· Premier League
Turan
ATT
5/10
DEF
7/10
vs
Sabah
10/10
ATT
8/10
DEF
Programmata· Fri 30 Oct · 14:30
Migliore scelta
Vittoria in trasferta
60%
Risultato esatto
Eccellente
0-1
13.8%
7.24
Eccellente
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
Formazioni non ancora annunciate — analisi completa della rosa sotto
Turan27 giocatori
49.5/ 99Medio
Migliori
1Haiderson HurtadoDEF88
2Jô BatistaFWD79
3Ibrahima WadjiFWD74
Sabah28 giocatori
53.8/ 99Medio
Migliori
1Joy-Lance MickelsFWD95
2Aleksey IsayevMID89
3Orphé MbinaFWD76
Turan29
PlayerValutazioneImpattoAggressivitàDisciplina
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
Sabah33
PlayerValutazioneImpattoAggressivitàDisciplina
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
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Joy-Lance MickelsSabah
99
2
Orphé MbinaSabah
86
3
Jô BatistaTuran
78
4
Ibrahima WadjiTuran
69
5
Khayal AliyevSabah
60
Muro
Migliori difensori
1
Haiderson HurtadoTuran
85
2
F. DamjanovićTuran
68
3
Roderick MillerTuran
60
4
Steve SolvetSabah
57
5
Faiq HacıyevTuran
56
Controllo
Migliori registi
1
Aleksey IsayevSabah
99
2
Du QueirozSabah
74
3
Alejandro SerranoTuran
68
4
Rahim SadikhovTuran
49
5
Ivan LepinjicaSabah
49
Cattivi ragazzi
Più propensi ai cartellini
1
F. DamjanovićTuran
1
2
Xayal NacafovTuran
1
3
Aleksa JankovićTuran
1
4
Josh GinnellyTuran
1
5
Rodrigo FernandesSabah
1
Classifica potenza
Più forti in assoluto
1
Joy-Lance MickelsSabah
95
2
Aleksey IsayevSabah
89
3
Haiderson HurtadoTuran
88
4
Jô BatistaTuran
79
5
Orphé MbinaSabah
76

Domande frequenti

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