AI DNA Similarity
Best Alternatives to Igor Vekic
Players most similar to Igor Vekic (Goalkeeper, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
A Commanding Keeper in Superliga. Statistically, he stands out as dominant in aerial duels (85% success), reliable in goal (3.2 saves/90) and commands the box with authority (0.8 punches/90). The three most similar players to Igor Vekic by playing style are:
- Mattias Lamhauge(99% match) — A Commanding Keeper in Superliga. Statistically, he stands out as dominant in aerial duels (75% success), exceptionally busy shot-stopper (4.3 saves/90) and commands the box with authority (0.7 punches/90).
- Frederik Ibsen(99% match) — A Commanding Keeper in Superliga. Statistically, he stands out as dominant in aerial duels (100% success), exceptionally busy shot-stopper (4.2 saves/90) and commands the box with authority (0.6 punches/90).
- Kasper Kiilerich(99% match) — A Commanding Keeper in Superliga. Statistically, he stands out as dominant in aerial duels (100% success), reliable in goal (3.2 saves/90) and commands the box with authority (0.5 punches/90).
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
Mattias Lamhauge
Fredericia · Superliga
Faroe Islands26y
Tkl/900.08
Commanding Keeper
99% match
N/A
#2
F
Frederik Ibsen
B 93 · Superliga
Denmark28y
Tkl/900.14
Commanding Keeper
99% match
N/A
#3
K
Kasper Kiilerich
Aarhus Fremad · Superliga
Denmark20y
Tkl/900.00
Commanding Keeper
99% match
N/A
#4
A
A. Danko
Kolding IF · Superliga
Slovakia22y
Tkl/900.00
Commanding KeeperSmall Sample
98% match
N/A
#5
L
Lucas Lund
Viborg FF · Superliga
Denmark26yContract 2027
Tkl/900.08
Traditional Keeper
97% match
N/A
#6
M
Matej Delac
Horsens · Superliga
Croatia33yContract 2027
Tkl/900.07
Traditional Keeper
96% match
N/A
#7
A
Aris Vaporakis
B 93 · Superliga
Denmark31y
Tkl/900.00
Sweeper-Keeper
96% match
N/A
#8
A
Andreas Hermansen
Horsens · Superliga
Denmark21y
Tkl/900.00
Reliable KeeperSmall Sample
96% match
N/A
#9
W
William Lykke
Nordsjælland · Superliga
Denmark21yContract 2028
Tkl/900.25
Traditional KeeperSmall Sample
95% match
N/A
#10
A
A. Søndergaard
Hobro · Superliga
Denmark25y
Tkl/900.19
Traditional KeeperSmall Sample
95% match
N/A
#11
P
Paul Izzo
Randers FC · Superliga
Australia31yContract 2027
Tkl/900.10
Sweeper-Keeper
95% match
N/A
#12
D
Dominik Reimann
Magdeburg · Bundesliga
Germany28y
Tkl/900.00
Commanding Keeper
95% 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 Igor Vekic.
Ask AI about Igor Vekic →Frequently Asked Questions
Who are the best alternatives to Igor Vekic?▼
The top alternatives to Igor Vekic based on AI DNA playing style analysis include: Mattias Lamhauge, Frederik Ibsen, Kasper Kiilerich, A. Danko, Lucas Lund. 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 Igor Vekic in 2026?▼
Players with a similar profile to Igor Vekic in 2026 include Mattias Lamhauge (N/A), Frederik Ibsen (N/A), Kasper Kiilerich (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Igor Vekic play and who plays similarly?▼
Igor Vekic plays as a Goalkeeper. Players with a comparable positional profile include Mattias Lamhauge (Faroe Islands, N/A); Frederik Ibsen (Denmark, N/A); Kasper Kiilerich (Denmark, N/A); A. Danko (Slovakia, 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.