· Stars League
AL Wakrah
ATT
5/10
DEF
5/10
vs
Al-Gharafa SC
5/10
ATT
3/10
DEF
Scheduled· Thu 8 Oct · 17:00
Best Pick
BTTS
63%
Correct Score
Top
1-1
9.9%
10.09
Top
2-1
9.4%
10.65
1-0
7.2%
13.88
2-0
6.8%
14.66
1-2
6.8%
14.66
2-2
6.5%
15.48
3-1
5.9%
16.87
0-1
5.2%
19.10
150%
2.01TOP
X22%
4.47
228%
3.57
1X72%
X250%
1278%
Probable XI4-4-2
Othman
Mitwali
Gomaa
Ali
Mendes
Raafat
Saei
Fadel
Laïdouni
Zouhzouh
Assal
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
AL Wakrah26 players
49.5/ 99Average
Top Rated
1Aïssa LaïdouniMID82
2Luis AlbertoMID78
3Hamdy FathyMID65
Al-Gharafa SC21 players
43.4/ 99Average
Top Rated
1Rayyan Ahmed Al AliDEF66
2Ayoub Al OuwiDEF65
3Frank MagriFWD60
AL Wakrah35
PlayerOverall RatingImpactAggressionDiscipline
Aïssa Laïdouni82725889
Luis Alberto78912389
Hamdy Fathy65816439
Ayoub Assal65549783
Nabil Erfan61377371
Nasser Al-Yazidi57732850
Rabh Boussafi57643250
Redouane Berkane55485967
Ahmed Fadel54594850
Abdullah Ali Saei52465656
Mohamed Alaaeldin50435952
Muayed Hassan50399956
Mohamed Al-Bakri49481250
Al-Gharafa SC34
PlayerOverall RatingImpactAggressionDiscipline
Rayyan Ahmed Al Ali66429187
Ayoub Al Ouwi65426382
Frank Magri60456791
Hassan Aladin54376991
Fabricio Díaz52535945
Dame Traore51613054
Yacine Brahimi51484067
Ahmed Kone50513950
Chalpan Abdulnasir50534550
Khalifa Ababacar49489954
Hyun Soo Jang48541183
Yousef Moussa Al Khlif46397045
Mostafa Essam Qadeera45454550
Player Rankings
Snipers
Top goal threats
1
Rabh BoussafiAL Wakrah
64
2
Ayoub AssalAL Wakrah
54
3
Amine ZouhzouhAL Wakrah
49
4
Redouane BerkaneAL Wakrah
48
5
Yacine BrahimiAl-Gharafa SC
48
Wall
Top defenders
1
Lucas Mendes AL Wakrah
67
2
Seydou SanoAl-Gharafa SC
67
3
Dame TraoreAl-Gharafa SC
61
4
Mason HolgateAl-Gharafa SC
57
5
Hyun Soo JangAl-Gharafa SC
54
Control
Top playmakers
1
Luis AlbertoAL Wakrah
91
2
Hamdy FathyAL Wakrah
81
3
Nasser Al-YazidiAL Wakrah
73
4
Aïssa LaïdouniAL Wakrah
72
5
Ahmed FadelAL Wakrah
59
Bad Boys
Most likely to get booked
1
Almahdi AliAL Wakrah
1
2
Abdulrahman Faiz Al RashidiAl-Gharafa SC
1
3
Mason HolgateAl-Gharafa SC
1
4
Seydou SanoAl-Gharafa SC
1
5
Florinel ComanAl-Gharafa SC
1
Power Rankings
Strongest overall
1
Aïssa LaïdouniAL Wakrah
82
2
Luis AlbertoAL Wakrah
78
3
Rayyan Ahmed Al AliAl-Gharafa SC
66
4
Hamdy FathyAL Wakrah
65
5
Ayoub AssalAL Wakrah
65

Frequently Asked Questions

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