RisingTransfers
AI DNA Similarity

Best Alternatives to Mohamed Simakan

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

Top 3 Alternatives to Mohamed Simakan

  1. 1.Moussa Niakhaté88% DNA match·Olympique Lyonnais€15.0M
  2. 2.Ibrahima Konaté87% DNA match·Liverpool€50.0M
  3. 3.Youssouf Ndayishimiye 87% DNA match·Nice€8.0M

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

RT

Intelligence Verdict

Ball RecoveriesTop 1%
???Bottom 21%

Simakan is the rare centre-back who makes opposing strikers irrelevant in the air while quietly...

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Playing Style Analysis

Ball-Playing CB

Simakan is the rare centre-back who makes opposing strikers irrelevant in the air while quietly threatening the scoreboard himself. His aerial win rate sits in the top 10% of the Pro League, but the more telling figure is his goals per 90—top 5% among defenders—suggesting a player who weaponises set-piece situations from both ends of the pitch. His duel win rate in the top 20% confirms this isn't just aerial dominance; he imposes himself physically across the board. The three most similar players to Mohamed Simakan by playing style are:

  • Moussa Niakhaté(88% match)A Ball-Playing CB. Statistically, he stands out as naturally left-footed, commanding in the air (6.4 clearances/90), meticulous in distribution (91% pass accuracy), wins the physical battle (70% duel success), penetrates with forward passing (9.0 final-third passes/90), central to possession (75 touches/90), dominant in the air (3.6 aerials won/90, 71%) and active off the ball (2.2 press score/90), contributing to defensive transitions. Note: this profile is based on 733 minutes of playing time this season.
  • Ibrahima Konaté(87% match)A Ball-Playing CB. Statistically, he stands out as commanding in the air (5.6 clearances/90), meticulous in distribution (90% pass accuracy), wins the physical battle (68% duel success), heavily involved in possession (63 passes/90), central to possession (77 touches/90) and dominant in the air (3.6 aerials won/90, 67%).
  • Youssouf Ndayishimiye (87% match)A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.9 tackles/90), commanding in the air (5.3 clearances/90), meticulous in distribution (90% pass accuracy), wins the physical battle (61% duel success), heavily involved in possession (61 passes/90) and central to possession (75 touches/90).

Transfer Intelligence

Moussa Niakhaté delivers 88% of the same playing style, at 25% lower cost (€15.0M vs €20.0M), with 0.85 tackles won per 90 at age 30.

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

M
Comparison Base
Mohamed Simakan
DefenderFrance€20.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
M
Moussa Niakhaté
Olympique Lyonnais · Ligue 1
Senegal30yContract 2028
Tkl/900.85
KP/900.36
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
vs Simakan: 4y older
88% match
€15.0M
#2
I
Ibrahima Konaté
Liverpool · Premier League
France26yContract 2026
Tkl/901.60
KP/900.22
Ball-Playing CBBall-Playing
Last 5: → Stable
vs Simakan: €30M more expensive
87% match
€50.0M
#3
Y
Youssouf Ndayishimiye 
Nice · Ligue 1
Burundi27yContract 2027
Tkl/901.95
KP/900.20
Ball-Playing CBBall-Playing
vs Simakan: €12M cheaper
87% match
€8.0M
#4
D
Danilho Doekhi
FC Union Berlin · Bundesliga
Netherlands27yContract 2026
Tkl/901.00
KP/900.29
Physical StopperAerial
87% match
€13.0M
#5
B
Brendan Chardonnet
Brest · Ligue 1
France31yContract 2027
Tkl/900.97
KP/900.32
Ball-Playing CBAerial
86% match
€5.0M
#6
M
Malick Thiaw
Newcastle United · Premier League
Germany24yContract 2029
Tkl/901.29
KP/900.19
Ball-Playing CBAerial
Last 5: ↑ Hot
86% match
€45.0M
#7
M
Mohammed Salisu
Monaco · Ligue 1
Ghana27yContract 2028
Tkl/900.20
KP/900.20
Ball-Playing CBBall-Playing
86% match
€10.0M
#8
M
Montassar Talbi
Lorient · Ligue 1
Tunisia27yContract 2027
Tkl/900.70
KP/900.30
Ball-Playing CBAerial
85% match
€7.0M
#9
A
Anel Ahmedhodzic
Feyenoord · Eredivisie
Bosnia and Herzegovina27yContract 2029
Tkl/901.10
KP/900.53
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€12.0M
#10
C
Clément Akpa
Auxerre · Ligue 1
France24yContract 2028
Tkl/901.45
KP/900.09
Ball-Playing CBAerial
Last 5: ↓ Dip
85% match
€8.0M
#11
C
Castello Lukeba
RB Leipzig · Bundesliga
France23yContract 2029
Tkl/900.93
KP/900.00
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
86% match
€45.0M
#12
A
Abdelhamid Ait Boudlal
Rennes · Ligue 1
Morocco20yContract 2028
Tkl/901.76
KP/900.00
Ball-Playing CBAerial
Last 5: → Stable
86% match
€10.0M

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 Mohamed Simakan.

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Frequently Asked Questions

Who are the best alternatives to Mohamed Simakan?
The top alternatives to Mohamed Simakan based on AI DNA playing style analysis include: Moussa Niakhaté, Ibrahima Konaté, Youssouf Ndayishimiye , Danilho Doekhi, Brendan Chardonnet. 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 Mohamed Simakan in 2026?
Players with a similar profile to Mohamed Simakan in 2026 include Moussa Niakhaté (€15.0M), Ibrahima Konaté (€50.0M), Youssouf Ndayishimiye  (€8.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mohamed Simakan play and who plays similarly?
Mohamed Simakan plays as a Defender. Players with a comparable positional profile include Moussa Niakhaté (Senegal, €15.0M); Ibrahima Konaté (France, €50.0M); Youssouf Ndayishimiye  (Burundi, €8.0M); Danilho Doekhi (Netherlands, €13.0M).
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.