AI DNA Similarity
Best Alternatives to Mario Cuze
Players most similar to Mario Cuze (Attacker, €300K) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Mario Cuze
- 1.Nikola Gjorgjev85% DNA match·Farense
- 2.Vaclav Jurecka84% DNA match·Baník Ostrava
- 3.Dani van der Moot85% DNA match·Katwijk
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
Inside ForwardProlific
Mario Ćuže is a Inside Forward. Wide attacker who cuts inside to score. Statistically, he stands out as a proven goalscorer (0.41 goals/90) and top 20% scorer in the league. The three most similar players to Mario Cuze by playing style are:
- Nikola Gjorgjev(85% match) — Nikola Gjorgjev is a Forward. Versatile attacking option. (Limited sample: 337 mins)
- Vaclav Jurecka(84% match) — Václav Jurečka is a Forward. Versatile attacking option. However, he loses possession under pressure (1.8 dispossessed/90).
- Dani van der Moot(85% match) — Dani van der Moot is a Forward. Versatile attacking option. Statistically, he stands out as a regular goalscorer (0.22 goals/90) and a reliable supplier (0.22 assists/90). (Limited sample: 414 mins)
Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →
Similar Players — Ranked by DNA Similarity
#1
N
Nikola Gjorgjev
Farense · Liga Portugal
Switzerland28y
G/900.00
A/900.00
Small Sample
85% match
N/A
#2
V
Vaclav Jurecka
Baník Ostrava · Super Lig
Czech Republic31y
G/900.19
A/900.00
Small Sample
Last 5: ↓ Dipvs Cuze: 4y older · -0.13 G/90
84% match
N/A
#3
D
Dani van der Moot
Katwijk · Eredivisie
Netherlands29y
G/900.22
A/900.22
Small Sample
vs Cuze: 2y older · -0.10 G/90
85% match
N/A
#4
F
Fer Niño
Burgos · La Liga
Spain25y
G/900.23
A/900.00
Target ManSmall Sample
Last 5: → Stable84% match
N/A
#5
R
Rober González
NEC Nijmegen · Eredivisie
Spain25y
G/900.18
A/900.18
83% match
N/A
#6
M
Martijn Berden
GVVV · Eredivisie
Netherlands28y
G/900.00
A/900.34
Small Sample
83% match
N/A
#7
M
Michiel Kramer
RKC Waalwijk · Eredivisie
Netherlands37y
G/900.39
A/900.00
Small Sample
83% match
N/A
#8
R
Riad Bajic
AEK Larnaca · Super Lig
Bosnia and Herzegovina32yContract 2027
G/900.46
A/900.15
82% match
€5.5M
#9
E
Enzo Stroo
Lisse · Eredivisie
Netherlands31y
G/900.00
A/900.22
Small Sample
83% match
N/A
#10
I
Ivan Lima
Benfica · Liga Portugal
Portugal21y
G/900.21
A/900.21
Small Sample
Last 5: ↓ Dip83% match
N/A
#11
A
Anders Holvad
Aarhus Fremad · Superliga
Denmark35y
G/900.23
A/900.00
Small Sample
Last 5: ↓ Dip83% match
N/A
#12
M
Michael Breij
Roda JC Kerkrade · Eredivisie
Netherlands29y
G/900.58
A/900.17
82% match
N/A
⬡
Ask AI: Why are these players similar?
Our 768-dimension Player DNA model matches playing style, physical profile, pressing intensity, and tactical fit. Ask the AI to explain exactly what makes these players statistically similar to Mario Cuze.
Ask AI about Mario Cuze →Frequently Asked Questions
Who are the best alternatives to Mario Cuze?▼
The top alternatives to Mario Cuze based on AI DNA playing style analysis include: Nikola Gjorgjev, Vaclav Jurecka, Dani van der Moot, Fer Niño, Rober González. These players were matched using Rising Transfers' 768-dimension DNA model across playing style, pressing intensity, and tactical fit — not just position or market value.
Which players are similar to Mario Cuze in 2026?▼
Players with a similar profile to Mario Cuze in 2026 include Nikola Gjorgjev (N/A), Vaclav Jurecka (N/A), Dani van der Moot (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mario Cuze play and who plays similarly?▼
Mario Cuze plays as a Attacker. Players with a comparable positional profile include Nikola Gjorgjev (Switzerland, N/A); Vaclav Jurecka (Czech Republic, N/A); Dani van der Moot (Netherlands, N/A); Fer Niño (Spain, N/A).
How does Rising Transfers find similar players?▼
Rising Transfers uses a proprietary 768-dimension Player DNA model trained on 3.2 million match events. Each player is represented as a vector across 35+ per-90 metrics including pressing intensity, passing footprint, dribbling profile, and defensive contribution. Similarity is measured using cosine distance — the same technique used in state-of-the-art AI systems — making it the most precise player comparison tool available publicly.