· AFC Champions League
Al-Wasl FC
ATT
5/10
DEF
5/10
vs
AL Ahli Saudi
7/10
ATT
6/10
DEF
Scheduled· Mon 12 Oct · 16:00
Best Pick
BTTS
55%
Correct Score
Top
1-1
12.2%
8.18
Top
1-0
9%
11.05
0-1
9%
11.10
2-1
8.3%
12.06
1-2
8.3%
12.11
0-0
6.7%
15.00
2-0
6.1%
16.29
0-2
6.1%
16.43
137%
2.68TOP
X26%
3.88
237%
2.70TOP
1X63%
X263%
1274%
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 FC17 players
49.8/ 99Average
Top Rated
1Renato TapiaMID55
2Ali SalehFWD55
3Sasa IvkovicDEF54
AL Ahli Saudi26 players
50.8/ 99Average
Top Rated
1Mohammed SulaimanDEF66
2Ricardo MathiasFWD62
3Mohammed AbdulrahmanDEF61
Al-Wasl FC36
PlayerOverall RatingImpactAggressionDiscipline
Renato Tapia55624550
Ali Saleh55584550
Sasa Ivkovic54643256
Adryelson53515156
Leandro Leite53535250
Pedro Malheiro52494956
Siaka Sidibe52494956
Nicolás Giménez51534950
Dinko Horkas50504550
Abdulrahman Saleh49494850
Soufiane Bouftini49454756
Matheus Saldanha49454761
Mehdi Taremi48454856
AL Ahli Saudi48
PlayerOverall RatingImpactAggressionDiscipline
Mohammed Sulaiman66487467
Ricardo Mathias62635656
Mohammed Abdulrahman61468853
Valentin Atangana60338786
Edouard Mendy 5962950
Ali Majrashi58327669
Saleh Abu Al-Shamat58656044
Zakaria Hawsawi57276283
Galeno56613950
Firas Al-Buraikan54377691
Roger Ibañez53495158
Naif Masoud53496450
Ibrahima Diaby52485156
Player Rankings
Snipers
Top goal threats
1
Ricardo MathiasAL Ahli Saudi
63
2
GalenoAL Ahli Saudi
61
3
Ali SalehAl-Wasl FC
58
4
Leandro LeiteAl-Wasl FC
53
5
SerginhoAl-Wasl FC
48
Wall
Top defenders
1
Sasa IvkovicAl-Wasl FC
64
2
Merih DemiralAL Ahli Saudi
53
3
AdryelsonAl-Wasl FC
51
4
Abdulrahman SalehAl-Wasl FC
49
5
Pedro MalheiroAl-Wasl FC
49
Control
Top playmakers
1
Saleh Abu Al-ShamatAL Ahli Saudi
65
2
Renato TapiaAl-Wasl FC
62
3
Nicolás GiménezAl-Wasl FC
53
4
Eduard Spertsyan AL Ahli Saudi
51
5
Ridwan PopoolaAl-Wasl FC
49
Bad Boys
Most likely to get booked
1
Enzo MillotAL Ahli Saudi
18
2
Ridwan PopoolaAl-Wasl FC
23
3
Eduard Spertsyan AL Ahli Saudi
23
4
Ivan ToneyAL Ahli Saudi
30
5
Ziyad Al-JohaniAL Ahli Saudi
39
Power Rankings
Strongest overall
1
Mohammed SulaimanAL Ahli Saudi
66
2
Ricardo MathiasAL Ahli Saudi
62
3
Mohammed AbdulrahmanAL Ahli Saudi
61
4
Valentin AtanganaAL Ahli Saudi
60
5
Edouard Mendy AL Ahli Saudi
59

Frequently Asked Questions

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