· AFC Champions League
Al-Gharafa SC
ATT
4/10
DEF
4/10
vs
Al Qadisiyah
5/10
ATT
5/10
DEF
Scheduled· Mon 2 Nov · 18:15
Best Pick
Under 2.5
54%
Correct Score
Top
1-1
12.8%
7.84
Top
1-0
11%
9.09
0-1
9.5%
10.51
2-1
8.6%
11.69
0-0
8.2%
12.19
1-2
7.4%
13.51
2-0
7.4%
13.56
0-2
5.5%
18.12
141%
2.45TOP
X27%
3.72
232%
3.10
1X68%
X259%
1273%
Probable XI4-4-2
Kone
Rashidi
Ouwi
Abdulnasir
Traore
Hassan
Díaz
Hamdeh
Sassi
Ounas
Mohamed
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Al-Gharafa SC21 players
48.8/ 99Average
Top Rated
1Ayoub Al OuwiDEF64
2Hassan AladinFWD64
3Mason HolgateDEF62
Al Qadisiyah18 players
49.4/ 99Average
Top Rated
1Mateo ReteguiFWD63
2Yasser Al-ShahraniDEF60
3Jehad Thakri DEF58
Al-Gharafa SC36
PlayerOverall RatingImpactAggressionDiscipline
Ayoub Al Ouwi64455978
Hassan Aladin64684556
Mason Holgate62456372
Ferjani Sassi62636056
Yousef Moussa Al Khlif58596350
Saifeldeen Fadlalla56415372
Rayyan Ahmed Al Ali55495856
Mohammed Muntari55584550
Adam Ounas53534556
Yacine Brahimi50464761
Frank Magri49454861
Ahmed Kone47463450
Dame Traore45486336
Al Qadisiyah39
PlayerOverall RatingImpactAggressionDiscipline
Mateo Retegui63565378
Yasser Al-Shahrani60505667
Jehad Thakri 58495267
Nahitan Nández56485661
Julian Weigl55554261
Julián Quiñones52674419
Koen Casteels51513450
Nacho Fernández 50524550
Tijjani Reijnders50513956
Ali Badra Diabaté49556336
Suleiman Sani48456350
Abdulmalik Al-Jaber46454550
Mohammed bin Hamad bin Dwaihi Al Swaidan Al Qahtani46424756
Player Rankings
Snipers
Top goal threats
1
Hassan AladinAl-Gharafa SC
68
2
Julián QuiñonesAl Qadisiyah
67
3
Mohammed MuntariAl-Gharafa SC
58
4
Mateo ReteguiAl Qadisiyah
56
5
Adam OunasAl-Gharafa SC
53
Wall
Top defenders
1
Seydou SanoAl-Gharafa SC
59
2
Ali Badra DiabatéAl Qadisiyah
55
3
Hyun Soo JangAl-Gharafa SC
54
4
Nacho Fernández Al Qadisiyah
52
5
Yasser Al-ShahraniAl Qadisiyah
50
Control
Top playmakers
1
Ferjani SassiAl-Gharafa SC
63
2
Musab Al-JuwayrAl Qadisiyah
61
3
Yousef Moussa Al KhlifAl-Gharafa SC
59
4
Cameron PuertasAl Qadisiyah
59
5
Julian WeiglAl Qadisiyah
55
Bad Boys
Most likely to get booked
1
Seydou SanoAl-Gharafa SC
1
2
Fabricio DíazAl-Gharafa SC
5
3
Gastón ÁlvarezAl Qadisiyah
16
4
Jamal HamedAl-Gharafa SC
19
5
Julián QuiñonesAl Qadisiyah
19
Power Rankings
Strongest overall
1
Ayoub Al OuwiAl-Gharafa SC
64
2
Hassan AladinAl-Gharafa SC
64
3
Mateo ReteguiAl Qadisiyah
63
4
Mason HolgateAl-Gharafa SC
62
5
Ferjani SassiAl-Gharafa SC
62

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Al-Gharafa SC vs Al Qadisiyah 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-Gharafa SC vs Al Qadisiyah 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-Gharafa SC 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-Gharafa SC vs Al Qadisiyah 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-Gharafa SC 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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