AI DNA Similarity
Best Alternatives to Jeremiah Mensah
Players most similar to Jeremiah Mensah (Midfielder, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Jeremiah Mensah
- 1.Joseph Nduquidi85% DNA match·Metz
- 2.Gerry Vlak85% DNA match·Koninklijke HFC
- 3.Kiano Dyer85% DNA match·FC Volendam
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
???Bottom 0%
“Jeremiah Dennit Mensah is a Midfielder....”
See Full Verdict + Share Card →Playing Style Analysis
Small Sample
Jeremiah Dennit Mensah is a Midfielder. Solid midfield option. (Limited sample: 327 mins) The three most similar players to Jeremiah Mensah by playing style are:
- Joseph Nduquidi(85% match) — Joseph N'Duquidi is a Balanced Midfielder. Well-rounded midfielder contributing in multiple phases. Note: this profile is based on 748 minutes of playing time this season.
- Gerry Vlak(85% match) — Gerry Vlak is a Midfielder. Solid midfield option. (Limited sample: 378 mins)
- Kiano Dyer(85% match) — Kiano Dyer is a Midfielder. Solid midfield option. (Limited sample: 286 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
J
Joseph Nduquidi
Metz · Ligue 1
France21y
KP/900.15
G/900.00
Balanced MidfielderSmall Sample
Last 5: ↓ Dipvs Mensah: 3y older
85% match
N/A
#2
G
Gerry Vlak
Koninklijke HFC · Eredivisie
Netherlands30y
KP/900.00
G/900.00
Small Sample
vs Mensah: 12y older
85% match
N/A
#3
K
Kiano Dyer
FC Volendam · Eredivisie
England19y
KP/900.00
G/900.00
Small Sample
85% match
N/A
#4
D
Doni Arifi
SpVgg Greuther Fürth · Bundesliga
Finland24y
KP/901.12
G/900.00
Small Sample
Last 5: ↓ Dip84% match
N/A
#5
M
Maurizio Brenna
Katwijk · Eredivisie
Netherlands27y
KP/900.00
G/900.00
Small Sample
84% match
N/A
#6
A
Alamara Djabi
FC Midtjylland · Superliga
Guinea-Bissau19y
KP/900.00
G/900.00
Small Sample
84% match
N/A
#7
Y
Yaw Ackah
Kayserispor · Super Lig
Ghana26y
KP/900.00
G/900.00
Ball-WinnerDefensive
83% match
N/A
#8
S
Salvador Blopa
Sporting CP · Liga Portugal
Portugal18y
KP/900.00
G/900.00
Small Sample
83% match
N/A
#9
E
Emmanuele De Chiara
Napoli · Serie A
Italy20y
KP/900.00
G/900.00
Small Sample
83% match
N/A
#10
R
Rick van der Meer
Katwijk · Eredivisie
Netherlands28y
KP/900.00
G/900.00
Small Sample
82% match
N/A
#11
Y
Yanis Khafi
Paris Saint Germain · Ligue 1
France20y
KP/901.18
G/900.00
Small Sample
82% match
N/A
#12
D
Daniel Bisgard Haarbo
Fredericia · Superliga
Denmark23y
KP/900.00
G/900.31
Small Sample
81% 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 Jeremiah Mensah.
Ask AI about Jeremiah Mensah →Frequently Asked Questions
Who are the best alternatives to Jeremiah Mensah?▼
The top alternatives to Jeremiah Mensah based on AI DNA playing style analysis include: Joseph Nduquidi, Gerry Vlak, Kiano Dyer, Doni Arifi, Maurizio Brenna. 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 Jeremiah Mensah in 2026?▼
Players with a similar profile to Jeremiah Mensah in 2026 include Joseph Nduquidi (N/A), Gerry Vlak (N/A), Kiano Dyer (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Jeremiah Mensah play and who plays similarly?▼
Jeremiah Mensah plays as a Midfielder. Players with a comparable positional profile include Joseph Nduquidi (France, N/A); Gerry Vlak (Netherlands, N/A); Kiano Dyer (England, N/A); Doni Arifi (Finland, 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.