· Premier League
Neftçi
ATT
9/10
DEF
5/10
vs
Sabah FC
10/10
ATT
7/10
DEF
Naplánováno· Fri 9 Oct · 15:30
Nejlepší tip
OBÁ SKÓRUJÍ
62%
Přesný výsledek
Výborná
1-1
10.8%
9.27
Výborná
2-1
8.8%
11.31
1-2
7.9%
12.68
1-0
7.4%
13.56
0-1
6.6%
15.21
2-2
6.5%
15.47
2-0
6%
16.53
3-1
4.8%
20.69
142%
2.38TOP
X24%
4.21
234%
2.92
1X66%
X258%
1276%
Probable XI4-4-2
Balayev
Gravillon
Şirinov
Badalov
Sacko
Camalov
Safarov
Mahmudov
Klismahn
Akinyemi
Salyanskiy
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Neftçi28 hráči
48.8/ 99Průměrný
Nejlepší
1Emin Mahmudov MID87
2Agadadas SalyanskiyFWD75
3Breno AlmeidaFWD69
Sabah FC27 hráči
54.4/ 99Průměrný
Nejlepší
1Joy-Lance MickelsFWD93
2Aleksey IsayevMID85
3Du QueirozMID81
Neftçi34
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Emin Mahmudov 87993684
Agadadas Salyanskiy75907132
Breno Almeida69624889
Bassala Sambou6591555
Rufat Abbasov64428475
Adeleke Akinyemi63713950
Ifeanyi Mathew62495077
Igor Ribeiro60526458
Moustapha Seck58279585
Emil Balayev5759950
F. Vargas54593648
Andreaw Gravillon53524161
Emil Safarov53397057
Sabah FC33
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Joy-Lance Mickels93993891
Aleksey Isayev85993877
Du Queiroz81706889
Orphé Mbina76871972
Steve Solvet72605977
Ivan Lepinjica66556270
Abdulakh Khaybulayev65429988
Kaheem Parris64575478
A. McCarthy62545172
Tellur Mütallimov62385389
Abdulla Rzayev55225991
Ravan Mirzammadov54563450
Aaron Malouda54583950
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Joy-Lance MickelsSabah FC
99
2
Bassala SambouNeftçi
91
3
Agadadas SalyanskiyNeftçi
90
4
Orphé MbinaSabah FC
87
5
Adeleke AkinyemiNeftçi
71
Zeď
Nejlepší obránci
1
Steve SolvetSabah FC
60
2
A. McCarthySabah FC
54
3
Andreaw GravillonNeftçi
52
4
Igor RibeiroNeftçi
52
5
Elvin BadalovNeftçi
45
Kontrola
Nejlepší tvůrci hry
1
Emin Mahmudov Neftçi
99
2
Aleksey IsayevSabah FC
99
3
Du QueirozSabah FC
70
4
Vicko SeveljNeftçi
57
5
Ivan LepinjicaSabah FC
55
Zlobivci
Nejpravděpodobnější žlutá karta
1
Elvin CamalovNeftçi
1
2
Gustavo KlismahnNeftçi
1
3
Sessi Octave Emile D'AlmeidaNeftçi
1
4
Rahman DashdamirovSabah FC
1
5
Rodrigo FernandesSabah FC
1
Žebříček síly
Nejsilnější celkově
1
Joy-Lance MickelsSabah FC
93
2
Emin Mahmudov Neftçi
87
3
Aleksey IsayevSabah FC
85
4
Du QueirozSabah FC
81
5
Orphé MbinaSabah FC
76

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Neftçi vs Sabah 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 Neftçi vs Sabah 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 Neftçi 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 Neftçi vs Sabah 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 Neftçi 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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