· Premier League
Sumqayıt
ATT
6/10
DEF
3/10
vs
Safa
4/10
ATT
4/10
DEF
Запланирован· Mon 2 Nov · 15:00
Лучший выбор
Победа хозяев
54%
Точный счёт
Отличная
1-0
11.7%
8.56
Отличная
1-1
11.4%
8.76
2-0
9.9%
10.06
2-1
9.7%
10.29
0-0
6.9%
14.56
0-1
6.7%
14.91
3-0
5.6%
17.72
1-2
5.6%
17.93
154%
1.84TOP
X24%
4.17
222%
4.63
1X78%
X246%
1276%
Probable XI4-4-2
Arixov
Süleymanly
Suliman
Haghverdi
Barker
Badolo
Mustafayev
Black
Murata
Janković
Jeam
Impact Player Hot Head
Составы ещё не объявлены — полный анализ игроков ниже
Sumqayıt29 игроков
44.3/ 99Средний
Лучшие
1Rachid MouminiDEF65
2Tiago AlmeidaDEF64
3Nikola NinkovićMID64
Safa23 игроков
49.5/ 99Средний
Лучшие
1Ibrahim AliyevFWD81
2Murad MusayevDEF75
3Dylan MertensMID59
Sumqayıt36
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Rachid Moumini65509253
Tiago Almeida64535178
Nikola Ninković64996319
Mamady Bangré60654550
Nadir Orucov58368359
Nsana Simon57265791
Jeam56406191
Renan Santana53795419
Khayal Farzullayev51509954
Marko Johansson50512850
Rufat Ahmadov49494850
Kanan Feyziyev48446743
Keffel48296560
Safa33
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Ibrahim Aliyev81886856
Murad Musayev75744977
Dylan Mertens59453591
Vusal Ganbarov59544964
Maudo Jarjué58954214
Nihad Guliyev57654056
Rauf Hüseynli57479938
Bafode Dansoko55376291
Alirza Müshtabazada51523450
Mammad Huseynov51513450
Nicat Mehbaliyev50501250
V. Pop50404367
Tiago Silva47475650
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Ibrahim AliyevSafa
88
2
Mamady BangréSumqayıt
65
3
Nihad OrujaliSumqayıt
45
4
Vüsal PaşayevSumqayıt
45
5
Luís FelipeSafa
45
Стена
Лучшие защитники
1
Maudo JarjuéSafa
95
2
Renan SantanaSumqayıt
79
3
Christopher MamengiSafa
75
4
Murad MusayevSafa
74
5
Nihad GuliyevSafa
65
Контроль
Лучшие плеймейкеры
1
Nikola NinkovićSumqayıt
99
2
Emil MustafayevSumqayıt
64
3
İsmayıl ZülfuqarlıSafa
54
4
Vusal GanbarovSafa
54
5
Ronaldo VásquezSumqayıt
52
Хулиганы
Склонны к карточкам
1
Easah SulimanSumqayıt
1
2
Lucas BlackSumqayıt
1
3
Nihad AhmadzadaSumqayıt
1
4
Ronaldo VásquezSumqayıt
1
5
Sabuhi AbdullazadaSumqayıt
1
Рейтинг силы
Сильнейшие в целом
1
Ibrahim AliyevSafa
81
2
Murad MusayevSafa
75
3
Rachid MouminiSumqayıt
65
4
Tiago AlmeidaSumqayıt
64
5
Nikola NinkovićSumqayıt
64

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Sumqayıt vs Safa 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 Sumqayıt vs Safa 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 Sumqayıt 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 Sumqayıt vs Safa 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 Sumqayıt 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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