· Arabian Gulf League
Al-Wasl FC
ATT
6/10
DEF
8/10
vs
AL Dhafra SSC
3/10
ATT
3/10
DEF
Scheduled· Fri 30 Oct · 12:55
Best Pick
Home Win
69%
Correct Score
Top
2-0
11.7%
8.57
Top
1-0
10.5%
9.57
2-1
9.7%
10.31
3-0
8.7%
11.51
1-1
8.7%
11.51
3-1
7.2%
13.86
4-0
4.8%
20.62
0-0
4.7%
21.37
169%
1.45TOP
X18%
5.46
213%
7.98
1X87%
X231%
1282%
Probable XI4-4-2
Amer
Saleh
Adryelson
Vareta
Hugo 
Ahmed
Puertas
Lima
Alves
Saleh
Bousso
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Al-Wasl FC33 players
51.4/ 99Average
Top Rated
1SerginhoFWD86
2Gabriel VaretaDEF77
3AdryelsonDEF73
AL Dhafra SSC32 players
49.5/ 99Average
Top Rated
1Rostand Delord Menpweh DjoohMID71
2Leonard AmasimekuDEF64
3Zayed Al AmeriFWD64
Al-Wasl FC38
PlayerOverall RatingImpactAggressionDiscipline
Serginho86994856
Gabriel Vareta77606391
Adryelson73803382
Malek Janeer72607971
Tahnoon Al-Zaabi67556272
Ridwan Popoola65478071
Hugo 61405583
Takashi Uchino61317591
Siaka Sidibe60605060
Nicolás Giménez59754143
Abdulrahman Saleh58269991
Pedro Malheiro57256391
Miguel Borja57605449
AL Dhafra SSC39
PlayerOverall RatingImpactAggressionDiscipline
Rostand Delord Menpweh Djooh71545884
Leonard Amasimeku64525376
Zayed Al Ameri64752850
Mansor Al Harbi62358191
Zé Welinton60515667
Serafim59544569
Arielson59486972
Marcelinho59486474
Dramane Koumare58742756
Feras Saleh Alkhaseebi57564761
Mohammed Ibrahim Al Hammadi55746311
Ezeckiel Noumonvi54403985
Vitinho54623950
Player Rankings
Snipers
Top goal threats
1
SerginhoAl-Wasl FC
99
2
Brahian PalaciosAl-Wasl FC
79
3
Zayed Al AmeriAL Dhafra SSC
75
4
Mohammed Ibrahim Al HammadiAL Dhafra SSC
74
5
Renato JúniorAl-Wasl FC
62
Wall
Top defenders
1
AdryelsonAl-Wasl FC
80
2
Dramane KoumareAL Dhafra SSC
74
3
Mouhcine RabjaAL Dhafra SSC
70
4
Sasa IvkovicAl-Wasl FC
65
5
Gabriel VaretaAl-Wasl FC
60
Control
Top playmakers
1
Nicolás GiménezAl-Wasl FC
75
2
Renato TapiaAl-Wasl FC
63
3
VitinhoAL Dhafra SSC
62
4
Malek JaneerAl-Wasl FC
60
5
Siaka SidibeAl-Wasl FC
60
Bad Boys
Most likely to get booked
1
Soufiane BouftiniAl-Wasl FC
1
2
Renato TapiaAl-Wasl FC
1
3
Brahian PalaciosAl-Wasl FC
1
4
Mouhcine RabjaAL Dhafra SSC
1
5
Yousif AlmarzooqiAL Dhafra SSC
1
Power Rankings
Strongest overall
1
SerginhoAl-Wasl FC
86
2
Gabriel VaretaAl-Wasl FC
77
3
AdryelsonAl-Wasl FC
73
4
Malek JaneerAl-Wasl FC
72
5
Rostand Delord Menpweh DjoohAL Dhafra SSC
71

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Al-Wasl FC vs AL Dhafra SSC 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-Wasl FC vs AL Dhafra SSC 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-Wasl FC 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-Wasl FC vs AL Dhafra SSC 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-Wasl FC 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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