head
Kadrolar henüz açıklanmadı — aşağıda tam kadro analizi
Al Sadd25 oyuncu
52.3/ 99Orta
En İyiler
1Rafa MujicaFWD95
2Javairô DilrosunFWD79
3Boualem KhoukhiDEF70
Al Ahli Saudi FC28 oyuncu
50.7/ 99Orta
En İyiler
1Mohammed SulaimanDEF62
2Mohammed AbdulrahmanDEF61
3Ricardo MathiasFWD61
Al Sadd40
PlayerGenel DereceEtkiAgresyonDisiplin
Rafa Mujica95997673
Javairô Dilrosun79885656
Boualem Khoukhi70604389
Paulo Otávio61399964
Claudinho61904127
Paul Caddis61625061
Mohamed Naceur Almanai60526072
Anas Abdulsalam Abweny59526061
Hashim Ali58595056
Hasan Al-Haydos 56585350
Younes El Hannach55663950
Mohammed Muntari54574550
Saad Al-Sheeb52534550
Al Ahli Saudi FC50
PlayerGenel DereceEtkiAgresyonDisiplin
Mohammed Sulaiman62476567
Mohammed Abdulrahman61468952
Ricardo Mathias61625756
Zakaria Hawsawi59316383
Valentin Atangana59337683
Firas Al-Buraikan59437189
Edouard Mendy 57591250
Saleh Abu Al-Shamat57645944
Ali Majrashi56386863
Galeno56613950
Roger Ibañez55494963
Merih Demiral54543567
Naif Masoud53496450
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Rafa MujicaAl Sadd
99
2
Javairô DilrosunAl Sadd
88
3
Ricardo MathiasAl Ahli Saudi FC
62
4
GalenoAl Ahli Saudi FC
61
5
Hashim AliAl Sadd
59
Duvar
En iyi savunmacılar
1
Younes El HannachAl Sadd
66
2
Boualem KhoukhiAl Sadd
60
3
Merih DemiralAl Ahli Saudi FC
54
4
Ahmed SuhailAl Sadd
52
5
Roger IbañezAl Ahli Saudi FC
49
Kontrol
En iyi oyun kurucular
1
ClaudinhoAl Sadd
90
2
Saleh Abu Al-ShamatAl Ahli Saudi FC
64
3
Paul CaddisAl Sadd
62
4
Agustín SoriaAl Sadd
55
5
Anas Abdulsalam AbwenyAl Sadd
52
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Mohamed Camara Al Sadd
1
2
Ahmed SuhailAl Sadd
4
3
Ivan ToneyAl Ahli Saudi FC
9
4
Mustafa MashaalAl Sadd
22
5
ClaudinhoAl Sadd
27
Güç sıralaması
Genel olarak en güçlüler
1
Rafa MujicaAl Sadd
95
2
Javairô DilrosunAl Sadd
79
3
Boualem KhoukhiAl Sadd
70
4
Mohammed SulaimanAl Ahli Saudi FC
62
5
Paulo OtávioAl Sadd
61

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Al Sadd vs Al Ahli Saudi FC 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 Sadd vs Al Ahli Saudi FC 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 Sadd 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 Sadd vs Al Ahli Saudi FC 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 Sadd 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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