RisingTransfers
AI DNA Similarity

Best Alternatives to Dayot Upamecano

Players most similar to Dayot Upamecano (Defender, €70.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Dayot Upamecano

  1. 1.Ibrahima Konaté86% DNA match·Liverpool€50.0M
  2. 2.Castello Lukeba86% DNA match·RB Leipzig€45.0M
  3. 3.Edmond Tapsoba86% DNA match·Bayer 04 Leverkusen€35.0M

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

RT

Intelligence Verdict

Ball RecoveriesTop 4%

A Ball-Playing CB....

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Playing Style Analysis

Ball-Playing CBBall-Playing

A Ball-Playing CB. Statistically, he stands out as active in the tackle (2.2 tackles/90), meticulous in distribution (92% pass accuracy), wins the physical battle (56% duel success), heavily involved in possession (82 passes/90), penetrates with forward passing (9.2 final-third passes/90), central to possession (94 touches/90), uses long balls frequently (5.2/90) and active off the ball (3.0 press score/90), contributing to defensive transitions. The three most similar players to Dayot Upamecano by playing style are:

  • Ibrahima Konaté(86% 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%).
  • Castello Lukeba(86% match)A Ball-Playing CB. Statistically, he stands out as naturally left-footed, commanding in the air (5.2 clearances/90), meticulous in distribution (90% pass accuracy), wins the physical battle (61% duel success), heavily involved in possession (67 passes/90), central to possession (82 touches/90), uses long balls frequently (5.8/90) and active off the ball (2.8 press score/90), contributing to defensive transitions.
  • Edmond Tapsoba(86% match)A Ball-Playing CB. Statistically, he stands out as commanding in the air (4.9 clearances/90), meticulous in distribution (91% pass accuracy), wins the physical battle (57% duel success), heavily involved in possession (84 passes/90), penetrates with forward passing (8.8 final-third passes/90), central to possession (98 touches/90), uses long balls frequently (7.3/90) and active off the ball (2.5 press score/90), contributing to defensive transitions.

Transfer Intelligence

Ibrahima Konaté delivers 86% of the same playing style, at 29% lower cost (€50.0M vs €70.0M), with 1.60 tackles won per 90 at age 26.

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

D
Comparison Base
Dayot Upamecano
DefenderFrance€70.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
I
Ibrahima Konaté
Liverpool · Premier League
France26yContract 2026
Tkl/901.60
KP/900.22
Ball-Playing CBBall-Playing
Last 5: → Stable
vs Upamecano: €20M cheaper
86% match
€50.0M
#2
C
Castello Lukeba
RB Leipzig · Bundesliga
France23yContract 2029
Tkl/900.93
KP/900.00
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
vs Upamecano: €25M cheaper · 4y younger
86% match
€45.0M
#3
E
Edmond Tapsoba
Bayer 04 Leverkusen · Bundesliga
Burkina Faso27yContract 2028
Tkl/901.22
KP/900.11
Ball-Playing CBBall-Playing
Last 5: → Stable
vs Upamecano: €35M cheaper
86% match
€35.0M
#4
M
Min-jae Kim
FC Bayern München · Bundesliga
South Korea29yContract 2028
Tkl/901.07
KP/900.27
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
86% match
€25.0M
#5
N
Nordi Mukiele
Sunderland · Premier League
France28yContract 2029
Tkl/902.31
KP/900.72
Active Full-BackAerial
Last 5: → Stable
85% match
€15.0M
#6
R
Robin Koch
Eintracht Frankfurt · Bundesliga
Germany29yContract 2030
Tkl/901.50
KP/900.08
Ball-Playing CBAerial
85% match
€15.0M
#7
T
Tobias Müller
Magdeburg · Bundesliga
Germany31y
Tkl/901.65
KP/900.35
Ball-Playing CBBall-Playing
Last 5: → Stable
86% match
€5.0M
#8
D
David Raum
RB Leipzig · Bundesliga
Germany28yContract 2027
Tkl/901.81
KP/902.57
Active Full-BackBall-Playing
Last 5: → Stable
85% match
€22.0M
#9
K
Konrad Laimer
FC Bayern München · Bundesliga
Austria28yContract 2027
Tkl/902.63
KP/900.84
Active Full-BackBall-Playing
Last 5: → Stable
85% match
€32.0M
#10
A
Axel Tape
Bayer 04 Leverkusen · Bundesliga
France18yContract 2030
Tkl/900.50
KP/900.00
Solid DefenderSmall Sample
85% match
€8.0M
#11
B
Benjamin Pavard
Olympique Marseille · Ligue 1
France30yContract 2026
Tkl/901.34
KP/900.34
Ball-Playing CBBall-Playing
85% match
€15.0M
#12
L
Lukas Ullrich
Borussia Mönchengladbach · Bundesliga
Germany22yContract 2027
Tkl/901.33
KP/900.53
Active Full-Back
84% match
€7.0M

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 Dayot Upamecano.

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Frequently Asked Questions

Who are the best alternatives to Dayot Upamecano?
The top alternatives to Dayot Upamecano based on AI DNA playing style analysis include: Ibrahima Konaté, Castello Lukeba, Edmond Tapsoba, Min-jae Kim, Nordi Mukiele. 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 Dayot Upamecano in 2026?
Players with a similar profile to Dayot Upamecano in 2026 include Ibrahima Konaté (€50.0M), Castello Lukeba (€45.0M), Edmond Tapsoba (€35.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Dayot Upamecano play and who plays similarly?
Dayot Upamecano plays as a Defender. Players with a comparable positional profile include Ibrahima Konaté (France, €50.0M); Castello Lukeba (France, €45.0M); Edmond Tapsoba (Burkina Faso, €35.0M); Min-jae Kim (South Korea, €25.0M).
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.