RisingTransfers
AI DNA Similarity

Best Alternatives to Min-jae Kim

Players most similar to Min-jae Kim (Defender, €25.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Min-jae Kim

  1. 1.Benjamin Pavard87% DNA match·Olympique Marseille€15.0M
  2. 2.Jonathan Tah86% DNA match·FC Bayern München€30.0M
  3. 3.Dayot Upamecano86% DNA match·FC Bayern München€70.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%

A Ball-Playing CB....

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Playing Style Analysis

Ball-Playing CBBall-PlayingAerial

A Ball-Playing CB. Statistically, he stands out as commanding in the air (4.6 clearances/90), reads the game exceptionally (2.1 interceptions/90), meticulous in distribution (95% pass accuracy), wins the physical battle (64% duel success), heavily involved in possession (88 passes/90), penetrates with forward passing (9.0 final-third passes/90), central to possession (101 touches/90) and a high-intensity presser (press score 3.1/90), constantly disrupting opposition build-up. The three most similar players to Min-jae Kim by playing style are:

  • Benjamin Pavard(87% match)A Ball-Playing CB. Statistically, he stands out as meticulous in distribution (92% pass accuracy), wins the physical battle (67% duel success), heavily involved in possession (63 passes/90) and central to possession (75 touches/90).
  • Jonathan Tah(86% match)A Ball-Playing CB. Statistically, he stands out as commanding in the air (5.4 clearances/90), meticulous in distribution (96% pass accuracy), wins the physical battle (60% duel success), heavily involved in possession (86 passes/90), penetrates with forward passing (8.1 final-third passes/90), central to possession (98 touches/90) and active off the ball (2.2 press score/90), contributing to defensive transitions.
  • Dayot Upamecano(86% match)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.

Transfer Intelligence

Benjamin Pavard delivers 87% of the same playing style, at 40% lower cost (€15.0M vs €25.0M), with 1.34 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
Min-jae Kim
DefenderSouth Korea€25.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
B
Benjamin Pavard
Olympique Marseille · Ligue 1
France30yContract 2026
Tkl/901.34
KP/900.34
Ball-Playing CBBall-Playing
vs Kim: €10M cheaper
87% match
€15.0M
#2
J
Jonathan Tah
FC Bayern München · Bundesliga
Germany30yContract 2029
Tkl/901.28
KP/900.24
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
86% match
€30.0M
#3
D
Dayot Upamecano
FC Bayern München · Bundesliga
France27yContract 2026
Tkl/902.13
KP/900.44
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
vs Kim: €45M more expensive · 2y younger
86% match
€70.0M
#4
J
Jarell Quansah
Bayer 04 Leverkusen · Bundesliga
England23yContract 2030
Tkl/900.73
KP/900.27
Ball-Playing CBBall-Playing
Last 5: → Stable
86% match
€40.0M
#5
T
Tobias Müller
Magdeburg · Bundesliga
Germany31y
Tkl/901.65
KP/900.35
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€5.0M
#6
L
Loïc Badé
Bayer 04 Leverkusen · Bundesliga
France26yContract 2030
Tkl/902.37
KP/900.59
Ball-Playing CBBall-Playing
85% match
€25.0M
#7
E
Edmond Tapsoba
Bayer 04 Leverkusen · Bundesliga
Burkina Faso27yContract 2028
Tkl/901.22
KP/900.11
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€35.0M
#8
R
Rúben Dias 
Manchester City · Premier League
Portugal28yContract 2027
Tkl/900.61
KP/900.26
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
84% match
€60.0M
#9
W
William Saliba
Arsenal · Premier League
France25yContract 2027
Tkl/901.28
KP/900.36
Ball-Playing CBBall-Playing
Last 5: → Stable
84% match
€90.0M
#10
R
Rav van den Berg
FC Köln · Bundesliga
Netherlands21yContract 2030
Tkl/901.25
KP/900.00
Ball-Playing CBBall-Playing
85% match
€8.0M
#11
R
Robin Koch
Eintracht Frankfurt · Bundesliga
Germany29yContract 2030
Tkl/901.50
KP/900.08
Ball-Playing CBAerial
84% match
€15.0M
#12
J
J. Timber
Arsenal · Premier League
Netherlands24yContract 2028
Tkl/902.42
KP/900.99
Active Full-Back
Last 5: → Stable
84% match
€70.0M

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 Min-jae Kim.

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Frequently Asked Questions

Who are the best alternatives to Min-jae Kim?
The top alternatives to Min-jae Kim based on AI DNA playing style analysis include: Benjamin Pavard, Jonathan Tah, Dayot Upamecano, Jarell Quansah, Tobias Müller. 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 Min-jae Kim in 2026?
Players with a similar profile to Min-jae Kim in 2026 include Benjamin Pavard (€15.0M), Jonathan Tah (€30.0M), Dayot Upamecano (€70.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Min-jae Kim play and who plays similarly?
Min-jae Kim plays as a Defender. Players with a comparable positional profile include Benjamin Pavard (France, €15.0M); Jonathan Tah (Germany, €30.0M); Dayot Upamecano (France, €70.0M); Jarell Quansah (England, €40.0M).
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.