AI DNA Similarity
Best Alternatives to S. Zdjelar
Players most similar to S. Zdjelar (Midfielder, €1.3M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to S. Zdjelar
- 1.Martin Spelmann79% DNA match·Hvidovre
- 2.Gerry Vlak80% DNA match·Koninklijke HFC
- 3.Jeremiah Mensah79% DNA match·Bayer 04 Leverkusen
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
???Bottom 0%
“Saša Zdjelar is a Midfielder....”
See Full Verdict + Share Card →Playing Style Analysis
Small Sample
Saša Zdjelar is a Midfielder. Solid midfield option. Note: this profile is based on 751 minutes of playing time this season. The three most similar players to S. Zdjelar by playing style are:
- Martin Spelmann(79% match) — Martin Spelmann is a Midfielder. Solid midfield option. Statistically, he stands out as a regular goalscorer (0.26 goals/90).
- Gerry Vlak(80% match) — Gerry Vlak is a Midfielder. Solid midfield option. (Limited sample: 378 mins)
- Jeremiah Mensah(79% match) — Jeremiah Dennit Mensah is a Midfielder. Solid midfield option. (Limited sample: 327 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
M
Martin Spelmann
Hvidovre · Superliga
Denmark39y
KP/900.40
G/900.00
Small Sample
vs Zdjelar: 8y older
79% match
N/A
#2
G
Gerry Vlak
Koninklijke HFC · Eredivisie
Netherlands30y
KP/900.00
G/900.00
Small Sample
80% match
N/A
#3
J
Jeremiah Mensah
Bayer 04 Leverkusen · Bundesliga
Germany18y
KP/900.00
G/900.00
Small Sample
vs Zdjelar: 13y younger
79% match
N/A
#4
M
Maurizio Brenna
Katwijk · Eredivisie
Netherlands27y
KP/900.00
G/900.00
Small Sample
79% match
N/A
#5
S
Salvador Blopa
Sporting CP · Liga Portugal
Portugal18y
KP/900.00
G/900.00
Small Sample
79% match
N/A
#6
R
Rick van der Meer
Katwijk · Eredivisie
Netherlands28y
KP/900.00
G/900.00
Small Sample
79% match
N/A
#7
D
Doni Arifi
SpVgg Greuther Fürth · Bundesliga
Finland24y
KP/901.12
G/900.00
Small Sample
Last 5: ↓ Dip78% match
N/A
#8
A
Aljaz Casar
MSV Duisburg · Bundesliga
Slovenia25y
KP/900.65
G/900.00
Box-to-BoxSmall Sample
78% match
N/A
#9
Y
Yann Rollim
Caxias · Superliga
Brazil31y
KP/900.64
G/900.21
Small Sample
77% match
N/A
#10
J
Joseph Nduquidi
Metz · Ligue 1
France21y
KP/900.15
G/900.00
Balanced MidfielderSmall Sample
Last 5: ↓ Dip78% match
N/A
#11
K
Kiano Dyer
FC Volendam · Eredivisie
England19y
KP/900.00
G/900.00
Small Sample
78% match
N/A
#12
U
Uros Racic
Aris · Liga Portugal
Serbia28y
KP/901.18
G/900.12
Box-to-BoxSmall Sample
Last 5: ↑ Hot77% match
€3.0M
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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 S. Zdjelar.
Ask AI about S. Zdjelar →Frequently Asked Questions
Who are the best alternatives to S. Zdjelar?▼
The top alternatives to S. Zdjelar based on AI DNA playing style analysis include: Martin Spelmann, Gerry Vlak, Jeremiah Mensah, Maurizio Brenna, Salvador Blopa. 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 S. Zdjelar in 2026?▼
Players with a similar profile to S. Zdjelar in 2026 include Martin Spelmann (N/A), Gerry Vlak (N/A), Jeremiah Mensah (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does S. Zdjelar play and who plays similarly?▼
S. Zdjelar plays as a Midfielder. Players with a comparable positional profile include Martin Spelmann (Denmark, N/A); Gerry Vlak (Netherlands, N/A); Jeremiah Mensah (Germany, N/A); Maurizio Brenna (Netherlands, 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.