· Premier League
Turan Tovuz
ATT
5/10
DEF
7/10
vs
Sabah FC
10/10
ATT
8/10
DEF
Naplánováno· Fri 30 Oct · 14:30
Nejlepší tip
Hostující výhra
60%
Přesný výsledek
Výborná
0-1
13.8%
7.24
Výborná
0-2
12.1%
8.28
1-1
10.9%
9.16
1-2
9.5%
10.48
0-0
7.9%
12.66
0-3
7%
14.20
1-0
6.2%
16.03
1-3
5.6%
17.97
117%
6.03
X23%
4.31
260%
1.66TOP
1X40%
X283%
1277%
Probable XI4-4-2
Keniaykin
Marandici
Hackman
Damjanović
Hacıyev
Serrano
Tavares
Sadikhov
Olabe
Janković
Balau
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Turan Tovuz27 hráči
49.5/ 99Průměrný
Nejlepší
1Haiderson HurtadoDEF88
2Jô BatistaFWD79
3Ibrahima WadjiFWD74
Sabah FC28 hráči
53.8/ 99Průměrný
Nejlepší
1Joy-Lance MickelsFWD95
2Aleksey IsayevMID89
3Orphé MbinaFWD76
Turan Tovuz29
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Haiderson Hurtado88856678
Jô Batista79789263
Ibrahima Wadji74696376
Rahim Sadikhov65496970
Alejandro Serrano62683468
Faiq Hacıyev61569642
Denis Marandici56496356
Jorge Silva55299965
Veysal Rzayev55415867
O. Baklov52536850
Alexey Keniaykin50499952
Diogo Balau50349075
Rufat Ahmadov49494850
Sabah FC33
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Joy-Lance Mickels95994591
Aleksey Isayev89994284
Orphé Mbina76861972
Steve Solvet69575976
Abdulakh Khaybulayev68469990
Ivan Lepinjica67495981
Kaheem Parris66575683
A. McCarthy61456965
Tellur Mütallimov60384889
Japhet Dungus Bulus59476561
Ravan Mirzammadov56582850
Stas Pokatilov5556950
Aaron Malouda54583950
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Joy-Lance MickelsSabah FC
99
2
Orphé MbinaSabah FC
86
3
Jô BatistaTuran Tovuz
78
4
Ibrahima WadjiTuran Tovuz
69
5
Khayal AliyevSabah FC
60
Zeď
Nejlepší obránci
1
Haiderson HurtadoTuran Tovuz
85
2
F. DamjanovićTuran Tovuz
68
3
Roderick MillerTuran Tovuz
60
4
Steve SolvetSabah FC
57
5
Faiq HacıyevTuran Tovuz
56
Kontrola
Nejlepší tvůrci hry
1
Aleksey IsayevSabah FC
99
2
Du QueirozSabah FC
74
3
Alejandro SerranoTuran Tovuz
68
4
Rahim SadikhovTuran Tovuz
49
5
Ivan LepinjicaSabah FC
49
Zlobivci
Nejpravděpodobnější žlutá karta
1
F. DamjanovićTuran Tovuz
1
2
Xayal NacafovTuran Tovuz
1
3
Aleksa JankovićTuran Tovuz
1
4
Josh GinnellyTuran Tovuz
1
5
Rodrigo FernandesSabah FC
1
Žebříček síly
Nejsilnější celkově
1
Joy-Lance MickelsSabah FC
95
2
Aleksey IsayevSabah FC
89
3
Haiderson HurtadoTuran Tovuz
88
4
Jô BatistaTuran Tovuz
79
5
Orphé MbinaSabah FC
76

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Turan Tovuz 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 Turan Tovuz 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 Turan Tovuz 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 Turan Tovuz 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 Turan Tovuz 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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