AI DNA Similarity
Best Alternatives to Jonathan Amon
Players most similar to Jonathan Amon (Attacker, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
A Dynamic Forward in Superliga. Statistically, he stands out as a dynamic dribbler (2.1/90). The three most similar players to Jonathan Amon by playing style are:
- Prince Amoako Junior(96% match) — A Dynamic Forward in Superliga. Statistically, he stands out as a capable chance creator (1.4 key passes/90), a constant goal threat (3.1 shots/90), a regular goalscorer (0.30 goals/90), a reliable supplier (0.20 assists/90) and a dynamic dribbler (2.2/90).
- Nebiyou Perry(96% match) — A Dynamic Forward in Superliga. Statistically, he stands out as a reliable supplier (0.15 assists/90) and a dynamic dribbler (2.9/90).
- O. Hyseni(96% match) — A Dynamic Forward in Superliga. Statistically, he stands out as an elite creator (1.5 key passes/90), a constant goal threat (3.1 shots/90), a prolific assist provider (0.27 assists/90) and a dynamic dribbler (2.1/90).
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
P
Prince Amoako Junior
Nordsjælland · Superliga
Ghana19yContract 2029
G/900.30
A/900.20
Dynamic ForwardDribbler
Last 5: → Stable96% match
N/A
#2
N
Nebiyou Perry
Kolding IF · Superliga
Sweden26y
G/900.15
A/900.15
Dynamic ForwardDribbler
Last 5: ↑ Hot96% match
N/A
#3
O
O. Hyseni
Sønderjyske Fodbold · Superliga
Denmark18yContract 2026
G/900.18
A/900.27
Dynamic ForwardDribbler
Last 5: ↑ Hot96% match
N/A
#4
A
Agon Mucolli
Fredericia · Superliga
Albania27y
G/900.52
A/900.13
Dynamic ForwardProlific
95% match
N/A
#5
S
S. Jalaei
Hillerød · Superliga
Denmark19y
G/900.14
A/900.14
Complete ForwardDribbler
Last 5: ↓ Dip94% match
N/A
#6
A
Adam Ahmad
B 93 · Superliga
Denmark22y
G/900.14
A/900.07
Complete Forward
Last 5: ↓ Dip93% match
N/A
#7
B
B. Alkhoudari
HB Køge · Superliga
Denmark19y
G/900.12
A/900.12
Complete ForwardDribbler
94% match
N/A
#8
P
Patrick Mortensen
AGF · Superliga
Denmark36y
G/900.33
A/900.00
Complete Forward
Last 5: ↓ Dip93% match
N/A
#9
C
C. Jensen
HB Køge · Superliga
Denmark26y
G/900.30
A/900.05
Complete Forward
Last 5: → Stable93% match
N/A
#10
M
Mikkel Duelund
Vejle Boldklub · Superliga
Denmark28y
G/900.34
A/900.07
Complete Forward
Last 5: ↑ Hot93% match
€4.0M
#11
M
Mohamad Al Naser
HB Køge · Superliga
Jordan29y
G/900.31
A/900.08
Complete Forward
Last 5: ↑ Hot93% match
N/A
#12
K
Kristian Arnstad
AGF · Superliga
Norway22y
G/900.42
A/900.14
Complete ForwardProlific
Last 5: ↑ Hot93% 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 Jonathan Amon.
Ask AI about Jonathan Amon →Frequently Asked Questions
Who are the best alternatives to Jonathan Amon?▼
The top alternatives to Jonathan Amon based on AI DNA playing style analysis include: Prince Amoako Junior, Nebiyou Perry, O. Hyseni, Agon Mucolli, S. Jalaei. 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 Jonathan Amon in 2026?▼
Players with a similar profile to Jonathan Amon in 2026 include Prince Amoako Junior (N/A), Nebiyou Perry (N/A), O. Hyseni (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Jonathan Amon play and who plays similarly?▼
Jonathan Amon plays as a Attacker. Players with a comparable positional profile include Prince Amoako Junior (Ghana, N/A); Nebiyou Perry (Sweden, N/A); O. Hyseni (Denmark, N/A); Agon Mucolli (Albania, 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.