· Division 1
Al Ula
ATT
8/10
DEF
7/10
vs
Al-Orobah FC
6/10
ATT
6/10
DEF
Scheduled· Mon 26 Oct · 16:45
Best Pick
Over 2.5
60%
Correct Score
Top
1-1
10%
10.00
Top
2-1
9.9%
10.09
1-0
9%
11.08
2-0
8.9%
11.18
3-1
6.6%
15.26
3-0
5.9%
16.91
1-2
5.5%
18.05
2-2
5.5%
18.21
158%
1.74TOP
X22%
4.63
221%
4.81
1X80%
X243%
1279%
Probable XI4-4-2
Atiah
Al-Khaibre
Al-Obaid
Al-Zubaidi
Al-Nakhli
Al-Hatila
Al-Sulayhim
Horta
Al-Omair
Adam
Al-Zain
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Al Ula30 players
55.5/ 99Strong
Top Rated
1Abdulfattah AdamFWD97
2Aseel Abdullah AbdulrazaqFWD84
3Freej Al-JizaniFWD81
Al-Orobah FC25 players
55.0/ 99Average
Top Rated
1Hassan Al-SolanFWD88
2Abdulaziz MasnomFWD83
3Omar Abdullah Al-SharariFWD81
Al Ula43
PlayerOverall RatingImpactAggressionDiscipline
Abdulfattah Adam97996589
Aseel Abdullah Abdulrazaq84999925
Freej Al-Jizani81828461
Efthymios Koulouris76802291
Abdulmajeed Al-Sulayhim73644491
André Horta68961950
Sylla Sow68713174
Khaled Al-Shamrani66417391
Damyan Yordanov66996523
Yousef Al-Honaifesh64597356
Yasser Yaqoub Ibrahim63396776
Ali Al-Zubaidi59298490
Yousef Haqawi59664256
Al-Orobah FC32
PlayerOverall RatingImpactAggressionDiscipline
Hassan Al-Solan88995261
Abdulaziz Masnom83993950
Omar Abdullah Al-Sharari81819967
Fahad Al-Muneef75795764
Anass Najah65508563
Khaled Eid65645167
Ali Al-Sultan59469447
Hatem Al-Jahani57581250
Ammar Al Harfi57505461
Fahad Al-Rashidi57256591
Famara Diédhiou 55585147
Saud Al-Ruwaili53534550
Paulo Vitor53564850
Player Rankings
Snipers
Top goal threats
1
Abdulfattah AdamAl Ula
99
2
Aseel Abdullah AbdulrazaqAl Ula
99
3
Abdulaziz MasnomAl-Orobah FC
99
4
Hassan Al-SolanAl-Orobah FC
99
5
Freej Al-JizaniAl Ula
82
Wall
Top defenders
1
Fahad Al-MuneefAl-Orobah FC
79
2
Khaled Al RuwailyAl Ula
75
3
Yousef HaqawiAl Ula
66
4
Ziyad Al-HunaitiAl-Orobah FC
65
5
Matija NastasićAl Ula
64
Control
Top playmakers
1
Damyan YordanovAl Ula
99
2
André HortaAl Ula
96
3
Abdulmajeed Al-SulayhimAl Ula
64
4
Khaled EidAl-Orobah FC
64
5
Yousef Al-HonaifeshAl Ula
59
Bad Boys
Most likely to get booked
1
Mohammed Abbas Al-NakhliAl Ula
1
2
Ahmed Al-JaweedAl-Orobah FC
1
3
Matija NastasićAl Ula
5
4
Khaled Al RuwailyAl Ula
7
5
Faisal Al-AsmariAl Ula
12
Power Rankings
Strongest overall
1
Abdulfattah AdamAl Ula
97
2
Hassan Al-SolanAl-Orobah FC
88
3
Aseel Abdullah AbdulrazaqAl Ula
84
4
Abdulaziz MasnomAl-Orobah FC
83
5
Freej Al-JizaniAl Ula
81

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Al Ula vs Al-Orobah FC 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 Ula vs Al-Orobah FC 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 Ula 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 Ula vs Al-Orobah FC 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 Ula 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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