RisingTransfers
AI DNA Similarity

Best Alternatives to Josh Acheampong

Players most similar to Josh Acheampong (Defender, €20.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Josh Acheampong

  1. 1.Ayden Heaven86% DNA match·Manchester United€10.0M
  2. 2.Kojo Peprah Oppong86% DNA match·Nice€4.0M
  3. 3.Malick Thiaw85% DNA match·Newcastle United€45.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%

A Active Full-Back....

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Playing Style Analysis

Active Full-BackBall-PlayingSmall Sample

A Active Full-Back. Statistically, he stands out as reads the game exceptionally (1.5 interceptions/90), meticulous in distribution (93% pass accuracy), wins the physical battle (64% duel success), heavily involved in possession (69 passes/90), central to possession (85 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions. Note: this profile is based on 585 minutes of playing time this season. The three most similar players to Josh Acheampong by playing style are:

  • Ayden Heaven(86% match)A Ball-Playing CB. Statistically, he stands out as naturally left-footed, active in the tackle (2.1 tackles/90), commanding in the air (5.8 clearances/90), meticulous in distribution (90% pass accuracy) and wins the physical battle (74% duel success).
  • Kojo Peprah Oppong(86% match)A Ball-Playing CB. Statistically, he stands out as active in the tackle (2.2 tackles/90), commanding in the air (4.8 clearances/90), meticulous in distribution (92% pass accuracy) and wins the physical battle (61% duel success). Note: this profile is based on 772 minutes of playing time this season.
  • Malick Thiaw(85% match)Thiaw has quietly become one of the Premier League's most complete defensive profiles — a towering centre-back who doesn't just win his battles, he wins them cleanly. His aerial win rate of 65.1% and duel success of 64.2% both sit in the league's top 20%, but the counterintuitive story is his attacking output: 0.16 goals per 90 and 0.98 shots per 90 place him in the top 10% among defenders, suggesting a genuine threat from set-pieces that opponents routinely underestimate. His pass accuracy of 90.6% reflects a ball-player comfortable in possession-heavy systems, and his above-average delivery into the final third adds genuine build-up value.

Transfer Intelligence

Ayden Heaven delivers 86% of the same playing style, at 50% lower cost (€10.0M vs €20.0M), with 2.14 tackles won per 90 at age 19.

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

J
Comparison Base
Josh Acheampong
DefenderEngland€20.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
A
Ayden Heaven
Manchester United · Premier League
England19yContract 2029
Tkl/902.14
KP/900.20
Ball-Playing CBAerial
Last 5: → Stable
vs Acheampong: €10M cheaper
86% match
€10.0M
#2
K
Kojo Peprah Oppong
Nice · Ligue 1
Ghana21yContract 2029
Tkl/902.21
KP/900.23
Ball-Playing CBAerial
Last 5: → Stable
vs Acheampong: €16M cheaper
86% match
€4.0M
#3
M
Malick Thiaw
Newcastle United · Premier League
Germany24yContract 2029
Tkl/901.29
KP/900.19
Ball-Playing CBAerial
Last 5: ↑ Hot
vs Acheampong: €25M more expensive · 4y older
85% match
€45.0M
#4
B
Bashir Humphreys
Burnley · Premier League
England23y
Tkl/901.64
KP/900.55
Ball-Playing CBAerial
Last 5: ↓ Dip
86% match
€12.0M
#5
J
Jaka Bijol
Leeds United · Premier League
Slovenia27yContract 2030
Tkl/901.30
KP/900.36
Ball-Playing CBAerial
Last 5: ↓ Dip
85% match
€18.0M
#6
O
Oliver Scarles
West Ham United · Premier League
England20yContract 2028
Tkl/904.08
KP/900.68
AerialSmall Sample
86% match
€8.0M
#7
I
Ian Maatsen
Aston Villa · Premier League
Netherlands24yContract 2030
Tkl/902.33
KP/901.44
Active Full-Back
Last 5: ↓ Dip
85% match
€25.0M
#8
J
Jeremie Frimpong
Liverpool · Eredivisie
Netherlands25yContract 2030
Tkl/901.05
KP/901.23
Active Full-Back
Last 5: ↑ Hot
85% match
€38.0M
#9
M
Marc Guéhi
Manchester City · Premier League
England25yContract 2026
Tkl/901.55
KP/900.45
Ball-Playing CBAerial
Last 5: → Stable
84% match
€65.0M
#10
R
Riccardo Calafiori
Arsenal · Premier League
Italy23yContract 2029
Tkl/901.81
KP/900.34
Active Full-Back
Last 5: → Stable
84% match
€50.0M
#11
K
Kenny Tete
Fulham · Premier League
Netherlands30yContract 2028
Tkl/903.26
KP/900.55
Physical Stopper
Last 5: → Stable
84% match
€11.0M
#12
C
Calvin Bassey
Fulham · Premier League
Nigeria26yContract 2027
Tkl/902.22
KP/900.34
Ball-Playing CBBall-Playing
Last 5: → Stable
84% match
€28.0M

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 Josh Acheampong.

Ask AI about Josh Acheampong

Frequently Asked Questions

Who are the best alternatives to Josh Acheampong?
The top alternatives to Josh Acheampong based on AI DNA playing style analysis include: Ayden Heaven, Kojo Peprah Oppong, Malick Thiaw, Bashir Humphreys, Jaka Bijol. 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 Josh Acheampong in 2026?
Players with a similar profile to Josh Acheampong in 2026 include Ayden Heaven (€10.0M), Kojo Peprah Oppong (€4.0M), Malick Thiaw (€45.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Josh Acheampong play and who plays similarly?
Josh Acheampong plays as a Defender. Players with a comparable positional profile include Ayden Heaven (England, €10.0M); Kojo Peprah Oppong (Ghana, €4.0M); Malick Thiaw (Germany, €45.0M); Bashir Humphreys (England, €12.0M).
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.