AI DNA Similarity
Best Alternatives to M. Agger
Players most similar to M. Agger (Attacker, €500K) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to M. Agger
- 1.Gyan de Regt99% DNA match·Excelsior
- 2.Bryan Limbombe98% DNA match·Heracles Almelo
- 3.Julien Lopez98% DNA match·Paris
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
AssistsTop 11%
???Bottom 13%
“A Forward....”
See Full Verdict + Share Card →Playing Style Analysis
A Forward. Statistically, he stands out as a capable chance creator (1.5 key passes/90), a reliable supplier (0.18 assists/90) and creates high-quality scoring opportunities (0.62 big chances/90). The three most similar players to M. Agger by playing style are:
- Gyan de Regt(99% match) — A Forward. Statistically, he stands out as an elite creator (1.9 key passes/90), a reliable supplier (0.21 assists/90) and creates high-quality scoring opportunities (0.52 big chances/90).
- Bryan Limbombe(98% match) — A Forward. Statistically, he stands out as a capable chance creator (1.3 key passes/90) and a reliable supplier (0.21 assists/90).
- Julien Lopez(98% match) — A Forward. Statistically, he stands out as a capable chance creator (1.2 key passes/90), meticulous in distribution (86% pass accuracy) and creates high-quality scoring opportunities (0.60 big chances/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
G
Gyan de Regt
Excelsior · Eredivisie
Netherlands22yContract 2027
G/900.10
A/900.21
vs Agger: 4y younger
99% match
N/A
#2
B
Bryan Limbombe
Heracles Almelo · Eredivisie
Belgium24y
G/900.07
A/900.21
vs Agger: 2y younger
98% match
N/A
#3
J
Julien Lopez
Paris · Ligue 1
Algeria34y
G/900.00
A/900.00
vs Agger: 8y older
98% match
N/A
#4
A
Alessio Besio
Verl · Bundesliga
Switzerland22y
G/900.09
A/900.17
98% match
N/A
#5
K
Kyliane Dong
FC Augsburg · Bundesliga
France21y
G/900.00
A/900.00
Inside Forward
98% match
N/A
#6
I
Iman Griffith
Roda JC Kerkrade · Eredivisie
Netherlands24y
G/900.22
A/900.22
98% match
N/A
#7
A
Axel Camblan
Clermont · Ligue 1
France22y
G/900.00
A/900.13
97% match
N/A
#8
E
Edoly Lukoki
FC Eindhoven · Eredivisie
France21y
G/900.00
A/900.20
97% match
N/A
#9
F
Francisco Miguel Ribeiro Tomé Tavares Bondoso
Moreirense · Liga Portugal
Portugal30yContract 2027
G/900.05
A/900.19
97% match
N/A
#10
M
Marco Matias
Farense · Liga Portugal
Portugal37y
G/900.21
A/900.21
97% match
N/A
#11
K
Kevin Csoboth
Gençlerbirliği · Super Lig
Hungary25y
G/900.07
A/900.07
97% match
N/A
#12
J
Jorg Schreuders
FC Groningen · Eredivisie
Netherlands21y
G/900.03
A/900.07
97% 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 M. Agger.
Ask AI about M. Agger →Frequently Asked Questions
Who are the best alternatives to M. Agger?▼
The top alternatives to M. Agger based on AI DNA playing style analysis include: Gyan de Regt, Bryan Limbombe, Julien Lopez, Alessio Besio, Kyliane Dong. 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 M. Agger in 2026?▼
Players with a similar profile to M. Agger in 2026 include Gyan de Regt (N/A), Bryan Limbombe (N/A), Julien Lopez (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does M. Agger play and who plays similarly?▼
M. Agger plays as a Attacker. Players with a comparable positional profile include Gyan de Regt (Netherlands, N/A); Bryan Limbombe (Belgium, N/A); Julien Lopez (Algeria, N/A); Alessio Besio (Switzerland, 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.