AI DNA Similarity
Best Alternatives to Grad Damen
Players most similar to Grad Damen (Midfielder, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
A Creator in KNVB Beker. Statistically, he stands out as a capable chance creator (1.2 key passes/90) and reads the game exceptionally (1.8 interceptions/90). The three most similar players to Grad Damen by playing style are:
- Jesse Schuurman(94% match) — A Balanced Midfielder in KNVB Beker. Statistically, he stands out as reads the game exceptionally (1.9 interceptions/90) and wins the physical battle (60% duel success).
- S. van Doorm(94% match) — A Box-to-Box in KNVB Beker. Statistically, he stands out as active in the tackle (2.2 tackles/90) and reads the game exceptionally (1.5 interceptions/90).
- Rick Dekker(93% match) — A Ball-Winner in KNVB Beker. Statistically, he stands out as an aggressive ball-winner (3.0 tackles/90), reads the game exceptionally (1.8 interceptions/90), wins the physical battle (55% duel success) and top 10% tackler in the league.
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
J
Jesse Schuurman
IJsselmeervogels · Eredivisie
Netherlands28y
KP/900.30
G/900.00
Balanced Midfielder
94% match
N/A
#2
S
S. van Doorm
HHC · Eredivisie
Netherlands28y
KP/900.90
G/900.06
Box-to-Box
94% match
N/A
#3
R
Rick Dekker
DVS '33 · Eredivisie
Netherlands31y
KP/900.34
G/900.11
Ball-WinnerDefensive
93% match
N/A
#4
I
Irakli Yegoian
Excelsior · Eredivisie
Georgia22y
KP/901.39
G/900.16
Creator
Last 5: → Stable93% match
N/A
#5
T
Tygo Land
FC Groningen · Eredivisie
Netherlands20y
KP/901.41
G/900.09
Creator
Last 5: → Stable93% match
N/A
#6
J
Jamie Jacobs
Almere City · Eredivisie
Netherlands28y
KP/901.06
G/900.24
Balanced Midfielder
93% match
N/A
#7
D
David van der Werff
FC Groningen · Eredivisie
Netherlands21y
KP/901.89
G/900.11
CreatorCreative
Last 5: → Stable93% match
N/A
#8
S
Sven Simons
FC Eindhoven · Eredivisie
Netherlands22yContract 2026
KP/901.67
G/900.15
Creator
Last 5: → Stable93% match
N/A
#9
A
Ayoni Santos
Sparta Rotterdam · Eredivisie
Netherlands20yContract 2030
KP/901.31
G/900.00
CreatorDefensive
Last 5: → Stable93% match
€40.0M
#10
S
Sami Ouaissa
NEC Nijmegen · Eredivisie
Netherlands21y
KP/901.52
G/900.26
CreatorCreative
Last 5: ↑ Hot92% match
N/A
#11
N
Nick Runderkamp
RKAV Volendam · Eredivisie
Netherlands29y
KP/900.00
G/900.15
92% match
N/A
#12
O
Odysseus Velanas
PEC Zwolle · Eredivisie
Netherlands27yContract 2026
KP/901.53
G/900.07
CreatorCreative
Last 5: ↑ Hot92% 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 Grad Damen.
Ask AI about Grad Damen →Frequently Asked Questions
Who are the best alternatives to Grad Damen?▼
The top alternatives to Grad Damen based on AI DNA playing style analysis include: Jesse Schuurman, S. van Doorm, Rick Dekker, Irakli Yegoian, Tygo Land. 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 Grad Damen in 2026?▼
Players with a similar profile to Grad Damen in 2026 include Jesse Schuurman (N/A), S. van Doorm (N/A), Rick Dekker (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Grad Damen play and who plays similarly?▼
Grad Damen plays as a Midfielder. Players with a comparable positional profile include Jesse Schuurman (Netherlands, N/A); S. van Doorm (Netherlands, N/A); Rick Dekker (Netherlands, N/A); Irakli Yegoian (Georgia, 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.