AI DNA Similarity
Best Alternatives to Marco Matias
Players most similar to Marco Matias (Attacker, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Playing Style Analysis
A Forward. Statistically, he stands out as an elite creator (1.6 key passes/90), a regular goalscorer (0.21 goals/90), a reliable supplier (0.21 assists/90), creates high-quality scoring opportunities (1.00 big chances/90) and draws fouls effectively (2.9/90). The three most similar players to Marco Matias by playing style are:
- Dilane Bakwa(98% match) — A Forward. Statistically, he stands out as naturally left-footed, an elite creator (1.7 key passes/90), a regular goalscorer (0.21 goals/90), a prolific assist provider (0.29 assists/90), creates high-quality scoring opportunities (0.82 big chances/90) and draws fouls effectively (2.5/90).
- Jean-Luc Dompé(98% match) — A Forward. Statistically, he stands out as an elite creator (2.6 key passes/90), a regular goalscorer (0.24 goals/90) and creates high-quality scoring opportunities (0.55 big chances/90).
- Gyan de Regt(98% match) — A Forward. Statistically, he stands out as an elite creator (1.9 key passes/90), a reliable supplier (0.17 assists/90) and creates high-quality scoring opportunities (0.51 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
D
Dilane Bakwa
Nottingham Forest · Premier League
France23yContract 2030
G/900.21
A/900.29
98% match
€32.0M
#2
J
Jean-Luc Dompé
Hamburger SV · Bundesliga
France30yContract 2027
G/900.24
A/900.00
98% match
€3.5M
#3
G
Gyan de Regt
Excelsior · Eredivisie
Netherlands22yContract 2027
G/900.08
A/900.17
98% match
N/A
#4
A
Abraham Marcus
Estrela Amadora · Liga Portugal
Nigeria26yContract 2028
G/900.23
A/900.23
98% match
N/A
#5
R
Riccardo Sottil
Lecce · Serie A
Italy26yContract 2026
G/900.08
A/900.17
97% match
€3.5M
#6
J
Jakub Kamiński
FC Köln · Bundesliga
Poland23yContract 2026
G/900.23
A/900.12
97% match
€12.0M
#7
A
Afonso Moreira
Olympique Lyonnais · Ligue 1
Portugal21yContract 2029
G/900.17
A/900.50
97% match
€5.0M
#8
B
Ben Gannon-Doak
AFC Bournemouth · Premier League
Scotland20yContract 2030
G/900.15
A/900.35
97% match
€18.0M
#9
F
Franck Honorat
Borussia Mönchengladbach · Bundesliga
France29yContract 2029
G/900.06
A/900.35
97% match
€12.0M
#10
K
Kyliane Dong
FC Augsburg · Bundesliga
France21y
G/900.09
A/900.26
97% match
N/A
#11
D
Dimitrios Limnios
Fortuna Sittard · Eredivisie
Greece27yContract 2026
G/900.10
A/900.21
97% match
€3.3M
#12
A
Alessio Besio
Verl · Bundesliga
Switzerland22y
G/900.09
A/900.17
97% 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 Marco Matias.
Ask AI about Marco Matias →Frequently Asked Questions
Who are the best alternatives to Marco Matias?▼
The top alternatives to Marco Matias based on AI DNA playing style analysis include: Dilane Bakwa, Jean-Luc Dompé, Gyan de Regt, Abraham Marcus, Riccardo Sottil. 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 Marco Matias in 2026?▼
Players with a similar profile to Marco Matias in 2026 include Dilane Bakwa (€32.0M), Jean-Luc Dompé (€3.5M), Gyan de Regt (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Marco Matias play and who plays similarly?▼
Marco Matias plays as a Attacker. Players with a comparable positional profile include Dilane Bakwa (France, €32.0M); Jean-Luc Dompé (France, €3.5M); Gyan de Regt (Netherlands, N/A); Abraham Marcus (Nigeria, 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.