RisingTransfers
AI DNA Similarity

Best Alternatives to Stephen Acquah

Players most similar to Stephen Acquah (Defender, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Stephen Acquah

  1. 1.Omar Fayed99% DNA match·Arouca
  2. 2.Gustav Bjerge98% DNA match·Hobro
  3. 3.João Afonso98% DNA match·Tondela

Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.

RT

Intelligence Verdict

Press IntensityTop 0%
???Bottom 0%

A Ball-Playing CB....

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Playing Style Analysis

Ball-Playing CBBall-PlayingSmall Sample

A Ball-Playing CB. Statistically, he stands out as active in the tackle (2.4 tackles/90), reads the game exceptionally (1.6 interceptions/90), meticulous in distribution (92% pass accuracy), wins the physical battle (61% duel success), heavily involved in possession (73 passes/90), central to possession (92 touches/90) and a high-intensity presser (press score 4.2/90), constantly disrupting opposition build-up. Note: this profile is based on 454 minutes of playing time this season. The three most similar players to Stephen Acquah by playing style are:

  • Omar Fayed(99% match)A Ball-Playing CB. Statistically, he stands out as an aggressive ball-winner (3.5 tackles/90), commanding in the air (4.5 clearances/90), reads the game exceptionally (2.0 interceptions/90), meticulous in distribution (88% pass accuracy), wins the physical battle (65% duel success), wins the ball cleanly (2.0 successful tackles/90), central to possession (73 touches/90), uses long balls frequently (7.5/90), a high-intensity presser (press score 3.0/90), constantly disrupting opposition build-up and top 10% tackler in the league. Note: this profile is based on 494 minutes of playing time this season.
  • Gustav Bjerge(98% match)A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.8 tackles/90), commanding in the air (6.9 clearances/90), reads the game exceptionally (2.9 interceptions/90), wins the physical battle (62% duel success), heavily involved in possession (64 passes/90), central to possession (83 touches/90), strong in aerial duels (3.4 aerials won/90), uses long balls frequently (11.4/90) and active off the ball (2.6 press score/90), contributing to defensive transitions. Note: this profile is based on 836 minutes of playing time this season.
  • João Afonso(98% match)A Physical Stopper. Statistically, he stands out as active in the tackle (1.9 tackles/90), commanding in the air (7.4 clearances/90), reads the game exceptionally (3.0 interceptions/90), wins the physical battle (62% duel success), dominant in the air (3.6 aerials won/90, 64%), uses long balls frequently (6.3/90) and active off the ball (2.4 press score/90), contributing to defensive transitions. Note: this profile is based on 599 minutes of playing time this season.

Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →

S
Comparison Base
Stephen Acquah
DefenderGhanaN/A
Full profile →

Similar Players — Ranked by DNA Similarity

#1
O
Omar Fayed
Arouca · Liga Portugal
Egypt22y
Tkl/903.46
KP/900.00
Ball-Playing CBBall-Playing
vs Acquah: 2y older
99% match
N/A
#2
G
Gustav Bjerge
Hobro · Superliga
Denmark20y
Tkl/901.25
KP/900.42
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
98% match
N/A
#3
J
João Afonso
Tondela · Liga Portugal
Portugal35yContract 2026
Tkl/901.95
KP/900.00
Physical StopperAerial
Last 5: ↑ Hot
vs Acquah: 15y older
98% match
N/A
#4
F
Fabio Chiarodia
Borussia Mönchengladbach · Bundesliga
Italy20yContract 2027
Tkl/900.48
KP/900.00
Ball-Playing CBBall-Playing
98% match
€3.0M
#5
V
V. Sørensen
HB Køge · Superliga
Denmark19y
Tkl/903.77
KP/900.22
Physical Stopper
Last 5: → Stable
98% match
N/A
#6
C
Claudio Kammerknecht
Dynamo Dresden · Bundesliga
Germany26yContract 2026
Tkl/903.84
KP/900.27
Ball-Playing CBBall-Playing
98% match
N/A
#7
M
Magnus Døj
Kolding IF · Superliga
Denmark20y
Tkl/902.36
KP/900.54
Ball-Playing CBAerial
Last 5: → Stable
98% match
N/A
#8
T
Tijn Joosten
VVV-Venlo · Eredivisie
Netherlands19y
Tkl/900.45
KP/900.45
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
98% match
N/A
#9
N
Noah Markmann
Nordsjælland · Superliga
Denmark19yContract 2027
Tkl/902.48
KP/900.23
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
98% match
N/A
#10
L
Luka Callø Carstensen
Aarhus Fremad · Superliga
Denmark20y
Tkl/901.22
KP/900.52
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
98% match
N/A
#11
D
Dylan Timber
VVV-Venlo · Eredivisie
Netherlands26yContract 2026
Tkl/903.14
KP/900.18
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
98% match
N/A
#12
L
Laurits Bust
HB Køge · Superliga
Denmark27y
Tkl/902.33
KP/901.00
Ball-Playing CBAerial
Last 5: ↓ Dip
98% 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 Stephen Acquah.

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Frequently Asked Questions

Who are the best alternatives to Stephen Acquah?
The top alternatives to Stephen Acquah based on AI DNA playing style analysis include: Omar Fayed, Gustav Bjerge, João Afonso, Fabio Chiarodia, V. Sørensen. 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 Stephen Acquah in 2026?
Players with a similar profile to Stephen Acquah in 2026 include Omar Fayed (N/A), Gustav Bjerge (N/A), João Afonso (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Stephen Acquah play and who plays similarly?
Stephen Acquah plays as a Defender. Players with a comparable positional profile include Omar Fayed (Egypt, N/A); Gustav Bjerge (Denmark, N/A); João Afonso (Portugal, N/A); Fabio Chiarodia (Italy, €3.0M).
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.