AI DNA Similarity
Best Alternatives to Aleksandr Selyava
Players most similar to Aleksandr Selyava (Midfielder, €120K) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Aleksandr Selyava
- 1.Salvador Blopa87% DNA match·Sporting CP
- 2.Gerry Vlak84% DNA match·Koninklijke HFC
- 3.Maurizio Brenna84% DNA match·Katwijk
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
Chances MissedTop 0%
???Bottom 0%
“Aleskandr Selyava is a Midfielder....”
See Full Verdict + Share Card →Playing Style Analysis
Aleskandr Selyava is a Midfielder. Solid midfield option. (Limited sample: 312 mins) The three most similar players to Aleksandr Selyava by playing style are:
- Salvador Blopa(87% match) — Salvador Blopa is a Midfielder. Solid midfield option. (Limited sample: 270 mins)
- Gerry Vlak(84% match) — Gerry Vlak is a Midfielder. Solid midfield option. (Limited sample: 378 mins)
- Maurizio Brenna(84% match) — Maurizio Brenna is a Midfielder. Solid midfield option. (Limited sample: 299 mins)
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
S
Salvador Blopa
Sporting CP · Liga Portugal
Portugal18y
KP/900.00
G/900.00
Small Sample
vs Selyava: 15y younger
87% match
N/A
#2
G
Gerry Vlak
Koninklijke HFC · Eredivisie
Netherlands30y
KP/900.00
G/900.00
Small Sample
vs Selyava: 3y younger
84% match
N/A
#3
M
Maurizio Brenna
Katwijk · Eredivisie
Netherlands27y
KP/900.00
G/900.00
Small Sample
vs Selyava: 6y younger
84% match
N/A
#4
K
Kiano Dyer
FC Volendam · Eredivisie
England19y
KP/900.00
G/900.00
Small Sample
83% match
N/A
#5
A
Alamara Djabi
FC Midtjylland · Superliga
Guinea-Bissau19y
KP/900.00
G/900.00
Small Sample
83% match
N/A
#6
J
Jeremiah Mensah
Bayer 04 Leverkusen · Bundesliga
Germany18y
KP/900.00
G/900.00
Small Sample
83% match
N/A
#7
D
Doni Arifi
SpVgg Greuther Fürth · Bundesliga
Finland24y
KP/901.12
G/900.00
Small Sample
Last 5: ↓ Dip83% match
N/A
#8
J
Joseph Nduquidi
Metz · Ligue 1
France21y
KP/900.15
G/900.00
Balanced MidfielderSmall Sample
Last 5: ↓ Dip82% match
N/A
#9
Y
Yanis Khafi
Paris Saint Germain · Ligue 1
France20y
KP/901.18
G/900.00
Small Sample
81% match
N/A
#10
R
Rick van der Meer
Katwijk · Eredivisie
Netherlands28y
KP/900.00
G/900.00
Small Sample
81% match
N/A
#11
D
Daniel Bisgard Haarbo
Fredericia · Superliga
Denmark23y
KP/900.00
G/900.31
Small Sample
81% match
N/A
#12
E
Emmanuele De Chiara
Napoli · Serie A
Italy20y
KP/900.00
G/900.00
Small Sample
81% 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 Aleksandr Selyava.
Ask AI about Aleksandr Selyava →Frequently Asked Questions
Who are the best alternatives to Aleksandr Selyava?▼
The top alternatives to Aleksandr Selyava based on AI DNA playing style analysis include: Salvador Blopa, Gerry Vlak, Maurizio Brenna, Kiano Dyer, Alamara Djabi. 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 Aleksandr Selyava in 2026?▼
Players with a similar profile to Aleksandr Selyava in 2026 include Salvador Blopa (N/A), Gerry Vlak (N/A), Maurizio Brenna (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Aleksandr Selyava play and who plays similarly?▼
Aleksandr Selyava plays as a Midfielder. Players with a comparable positional profile include Salvador Blopa (Portugal, N/A); Gerry Vlak (Netherlands, N/A); Maurizio Brenna (Netherlands, N/A); Kiano Dyer (England, 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.