· Superliga
CFR Cluj
ATT
6/10
DEF
7/10
0:0
Universitatea Cluj
6/10
ATT
6/10
DEF
Programmata· Thu 8 Oct · 17:30
Migliore scelta
Sotto 2.5
53%
Risultato esatto
Eccellente
1-0
12.1%
8.24
Eccellente
1-1
12%
8.32
2-0
9.5%
10.49
2-1
9.4%
10.60
0-0
7.7%
12.95
0-1
7.6%
13.08
1-2
5.9%
16.82
3-0
5%
20.03
151%
1.97TOP
X25%
3.95
224%
4.19
1X76%
X249%
1275%
Probable XI4-4-2
Moreira
Radu
Țîrlea
Abeid
Kresic
Nalić
Ziblim
Păun
Fică
Ferenți
Cordea
Impact Player Hot Head
Formazioni non ancora annunciate — analisi completa della rosa sotto
CFR Cluj42 giocatori
54.9/ 99Medio
Migliori
1Nicolae SulaFWD83
2Karlo MuharMID79
3Andrei CordeaFWD77
Universitatea Cluj36 giocatori
47.0/ 99Medio
Migliori
1Andrej FabryMID75
2Andrei CoubișDEF67
3Florent PouloloDEF67
CFR Cluj64
PlayerValutazioneImpattoAggressivitàDisciplina
Nicolae Sula83994550
Karlo Muhar79748567
Andrei Cordea77773491
Sergiu Buş72742591
Aly Abeid70565187
Viktor Kun70548669
Léo Bolgado69595279
Francisco Barrios67646661
Christopher Braun66488665
Alin Fică64584971
Ovidiu Perianu64386291
Meriton Korenica64516791
A. Nalić61605161
Universitatea Cluj53
PlayerValutazioneImpattoAggressivitàDisciplina
Andrej Fabry75605191
Andrei Coubiș67782976
Florent Poulolo67704269
Dorin Codrea67538967
Iulian Cristea59573773
Taiwo Olakunle58624550
Edvinas Gertmonas5759950
Adams Friday56236491
Miguel Silva55395471
Jovo Lukic54564653
Marius Ștefănescu54616733
Neofytos Michail52523950
Elio Capradossi52692449
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Nicolae SulaCFR Cluj
99
2
Andrei CordeaCFR Cluj
77
3
Sergiu BuşCFR Cluj
74
4
Taiwo OlakunleUniversitatea Cluj
62
5
Marius ȘtefănescuUniversitatea Cluj
61
Muro
Migliori difensori
1
Marian HujaCFR Cluj
90
2
Andrei CoubișUniversitatea Cluj
78
3
Florent PouloloUniversitatea Cluj
70
4
Elio CapradossiUniversitatea Cluj
69
5
Jonathan CisséUniversitatea Cluj
69
Controllo
Migliori registi
1
Karlo MuharCFR Cluj
74
2
Francisco BarriosCFR Cluj
64
3
Ovidiu BicUniversitatea Cluj
62
4
A. NalićCFR Cluj
60
5
Andrej FabryUniversitatea Cluj
60
Cattivi ragazzi
Più propensi ai cartellini
1
Marian HujaCFR Cluj
1
2
Dan NistorUniversitatea Cluj
1
3
Pedro PinhoUniversitatea Cluj
3
4
Alessandro MurgiaUniversitatea Cluj
9
5
Oucasse MendyUniversitatea Cluj
11
Classifica potenza
Più forti in assoluto
1
Nicolae SulaCFR Cluj
83
2
Karlo MuharCFR Cluj
79
3
Andrei CordeaCFR Cluj
77
4
Andrej FabryUniversitatea Cluj
75
5
Sergiu BuşCFR Cluj
72

Domande frequenti

Our Poisson model calculates the probability of every possible scoreline for CFR Cluj vs Universitatea Cluj 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 CFR Cluj vs Universitatea Cluj 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 CFR Cluj 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 CFR Cluj vs Universitatea Cluj 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 CFR Cluj 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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