⬡ AI DNA · Live season

Pohang Steelers· 2026 · K League 1 · PARTIAL

Best Alternatives to Sung-yueng Ki

Defensive MidfieldSouth Korea37y€7.0MFull profile →

MetronomeCreativeSmall Sample

1.25KP/90

0.00G/90

0.00A/90

Based on 559 min · 2026 · K League 1 · PARTIAL

Ranked by AI DNA similarity — playing style, pressing, and tactical fit. Below: the closest replacements with per-90 stats and form.

DNA matchPrioritizes K League 1 & top-tier peers

Top 3 alternatives to Sung-yueng Ki

Rafael Filipe Gonçalves Soares Luís96% match
Strasbourg · Ligue 1 · €4.0M
Marc Bernal95% match
FC Barcelona · La Liga · €10.0M
Charlie Gray99% match
Manchester City · Premier League

Ranked by RT Player DNA + league/value credibility. Scroll for full list, per-90 stats, and share cards.

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Playing Style Analysis

MetronomeCreativeSmall Sample

A Metronome. Statistically, he stands out as an elite creator (1.8 key passes/90), a reliable supplier (0.16 assists/90), meticulous in distribution (90% pass accuracy), wins the physical battle (57% duel success), delivers dangerous crosses (3.1 accurate crosses/90), central to possession (74 touches/90) and top 20% creator in the league. Note: this profile is based on 559 minutes of playing time this season. The three closest DNA matches to Sung-yueng Ki:

Rafael Filipe Gonçalves Soares Luís(96% match) — A Ball-Winner. Statistically, he stands out as naturally left-footed, a reliable supplier (0.15 assists/90), an aggressive ball-winner (2.8 tackles/90), meticulous in distribution (88% pass accuracy), central to possession (74 touches/90) and uses long balls frequently (5.5/90).
Marc Bernal(95% match) — A Ball-Winner. Statistically, he stands out as naturally left-footed, a regular goalscorer (0.23 goals/90), an aggressive ball-winner (3.0 tackles/90), meticulous in distribution (92% pass accuracy), wins the physical battle (59% duel success), heavily involved in possession (67 passes/90), wins the ball cleanly (2.2 successful tackles/90), central to possession (84 touches/90), active off the ball (2.4 press score/90), contributing to defensive transitions and top 10% tackler in the league.
Charlie Gray(99% match) — A Ball-Winner. Statistically, he stands out as a capable chance creator (1.0 key passes/90), a reliable supplier (0.17 assists/90), meticulous in distribution (92% pass accuracy), heavily involved in play (56 touches/90) and top 10% creator in the league.

Similarity uses per-90 performance across playing-style dimensions. How Player DNA matching works →

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Similar Players — Ranked by DNA Similarity

Rafael Filipe Gonçalves Soares Luís

Strasbourg · Ligue 1

Portugal21yContract 2026

KP/900.92

G/900.00

Ball-WinnerDefensiveSteady form

vs Ki: 16y younger

96% match

€4.0M

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Same DNA shortlist as above — budget fit, who to avoid, or why the top match works.

Knows 56,883 players. Knows 3,481 clubs.

Frequently Asked Questions

Who are the best alternatives to Sung-yueng Ki?▼

The top alternatives to Sung-yueng Ki based on AI DNA playing style analysis include: Rafael Filipe Gonçalves Soares Luís, Marc Bernal, Charlie Gray, Mussa Kaba, Landon Emenalo. 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 Sung-yueng Ki in 2026?▼

Players with a similar profile to Sung-yueng Ki in 2026 include Rafael Filipe Gonçalves Soares Luís (€4.0M), Marc Bernal (€10.0M), Charlie Gray (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Sung-yueng Ki play and who plays similarly?▼

Sung-yueng Ki plays as a Midfielder. Players with a comparable positional profile include Rafael Filipe Gonçalves Soares Luís (Portugal, €4.0M); Marc Bernal (Spain, €10.0M); Charlie Gray (England, N/A); Mussa Kaba (Guinea, N/A).

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.

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