· AFC Champions League
Al-Sadd
ATT
6/10
DEF
2/10
vs
AL Ahli Saudi
8/10
ATT
7/10
DEF
Scheduled· Mon 2 Nov · 16:00
Best Pick
BTTS
55%
Correct Score
Top
1-1
12.2%
8.19
Top
0-1
9.5%
10.48
1-2
8.7%
11.53
1-0
8.6%
11.62
2-1
7.8%
12.79
0-2
6.8%
14.76
0-0
6.7%
14.88
2-2
5.6%
18.01
134%
2.95
X26%
3.88
240%
2.48TOP
1X60%
X266%
1274%
Probable XI4-4-2
Yahia
Bounacer
Al-Yazidi
Al-Ameen
Suhail
Soria
Abweny
Eid
Claudinho
Afif
González
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Al-Sadd25 players
53.0/ 99Average
Top Rated
1Rafa MujicaFWD95
2Javairô DilrosunFWD80
3Boualem KhoukhiDEF70
AL Ahli Saudi28 players
50.9/ 99Average
Top Rated
1Mohammed SulaimanDEF62
2Ricardo MathiasFWD62
3Mohammed AbdulrahmanDEF61
Al-Sadd40
PlayerOverall RatingImpactAggressionDiscipline
Rafa Mujica95997673
Javairô Dilrosun80895656
Boualem Khoukhi70614389
Paulo Otávio61409964
Claudinho61904228
Paul Caddis61625061
Mohamed Naceur Almanai60526072
Anas Abdulsalam Abweny59526061
Hashim Ali58595056
Hasan Al-Haydos 57595350
Younes El Hannach55663950
Mohammed Muntari55584550
Saad Al-Sheeb52534550
AL Ahli Saudi50
PlayerOverall RatingImpactAggressionDiscipline
Mohammed Sulaiman62486667
Ricardo Mathias62635756
Mohammed Abdulrahman61478952
Zakaria Hawsawi59316383
Valentin Atangana59337883
Firas Al-Buraikan59447089
Saleh Abu Al-Shamat58646044
Edouard Mendy 57591250
Ali Majrashi56386863
Roger Ibañez56504963
Galeno56623950
Merih Demiral54543567
Naif Masoud53496450
Player Rankings
Snipers
Top goal threats
1
Rafa MujicaAl-Sadd
99
2
Javairô DilrosunAl-Sadd
89
3
Ricardo MathiasAL Ahli Saudi
63
4
GalenoAL Ahli Saudi
62
5
Hasan Al-Haydos Al-Sadd
59
Wall
Top defenders
1
Younes El HannachAl-Sadd
66
2
Boualem KhoukhiAl-Sadd
61
3
Merih DemiralAL Ahli Saudi
54
4
Ahmed SuhailAl-Sadd
52
5
Roger IbañezAL Ahli Saudi
50
Control
Top playmakers
1
ClaudinhoAl-Sadd
90
2
Saleh Abu Al-ShamatAL Ahli Saudi
64
3
Paul CaddisAl-Sadd
62
4
Agustín SoriaAl-Sadd
55
5
Eduard Spertsyan AL Ahli Saudi
53
Bad Boys
Most likely to get booked
1
Mohamed Camara Al-Sadd
1
2
Ivan ToneyAL Ahli Saudi
10
3
Mustafa MashaalAl-Sadd
23
4
ClaudinhoAl-Sadd
28
5
Ibrahima DiabyAL Ahli Saudi
28
Power Rankings
Strongest overall
1
Rafa MujicaAl-Sadd
95
2
Javairô DilrosunAl-Sadd
80
3
Boualem KhoukhiAl-Sadd
70
4
Mohammed SulaimanAL Ahli Saudi
62
5
Ricardo MathiasAL Ahli Saudi
62

Frequently Asked Questions

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