· AFC Cup
Al Wahda
ATT
5/10
DEF
5/10
vs
AL Khalidiyah
4/10
ATT
5/10
DEF
Scheduled· Wed 4 Nov · 16:00
Best Pick
BTTS
54%
Correct Score
Top
1-1
11.3%
8.88
Top
1-0
10.7%
9.36
2-1
9.8%
10.25
2-0
9.3%
10.81
0-1
6.5%
15.38
0-0
6.2%
16.21
1-2
5.9%
16.85
3-1
5.6%
17.76
153%
1.88TOP
X24%
4.21
223%
4.34
1X77%
X247%
1276%
Probable XI4-4-2
Amer
Zhir
Majhed
Ogbu
Guga
Hamad
Cerin
Suarez
Jadsom
Somhi
Pradas
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Al Wahda17 players
54.0/ 99Average
Top Rated
1Gaston Alvarez SuarezMID74
2Favour Inyeka OgbuDEF72
3Abdulla HamadMID67
AL Khalidiyah16 players
48.1/ 99Average
Top Rated
1Anas OuahimMID58
2Nizar El OtmaniMID55
3Sayed DhiyaMID55
Al Wahda35
PlayerOverall RatingImpactAggressionDiscipline
Gaston Alvarez Suarez74699956
Favour Inyeka Ogbu72953950
Abdulla Hamad67595672
Adam Gnezda Cerin67704767
Omar Khribin 58594956
Amenallah Majhed56505261
Guga52406556
Lucas Pimenta52613650
Asher Agbinone52475661
Caio Canedo52458850
Facundo Insfrán50503450
Jadsom47565535
Renne Rivas46454550
AL Khalidiyah26
PlayerOverall RatingImpactAggressionDiscipline
Anas Ouahim58465767
Nizar El Otmani55565750
Sayed Dhiya55604850
Mohammad Al Gharably52534550
Darko Velkovski52544356
Oussama Shili51454861
Patrick Maswanganyi51514156
Domagoj Drožđek51534350
Mohamed Al Romaihi50464761
A. Assal48484550
Mohamed Adel48505841
Dominique Mendy43664118
Rachid Boumessous42515430
Player Rankings
Snipers
Top goal threats
1
Christian Benteke Al Wahda
59
2
Omar Khribin Al Wahda
59
3
Domagoj DrožđekAL Khalidiyah
53
4
Asher AgbinoneAl Wahda
47
5
Mohamed Al RomaihiAL Khalidiyah
46
Wall
Top defenders
1
Favour Inyeka OgbuAl Wahda
95
2
Lucas PimentaAl Wahda
61
3
Darko VelkovskiAL Khalidiyah
54
4
Rachid BoumessousAL Khalidiyah
51
5
Amenallah MajhedAl Wahda
50
Control
Top playmakers
1
Adam Gnezda CerinAl Wahda
70
2
Gaston Alvarez SuarezAl Wahda
69
3
Dominique MendyAL Khalidiyah
66
4
Sayed DhiyaAL Khalidiyah
60
5
Abdulla HamadAl Wahda
59
Bad Boys
Most likely to get booked
1
Walid SabbarAL Khalidiyah
1
2
Christian Benteke Al Wahda
9
3
Dominique MendyAL Khalidiyah
18
4
Rachid BoumessousAL Khalidiyah
30
5
JadsomAl Wahda
35
Power Rankings
Strongest overall
1
Gaston Alvarez SuarezAl Wahda
74
2
Favour Inyeka OgbuAl Wahda
72
3
Abdulla HamadAl Wahda
67
4
Adam Gnezda CerinAl Wahda
67
5
Omar Khribin Al Wahda
58

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Al Wahda vs AL Khalidiyah 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 Wahda vs AL Khalidiyah 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 Wahda 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 Wahda vs AL Khalidiyah 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 Wahda 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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