Dinamo’s +32% corner attack and “very often” winning trend give this Europa League clash a strong set-piece pulse. NEC also create corners above average (+10%) and win them frequently, so both sides should keep testing the flanks as tournament pressure builds. Their solid corner defenses may slow the count, but the attacking momentum points toward Over 8.5.
Corners Over/UnderOver 8.5
Best Pick
BTTS
57%
Correct Score
Top
1-1
10.9%
8.00
Top
2-1
9.7%
8.50
1-0
9.5%
9.50
2-0
8.4%
9.50
1-2
6.3%
17.00
0-1
6.1%
19.00
3-1
5.8%
12.00
2-2
5.6%
13.00
152%
1.61TOP
X23%
4.20
225%
5.00
1X75%
X248%
1277%
Combined Bet Builder
5 legs
Dinamo’s 2.15 xG and NEC’s +43% attack power point to goals at both ends; combined 3.32 xG supports over 2.5, with Dinamo’s 1.09 first-half xG backing an early goal. Their 9.17 expected corners clear 8.5, while Dion Drena Beljo looks a strong anytime scorer candidate.
1Match GoalsOver 3.5
2Both Teams to ScoreYes
3First Half Over/UnderOver 0.5
4Corners Over/UnderOver 8.5
5GoalscorersAnytime
Confirmed XI4-4-2
24
Kotarski
15
Galesic
36
Domínguez
26
McKenna
22
Vinlöf
27
Misic
23
Almena
10
Stojkovic
17
Ivanušec
11
Hoxha
9
Beljo
Impact Player Hot Head
GNK Dinamo34 players
52.1/ 99Average
Top Rated
1Niko GalesicDEF75
2Dion BeljoFWD73
3Ivan FilipovicGK68
NEC Nijmegen33 players
50.7/ 99Average
Top Rated
1Jamiro MonteiroMID79
2Darko NejašmićMID78
3Dennis GeigerMID76
Player Rankings
Snipers
Top goal threats
1
Dion BeljoGNK Dinamo
81
2
K. SierhuisNEC Nijmegen
75
3
Dani RodríguezGNK Dinamo
60
4
Koki OgawaNEC Nijmegen
57
5
Mislav OrsicGNK Dinamo
53
Wall
Top defenders
1
Bram NuytinckNEC Nijmegen
76
2
Raúl Torrente GNK Dinamo
68
3
Sergi DomínguezGNK Dinamo
68
4
Scott McKennaGNK Dinamo
65
5
Stjepan RadeljicGNK Dinamo
64
Control
Top playmakers
1
Darko NejašmićNEC Nijmegen
82
2
Kodai SanoNEC Nijmegen
69
3
Jamiro MonteiroNEC Nijmegen
68
4
Josip MisicGNK Dinamo
67
5
Dennis GeigerNEC Nijmegen
64
Bad Boys
Most likely to get booked
1
Monsef BakrarGNK Dinamo
1
Aggression 43
2
Vito van CrooijNEC Nijmegen
1
Aggression 42
3
Ahmetcan KaplanNEC Nijmegen
9
Aggression 69
4
Almugera KabarNEC Nijmegen
9
Aggression 74
5
Philippe SandlerNEC Nijmegen
9
Aggression 55
Power Rankings
Strongest overall
1
Jamiro MonteiroNEC Nijmegen
79
2
Darko NejašmićNEC Nijmegen
78
3
Dennis GeigerNEC Nijmegen
76
4
Niko GalesicGNK Dinamo
75
5
Kodai SanoNEC Nijmegen
75
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for GNK Dinamo vs NEC Nijmegen 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 GNK Dinamo vs NEC Nijmegen 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 GNK Dinamo 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 GNK Dinamo vs NEC Nijmegen 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 GNK Dinamo 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.