AI DNA Similarity
Best Alternatives to S. van Doorm
Players most similar to S. van Doorm (Midfielder, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
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). The three most similar players to S. van Doorm by playing style are:
- Jesse Schuurman(95% 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).
- Rick Dekker(94% 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.
- Tika de Jonge(94% match) — A Box-to-Box in Eredivisie. Statistically, he stands out as active in the tackle (2.4 tackles/90) and heavily involved in play (52 touches/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
J
Jesse Schuurman
IJsselmeervogels · Eredivisie
Netherlands28y
KP/900.30
G/900.00
Balanced Midfielder
95% match
N/A
#2
R
Rick Dekker
DVS '33 · Eredivisie
Netherlands31y
KP/900.34
G/900.11
Ball-WinnerDefensive
94% match
N/A
#3
T
Tika de Jonge
FC Groningen · Eredivisie
Netherlands23y
KP/900.80
G/900.12
Box-to-Box
Last 5: → Stable94% match
N/A
#4
G
Gijs Besselink
FC Twente · Eredivisie
Netherlands21y
KP/900.00
G/900.04
Box-to-Box
94% match
N/A
#5
G
Grad Damen
Kozakken Boys · Eredivisie
Netherlands28y
KP/901.36
G/900.15
Creator
94% match
N/A
#6
E
Edouard Michut
Fortuna Sittard · Eredivisie
France23yContract 2027
KP/900.90
G/900.00
Box-to-BoxDefensive
Last 5: ↓ Dip93% match
N/A
#7
J
Julian Baas
Sparta Rotterdam · Eredivisie
Netherlands23y
KP/901.60
G/900.00
Box-to-BoxCreative
Last 5: → Stable93% match
N/A
#8
M
Max Balard
NAC Breda · Eredivisie
Australia25y
KP/900.87
G/900.00
Box-to-Box
Last 5: → Stable93% match
N/A
#9
A
Alex Plat
FC Volendam · Eredivisie
Netherlands28y
KP/900.90
G/900.00
Box-to-BoxSmall Sample
Last 5: → Stable93% match
N/A
#10
P
Pele Van Anholt
TEC · Eredivisie
Netherlands34y
KP/900.56
G/900.00
Ball-WinnerDefensive
92% match
N/A
#11
D
Daan Huisman
FC Eindhoven · Eredivisie
Netherlands23yContract 2026
KP/901.20
G/900.11
Box-to-Box
Last 5: → Stable92% match
N/A
#12
N
Nick Runderkamp
RKAV Volendam · Eredivisie
Netherlands29y
KP/900.00
G/900.15
92% 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 S. van Doorm.
Ask AI about S. van Doorm →Frequently Asked Questions
Who are the best alternatives to S. van Doorm?▼
The top alternatives to S. van Doorm based on AI DNA playing style analysis include: Jesse Schuurman, Rick Dekker, Tika de Jonge, Gijs Besselink, Grad Damen. 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. van Doorm in 2026?▼
Players with a similar profile to S. van Doorm in 2026 include Jesse Schuurman (N/A), Rick Dekker (N/A), Tika de Jonge (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. van Doorm play and who plays similarly?▼
S. van Doorm plays as a Midfielder. Players with a comparable positional profile include Jesse Schuurman (Netherlands, N/A); Rick Dekker (Netherlands, N/A); Tika de Jonge (Netherlands, N/A); Gijs Besselink (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.