· Division 1
Al Anwar
ATT
6/10
DEF
5/10
vs
Al-Okhdood Club
5/10
ATT
6/10
DEF
Scheduled· Mon 26 Oct · 15:00
Best Pick
BTTS
57%
Correct Score
Top
1-1
10.6%
9.43
Top
2-1
9.9%
10.15
1-0
9.5%
10.49
2-0
8.9%
11.28
3-1
6.1%
16.38
1-2
5.9%
16.96
0-1
5.7%
17.53
3-0
5.5%
18.21
155%
1.82TOP
X23%
4.42
223%
4.43
1X78%
X246%
1278%
Probable XI4-4-2
Shrahili
Rashidi
Al-Rashidi
Al-Shammari
Derradji
Damdam
Al-Samtai
Majrashi
Al-Majali
Bahusayn
Bruno
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Al Anwar28 players
48.1/ 99Average
Top Rated
1Mohammed Abdulwahab Al-ZahraniDEF92
2Yazid JoshanFWD79
3Fahad Al-MuneefDEF75
Al-Okhdood Club19 players
51.3/ 99Average
Top Rated
1Zinho GanoFWD77
2AlexDEF75
3Saud SalemMID63
Al Anwar36
PlayerOverall RatingImpactAggressionDiscipline
Mohammed Abdulwahab Al-Zahrani92999950
Yazid Joshan79846161
Fahad Al-Muneef75795764
Abdulmalek Al-Shammari61455972
Faisal Al-Majali 59429756
Rashid Al Mutairi58329570
Sulaiman Al-Nakhli58732556
Kack Gabriel54365770
Khaled Shrahili53545747
Abdullah Al Rashidi50514256
Fawaz Rabie49534349
Hussam Majrashi49494553
Saif Hussein49553553
Al-Okhdood Club34
PlayerOverall RatingImpactAggressionDiscipline
Zinho Gano77746375
Alex75774678
Saud Salem63387389
Hassan Al-Harbi61672383
Naif Asiri61595361
Hatem Al-Jahani57581250
Mohammed Al-Jahif56683450
Rui Pedro5392417
Rakan Al-Najjar5253950
Hussain Al-Zabdani 51455456
Mohammed Abo Abd50364078
Nawaf Al-Hawsawi49435653
Balla Sangaré49454661
Player Rankings
Snipers
Top goal threats
1
Yazid JoshanAl Anwar
84
2
Zinho GanoAl-Okhdood Club
74
3
Mohammed MajrashiAl Anwar
65
4
Khaled Al-LazamAl-Okhdood Club
54
5
Rédha BensayahAl Anwar
49
Wall
Top defenders
1
Mohammed Abdulwahab Al-ZahraniAl Anwar
99
2
Fahad Al-MuneefAl Anwar
79
3
AlexAl-Okhdood Club
77
4
Mohammed Al-GhamdiAl Anwar
70
5
Hassan Al-HarbiAl-Okhdood Club
67
Control
Top playmakers
1
Rui PedroAl-Okhdood Club
92
2
Sulaiman Al-NakhliAl Anwar
73
3
Mohammed Al-JahifAl-Okhdood Club
68
4
Saif Al-QarehAl-Okhdood Club
46
5
Faisal Al-Majali Al Anwar
42
Bad Boys
Most likely to get booked
1
Mohammed Al-GhamdiAl Anwar
1
2
Abdullah Al-SamtaiAl Anwar
1
3
Mohammed MajrashiAl Anwar
1
4
Ghassan HawsawiAl-Okhdood Club
1
5
Nuhair Al-MahriAl Anwar
3
Power Rankings
Strongest overall
1
Mohammed Abdulwahab Al-ZahraniAl Anwar
92
2
Yazid JoshanAl Anwar
79
3
Zinho GanoAl-Okhdood Club
77
4
Fahad Al-MuneefAl Anwar
75
5
AlexAl-Okhdood Club
75

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Al Anwar vs Al-Okhdood Club 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 Anwar vs Al-Okhdood Club 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 Anwar 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 Anwar vs Al-Okhdood Club 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 Anwar 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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