· Liga 2
CS Dinamo București
ATT
3/10
DEF
3/10
2:2
CSM Satu Mare
4/10
ATT
5/10
DEF
Full-time
Best Pick
Under 2.5
53%
Correct Score94′
Top
1-1
12.6%
7.50
Top
1-0
10.5%
17.00
0-1
9.2%
8.50
2-1
8.6%
17.00
0-0
7.6%
13.00
1-2
7.5%
8.50
2-0
7.2%
29.00
0-2
5.5%
8.50
141%
2.45TOP
X26%
3.78
233%
3.06
1X67%
X259%
1274%
xG — Expected Goals
CS Dinamo București
CS Dinamo București exp
CSM Satu Mare
CSM Satu Mare exp
goal
per 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
CS Dinamo București
CSM Satu Mare
Probable XI4-4-2
Oncescu
Demici
Rus
Anghel
Dumbravean
Alexandru
Renta
Gogescu
Leață
Sîrbu
Peter
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
CS Dinamo București13 players
51.9/ 99Average
Top Rated
1Adrian SîrbuFWD69
2Rareș CernamorițDEF60
3Ayan AnghelDEF58
CSM Satu Mare13 players
56.5/ 99Strong
Top Rated
1Vadim CălugherMID87
2Matei MarinDEF66
3Cătălin OaneaDEF64
CS Dinamo București20
PlayerOverall RatingImpactAggressionDiscipline
Adrian Sîrbu69756050
Rareș Cernamoriț60734150
Ayan Anghel58635550
George Leață54731750
Roberto Mălăele53555350
Andrei Rus52574550
Denis Renta51505250
Denis Oncescu50504550
George Gogescu49514550
Mircea-Mario Tache48533450
Ionuț Mitran 47415650
Bogdan Alexandru43267750
Alin Demici41325150
CSM Satu Mare29
PlayerOverall RatingImpactAggressionDiscipline
Vadim Călugher87999250
Matei Marin66598750
Cătălin Oanea64597850
Ervin Zsiga61783250
Dorian Andrei Todoran57566350
Mircea Donca56624950
Alexandru Sabău54535750
Paul Copaci52593950
Mark Karacsony49494550
José Elo48523950
Albert Hofman48484550
Ionuţ Pop46454550
Alin Văsălie46356750
Player Rankings
Snipers
Top goal threats
1
Adrian SîrbuCS Dinamo București
75
2
Albert HofmanCSM Satu Mare
48
Wall
Top defenders
1
Rareș CernamorițCS Dinamo București
73
2
Ayan AnghelCS Dinamo București
63
3
Cătălin OaneaCSM Satu Mare
59
4
Matei MarinCSM Satu Mare
59
5
Paul CopaciCSM Satu Mare
59
Control
Top playmakers
1
Vadim CălugherCSM Satu Mare
99
2
Ervin ZsigaCSM Satu Mare
78
3
George LeațăCS Dinamo București
73
4
Mircea DoncaCSM Satu Mare
62
5
Roberto MălăeleCS Dinamo București
55
Bad Boys
Most likely to get booked
1
Denis OncescuCS Dinamo București
50
2
Alin DemiciCS Dinamo București
50
3
Andrei RusCS Dinamo București
50
4
Ayan AnghelCS Dinamo București
50
5
Ionuț Mitran CS Dinamo București
50
Power Rankings
Strongest overall
1
Vadim CălugherCSM Satu Mare
87
2
Adrian SîrbuCS Dinamo București
69
3
Matei MarinCSM Satu Mare
66
4
Cătălin OaneaCSM Satu Mare
64
5
Ervin ZsigaCSM Satu Mare
61

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for CS Dinamo București vs CSM Satu Mare 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 CS Dinamo București vs CSM Satu Mare 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 CS Dinamo București 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 CS Dinamo București vs CSM Satu Mare 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 CS Dinamo București 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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