Best Alternatives to Luka Ilic

Top 3 Alternatives to Luka Ilic

1. 1.Ivan Ilić88% DNA match·Torino€10.0M
2. 2.Luka Sucic85% DNA match·Real Sociedad€10.0M
3. 3.Filip Ugrinic84% DNA match·Valencia€6.0M

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

Playing Style Analysis

A Creator. Statistically, he stands out as naturally left-footed, active in the tackle (1.8 tackles/90) and active off the ball (2.2 press score/90), contributing to defensive transitions. However, he can be exposed in 1v1 situations. The three most similar players to Luka Ilic by playing style are:

• Ivan Ilić(88% match) — Ilić is the kind of midfielder who makes the game look slower than it is—not because he's languid, but because he's almost always in the right place before the problem arrives. His interceptions and key passes both sit in the top 20% of Serie A midfielders, a rare pairing that signals genuine two-way intelligence rather than a specialist's one-trick value. Here's the counterintuitive part: his duel win rate sits in the bottom 10%, which looks damning until you realize that a midfielder winning this many interceptions rarely needs to win duels—he's already cut the pass off.
• Luka Sucic(85% match) — A Balanced Midfielder. Statistically, he stands out as naturally left-footed, a capable chance creator (1.0 key passes/90) and heavily involved in play (52 touches/90). However, he loses possession under pressure (1.7 dispossessed/90).
• Filip Ugrinic(84% match) — A Box-to-Box. Statistically, he stands out as a capable chance creator (1.5 key passes/90), a prolific assist provider (0.34 assists/90), active in the tackle (1.8 tackles/90), wins the physical battle (57% duel success), creates high-quality scoring opportunities (0.56 big chances/90), heavily involved in play (59 touches/90) and active off the ball (2.5 press score/90), contributing to defensive transitions. Note: this profile is based on 799 minutes of playing time this season.

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