AI DNA Similarity
Best Alternatives to Simon Bækgård
Players most similar to Simon Bækgård (Midfielder, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
A Metronome in Superliga. Statistically, he stands out as meticulous in distribution (88% pass accuracy), heavily involved in possession (67 passes/90), penetrates with forward passing (8. The three most similar players to Simon Bækgård by playing style are:
- Magnus Saaby(99% match) — A Metronome in Superliga. Statistically, he stands out as meticulous in distribution (88% pass accuracy), heavily involved in possession (62 passes/90), penetrates with forward passing (8.
- Frederik Grube(99% match) — A Metronome in Superliga. Statistically, he stands out as meticulous in distribution (89% pass accuracy), heavily involved in possession (67 passes/90), penetrates with forward passing (8.
- Mark Brink(98% match) — A Metronome in Superliga. Statistically, he stands out as meticulous in distribution (91% pass accuracy), heavily involved in possession (72 passes/90), central to possession (80 touches/90) and active off the ball (2.
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
Magnus Saaby
Kolding IF · Superliga
Denmark23y
KP/900.47
G/900.06
Metronome
Last 5: ↑ Hot99% match
N/A
#2
F
Frederik Grube
Aarhus Fremad · Superliga
Denmark25y
KP/900.54
G/900.00
Metronome
Last 5: ↑ Hot99% match
N/A
#3
M
Mark Brink
Nordsjælland · Superliga
Denmark28yContract 2027
KP/900.40
G/900.00
Metronome
Last 5: → Stable98% match
N/A
#4
M
Mikkel Wohlgemuth
B 93 · Superliga
Denmark30y
KP/900.63
G/900.39
Metronome
98% match
N/A
#5
F
Felix Beijmo
AGF · Superliga
Sweden28y
KP/901.98
G/900.09
MetronomeCreative
Last 5: → Stable98% match
€269K
#6
N
Nicolai Poulsen
AGF · Superliga
Denmark32y
KP/901.01
G/900.07
Metronome
98% match
N/A
#7
M
Magnus Kirchheiner
Aarhus Fremad · Superliga
Denmark24y
KP/901.24
G/900.19
MetronomeCreative
Last 5: ↑ Hot98% match
N/A
#8
L
L. Sandgrav
Lyngby Boldklub · Superliga
Denmark21yContract 2028
KP/901.05
G/900.06
Metronome
Last 5: ↑ Hot98% match
N/A
#9
R
Rasmus Falk
Odense BK · Superliga
Denmark34yContract 2028
KP/901.57
G/900.05
Metronome
Last 5: ↓ Dip98% match
N/A
#10
N
Nicklas Røjkjær
Nordsjælland · Superliga
Denmark27y
KP/901.94
G/900.09
MetronomeCreative
Last 5: ↓ Dip97% match
€1.6M
#11
C
C. Winther
Lyngby Boldklub · Superliga
Denmark23yContract 2027
KP/901.37
G/900.39
Metronome
Last 5: → Stable97% match
N/A
#12
M
Marcus Bonde
Aalborg BK · Superliga
Denmark19y
KP/900.40
G/900.00
MetronomeDefensive
Last 5: → Stable97% match
N/A
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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 Simon Bækgård.
Ask AI about Simon Bækgård →Frequently Asked Questions
Who are the best alternatives to Simon Bækgård?▼
The top alternatives to Simon Bækgård based on AI DNA playing style analysis include: Magnus Saaby, Frederik Grube, Mark Brink, Mikkel Wohlgemuth, Felix Beijmo. 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 Simon Bækgård in 2026?▼
Players with a similar profile to Simon Bækgård in 2026 include Magnus Saaby (N/A), Frederik Grube (N/A), Mark Brink (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Simon Bækgård play and who plays similarly?▼
Simon Bækgård plays as a Midfielder. Players with a comparable positional profile include Magnus Saaby (Denmark, N/A); Frederik Grube (Denmark, N/A); Mark Brink (Denmark, N/A); Mikkel Wohlgemuth (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.