· Premier League
Sumgayit FK
ATT
6/10
DEF
3/10
vs
Safa
4/10
ATT
4/10
DEF
Scheduled· Mon 2 Nov · 15:00
Best Pick
Home Win
54%
Correct Score
Top
1-0
11.7%
8.56
Top
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
Lineups not yet announced — full squad analysis below
Sumgayit FK29 players
44.6/ 99Average
Top Rated
1Tiago AlmeidaDEF65
2Nikola NinkovićMID65
3Mamady BangréFWD60
Safa23 players
50.1/ 99Average
Top Rated
1Ibrahim AliyevFWD82
2Murad MusayevDEF77
3Dylan MertensMID60
Sumgayit FK36
PlayerOverall RatingImpactAggressionDiscipline
Tiago Almeida65545278
Nikola Ninković65996420
Mamady Bangré60654550
Nadir Orucov59378459
Nsana Simon58275891
Rachid Moumini57478448
Jeam57406291
Renan Santana54815420
Khayal Farzullayev51519954
Marko Johansson50512850
Keffel49306760
Rufat Ahmadov49494850
Imani Barker48654331
Safa33
PlayerOverall RatingImpactAggressionDiscipline
Ibrahim Aliyev82896956
Murad Musayev77765078
Dylan Mertens60463691
Vusal Ganbarov60565065
Maudo Jarjué59984214
Rauf Hüseynli59499938
Nihad Guliyev58674056
Bafode Dansoko55386391
Alirza Müshtabazada51523450
Mammad Huseynov51513450
Nicat Mehbaliyev50511250
V. Pop50414467
Tiago Silva48475650
Player Rankings
Snipers
Top goal threats
1
Ibrahim AliyevSafa
89
2
Mamady BangréSumgayit FK
65
3
Luís FelipeSafa
46
4
Nihad OrujaliSumgayit FK
45
5
Vüsal PaşayevSumgayit FK
45
Wall
Top defenders
1
Maudo JarjuéSafa
98
2
Renan SantanaSumgayit FK
81
3
Christopher MamengiSafa
78
4
Murad MusayevSafa
76
5
Nihad GuliyevSafa
67
Control
Top playmakers
1
Nikola NinkovićSumgayit FK
99
2
Emil MustafayevSumgayit FK
65
3
Vusal GanbarovSafa
56
4
İsmayıl ZülfuqarlıSafa
54
5
Ronaldo VásquezSumgayit FK
52
Bad Boys
Most likely to get booked
1
Easah SulimanSumgayit FK
1
2
Nihad AhmadzadaSumgayit FK
1
3
Ronaldo VásquezSumgayit FK
1
4
Sabuhi AbdullazadaSumgayit FK
1
5
Christopher MamengiSafa
1
Power Rankings
Strongest overall
1
Ibrahim AliyevSafa
82
2
Murad MusayevSafa
77
3
Tiago AlmeidaSumgayit FK
65
4
Nikola NinkovićSumgayit FK
65
5
Mamady BangréSumgayit FK
60

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Sumgayit FK 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 Sumgayit FK 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 Sumgayit FK 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 Sumgayit FK 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 Sumgayit FK 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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