⬡ AI DNA · Live season

Ulsan HD· 2026 · K League 1 · PARTIAL

Best Alternatives to Dong-gyeong Lee

Attacking MidfieldSouth Korea28yN/AFull profile →

CreatorCreative

3.14KP/90

0.39G/90

0.39A/90

Based on 1,015 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 Dong-gyeong Lee

Adil Aouchiche98% match
Schalke 04 · Bundesliga
Jamal Musiala98% match
FC Bayern München · Bundesliga · €130.0M
Kevin De Bruyne 98% match
Napoli · Serie A · €10.0M

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

CreatorCreative

A Creator. Statistically, he stands out as an elite creator (3.4 key passes/90), a constant goal threat (3.6 shots/90), a proven goalscorer (0.44 goals/90), a prolific assist provider (0.27 assists/90), delivers dangerous crosses (2.6 accurate crosses/90) and top 10% creator in the league. The three closest DNA matches to Dong-gyeong Lee:

Adil Aouchiche(98% match) — A Creator. Statistically, he stands out as an elite creator (3.8 key passes/90), a constant goal threat (3.3 shots/90), a regular goalscorer (0.24 goals/90), a reliable supplier (0.16 assists/90), creates high-quality scoring opportunities (0.88 big chances/90), delivers dangerous crosses (3.1 accurate crosses/90), heavily involved in play (66 touches/90), active off the ball (2.5 press score/90), contributing to defensive transitions and top 10% creator in the league.
Jamal Musiala(98% match) — A Creator. Statistically, he stands out as an elite creator (2.2 key passes/90), a constant goal threat (4.0 shots/90), a proven goalscorer (0.40 goals/90), a prolific assist provider (0.53 assists/90), a dynamic dribbler (2.8/90), heavily involved in play (66 touches/90), draws fouls effectively (2.4/90) and top 10% creator in the league.
Kevin De Bruyne (98% match) — A Creator. Statistically, he stands out as an elite creator (2.9 key passes/90), a regular goalscorer (0.38 goals/90), a reliable supplier (0.15 assists/90), creates high-quality scoring opportunities (0.54 big chances/90), penetrates with forward passing (8.2 final-third passes/90), central to possession (72 touches/90) and top 10% creator in the league.

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

Comparison Base

Dong-gyeong Lee

MidfielderSouth KoreaK League 1N/A

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Similar Players — Ranked by DNA Similarity

Adil Aouchiche

Schalke 04 · Bundesliga

France23yContract 2027

KP/901.33

G/900.21

CreatorCreativeIn form ↑

vs Lee: 5y younger

98% match

N/A

Compare Lee vs Aouchiche →

Ask about replacements for Lee

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Frequently Asked Questions

Who are the best alternatives to Dong-gyeong Lee?▼

The top alternatives to Dong-gyeong Lee based on AI DNA playing style analysis include: Adil Aouchiche, Jamal Musiala, Kevin De Bruyne , Martin Kobylanski, Javi Hernández. 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 Dong-gyeong Lee in 2026?▼

Players with a similar profile to Dong-gyeong Lee in 2026 include Adil Aouchiche (N/A), Jamal Musiala (€130.0M), Kevin De Bruyne (€10.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Dong-gyeong Lee play and who plays similarly?▼

Dong-gyeong Lee plays as a Midfielder. Players with a comparable positional profile include Adil Aouchiche (France, N/A); Jamal Musiala (Germany, €130.0M); Kevin De Bruyne (Belgium, €10.0M); Martin Kobylanski (Poland, 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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