AI DNA Similarity
Best Alternatives to Nick Runderkamp
Players most similar to Nick Runderkamp (Midfielder, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Nick Runderkamp
- 1.Darko Lazović98% DNA match·Al Wahda
- 2.Fatih Kamaci97% DNA match·Dongen
- 3.Mitchel Van Rosmalen96% DNA match·Blauw Geel '38
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
AssistsTop 18%
???Bottom 0%
“A Midfielder....”
See Full Verdict + Share Card →Playing Style Analysis
A Midfielder. Statistically, he stands out as a reliable supplier (0.22 assists/90). The three most similar players to Nick Runderkamp by playing style are:
- Darko Lazović(98% match) — A Midfielder. Statistically, he stands out as a capable chance creator (1.2 key passes/90) and a reliable supplier (0.18 assists/90).
- Fatih Kamaci(97% match) — A Midfielder. Statistically, he stands out as a proven goalscorer (0.43 goals/90) and a reliable supplier (0.17 assists/90).
- Mitchel Van Rosmalen(96% match) — A Midfielder. Statistically, he stands out as a reliable supplier (0.18 assists/90). Note: this profile is based on 503 minutes of playing time this season.
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
D
Darko Lazović
Al Wahda · Serie A
Serbia35y
KP/901.07
G/900.12
vs Runderkamp: 6y older
98% match
N/A
#2
F
Fatih Kamaci
Dongen · Eredivisie
Netherlands36y
KP/900.00
G/900.43
vs Runderkamp: 7y older
97% match
N/A
#3
M
Mitchel Van Rosmalen
Blauw Geel '38 · Eredivisie
Netherlands24y
KP/900.72
G/900.00
Small Sample
vs Runderkamp: 5y younger
96% match
N/A
#4
H
Hjalte Toftegaard
Hillerød · Superliga
Denmark19y
KP/903.52
G/900.00
Small Sample
96% match
N/A
#5
M
Marcel Wenig
Ulm · Bundesliga
Germany22yContract 2027
KP/900.00
G/900.17
Small Sample
96% match
N/A
#6
B
B. Kücükylidiz
Hillerød · Superliga
Denmark25y
KP/900.00
G/900.00
Small Sample
96% match
N/A
#7
D
Diogo Ferreira Prioste
Benfica · Liga Portugal
Portugal22y
KP/901.18
G/900.00
Small Sample
95% match
N/A
#8
T
Tobias Blidegn
B 93 · Superliga
Denmark21y
KP/900.97
G/900.09
Balanced Midfielder
Last 5: ↑ Hot95% match
N/A
#9
J
Jeppe Andersen
Silkeborg IF · Superliga
Denmark33yContract 2026
KP/900.62
G/900.31
94% match
N/A
#10
F
F. Mortensen
Hobro · Superliga
Denmark27y
KP/900.38
G/900.00
94% match
N/A
#11
M
M. Nielsen
Hobro · Superliga
Denmark21y
KP/900.98
G/900.07
Balanced Midfielder
Last 5: ↑ Hot94% match
N/A
#12
F
Frederik Juul Christensen
Horsens · Superliga
Denmark26y
KP/900.44
G/900.00
94% 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 Nick Runderkamp.
Ask AI about Nick Runderkamp →Frequently Asked Questions
Who are the best alternatives to Nick Runderkamp?▼
The top alternatives to Nick Runderkamp based on AI DNA playing style analysis include: Darko Lazović, Fatih Kamaci, Mitchel Van Rosmalen, Hjalte Toftegaard, Marcel Wenig. 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 Nick Runderkamp in 2026?▼
Players with a similar profile to Nick Runderkamp in 2026 include Darko Lazović (N/A), Fatih Kamaci (N/A), Mitchel Van Rosmalen (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Nick Runderkamp play and who plays similarly?▼
Nick Runderkamp plays as a Midfielder. Players with a comparable positional profile include Darko Lazović (Serbia, N/A); Fatih Kamaci (Netherlands, N/A); Mitchel Van Rosmalen (Netherlands, N/A); Hjalte Toftegaard (Denmark, 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.