· AFC Champions League Two
Al Kuwait
ATT
5/10
DEF
5/10
vs
Nasaf
5/10
ATT
6/10
DEF
Запланирован· Wed 4 Nov · 16:00
Лучший выбор
ОБЕ ЗАБЬЮТ
54%
Точный счёт
Отличная
1-1
11.4%
8.74
Отличная
1-0
10.5%
9.52
2-1
9.7%
10.35
2-0
8.9%
11.27
0-1
6.8%
14.77
1-2
6.2%
16.06
0-0
6.2%
16.08
3-1
5.4%
18.39
151%
1.95TOP
X24%
4.15
224%
4.08
1X75%
X248%
1275%
Probable XI4-4-2
Kameel
Zola
Hammoud
Al-Saab
Al-Enezi
Al-Qarzaei
Al-Dhefiri
Dara
Jesús
Amoory
Kameel
Impact Player Hot Head
Составы ещё не объявлены — полный анализ игроков ниже
Al Kuwait15 игроков
52.1/ 99Средний
Лучшие
1Fahad HammoudDEF75
2Yousef NasserFWD70
3Sami Al-SaneaDEF62
Nasaf14 игроков
53.8/ 99Средний
Лучшие
1Alibek DavronovDEF67
2Murodbek RahmatovMID64
3Bobur AbdikholikovFWD61
Al Kuwait35
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Fahad Hammoud75994550
Yousef Nasser70676467
Sami Al-Sanea62705450
Oumar Gonzalez 56674050
Ahmad Al-Dhefiri56534961
Ali Pour Dara54534956
Saud Al-Hoshan53543450
Mohammad Freih50484161
Mohsen Ghareeb48504550
Redha Hani47464750
Taha Yassine Khenissi47434461
Arsène Zola46624130
Meshari Ghanam46454550
Nasaf27
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Alibek Davronov67466083
Murodbek Rahmatov64625067
Bobur Abdikholikov61565372
Zafarmurod Abdurakhmatov58485367
Sharof Mukhiddinov58604261
Umar Eshmuradov56525161
Oybek Rustamov53596040
Adenis Shala52514856
Abduvokhid Nematov51513450
Abbos Gulomov51455461
Diyorbek Abdunazarov49455650
Yusuf Otubanjo47425556
Akramzhon Komilov44424550
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Yousef NasserAl Kuwait
67
2
Bobur AbdikholikovNasaf
56
3
Adenis ShalaNasaf
51
4
Abbos GulomovNasaf
45
5
Taha Yassine KhenissiAl Kuwait
43
Стена
Лучшие защитники
1
Fahad HammoudAl Kuwait
99
2
Sami Al-SaneaAl Kuwait
70
3
Oumar Gonzalez Al Kuwait
67
4
Arsène ZolaAl Kuwait
62
5
Golib GaybullaevNasaf
53
Контроль
Лучшие плеймейкеры
1
Murodbek RahmatovNasaf
62
2
Sharof MukhiddinovNasaf
60
3
Oybek RustamovNasaf
59
4
Ahmad Al-DhefiriAl Kuwait
53
5
Ali Pour DaraAl Kuwait
53
Хулиганы
Склонны к карточкам
1
Abdullah Al-QarzaeiAl Kuwait
13
2
E. BarrahmaAl Kuwait
18
3
Arsène ZolaAl Kuwait
30
4
Golib GaybullaevNasaf
30
5
Oybek RustamovNasaf
40
Рейтинг силы
Сильнейшие в целом
1
Fahad HammoudAl Kuwait
75
2
Yousef NasserAl Kuwait
70
3
Alibek DavronovNasaf
67
4
Murodbek RahmatovNasaf
64
5
Sami Al-SaneaAl Kuwait
62

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Al Kuwait vs Nasaf 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 Kuwait vs Nasaf 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 Kuwait 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 Kuwait vs Nasaf 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 Kuwait 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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