· Stars League
Al Shamal SC
ATT
5/10
DEF
5/10
vs
Al Duhail
5/10
ATT
6/10
DEF
Запланирован· Thu 8 Oct · 14:45
Лучший выбор
ОБЕ ЗАБЬЮТ
65%
Точный счёт
Отличная
1-1
9.9%
10.06
Отличная
2-1
8.9%
11.28
1-2
7.6%
13.16
2-2
6.8%
14.76
1-0
6.5%
15.39
2-0
5.8%
17.25
0-1
5.6%
17.95
3-1
5.3%
18.97
144%
2.28TOP
X23%
4.39
233%
3.01
1X67%
X256%
1277%
Probable XI4-4-2
Mohamed
Hassan
Hagana
Bais
Waad
Musa
Sayyar
Tawfik
Collado
Al-Saeed
Al-Muhannadi
Impact Player Hot Head
Составы ещё не объявлены — полный анализ игроков ниже
Al Shamal SC28 игроков
50.0/ 99Средний
Лучшие
1Tiago SilvaMID85
2Tamer SeyamFWD66
3Álex ColladoMID65
Al Duhail30 игроков
50.7/ 99Средний
Лучшие
1Adam FriakhMID87
2Youssef AymanDEF66
3TutaDEF63
Al Shamal SC35
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Tiago Silva85866572
Tamer Seyam66559583
Álex Collado65516273
Mohamed Ali Ben Romdhane 62535967
Mowafak Awad60368271
Akram Tawfik58569938
Mohamed Naceur Almanai58347472
Nassim Benaissa56524367
Baghdad Bounedjah5680452
F. Bais54525950
Younes El Hannach54288758
Ali Said Al-Muhannadi53466561
Hussein Bahzad52544950
Al Duhail37
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Adam Friakh87929961
Youssef Ayman66484791
Tuta63445780
Edmílson Junior62575372
Ibrahima Bamba61428060
Ghanem Al-Minhali60488152
Mohamed Emad Aiash60526260
Marco Verratti59896818
Salah Zakaria55564552
Youssouf Sabaly55297466
Assim Madibo55499937
Sekou Oumar Yansane55435679
Mohammed Al-Naimi52269254
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Baghdad BounedjahAl Shamal SC
80
2
Edmílson JuniorAl Duhail
57
3
Krzysztof PiatekAl Duhail
57
4
Tamer SeyamAl Shamal SC
55
5
Tahsin JamshidAl Duhail
53
Стена
Лучшие защитники
1
Jeison MurilloAl Shamal SC
70
2
Hussein BahzadAl Shamal SC
54
3
Bassam Al-RawiAl Duhail
54
4
F. BaisAl Shamal SC
52
5
Mohamed Emad AiashAl Duhail
52
Контроль
Лучшие плеймейкеры
1
Adam FriakhAl Duhail
92
2
Marco VerrattiAl Duhail
89
3
Tiago SilvaAl Shamal SC
86
4
Karim BoudiafAl Duhail
76
5
Fares SaidAl Duhail
71
Хулиганы
Склонны к карточкам
1
Ahmad Mohammed Al-SaeedAl Shamal SC
1
2
Boubakary SoumaréAl Duhail
1
3
Baghdad BounedjahAl Shamal SC
2
4
Abdullah Al-AhrakAl Duhail
2
5
Tahsin JamshidAl Duhail
7
Рейтинг силы
Сильнейшие в целом
1
Adam FriakhAl Duhail
87
2
Tiago SilvaAl Shamal SC
85
3
Tamer SeyamAl Shamal SC
66
4
Youssef AymanAl Duhail
66
5
Álex ColladoAl Shamal SC
65

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Al Shamal SC vs Al Duhail 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 Shamal SC vs Al Duhail 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 Shamal SC 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 Shamal SC vs Al Duhail 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 Shamal SC 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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