Best Alternatives to Miha Zajc

Top 3 Alternatives to Miha Zajc

1. 1.Kristijan Jakic85% DNA match·FC Augsburg€6.0M
2. 2.Sandi Lovrić83% DNA match·Hellas Verona€6.0M
3. 3.Lovro Majer84% DNA match·VfL Wolfsburg€15.0M

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

Playing Style Analysis

Zajc is a high-volume creative engine operating at a frequency far too sophisticated for the 1. HNL, effectively serving as a cheat code for ball progression and final-third execution. His statistical profile is elite, characterized by a staggering 3.54 key passes and 0.57 assists per 90, figures that place him in the top 5% of all midfielders in the league. The three most similar players to Miha Zajc by playing style are:

• Kristijan Jakic(85% match) — A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.1 tackles/90), penetrates with forward passing (8.6 final-third passes/90), wins the ball cleanly (2.3 successful tackles/90), heavily involved in play (67 touches/90), uses long balls frequently (6.3/90), a high-intensity presser (press score 3.6/90), constantly disrupting opposition build-up and top 10% tackler in the league. Note: this profile is based on 470 minutes of playing time this season.
• Sandi Lovrić(83% match) — A Creator. Statistically, he stands out as a capable chance creator (1.2 key passes/90), active in the tackle (1.9 tackles/90), meticulous in distribution (85% pass accuracy), heavily involved in play (52 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
• Lovro Majer(84% match) — A Creator. Statistically, he stands out as naturally left-footed, a capable chance creator (1.3 key passes/90), a reliable supplier (0.19 assists/90), active in the tackle (1.9 tackles/90), heavily involved in play (55 touches/90) and active off the ball (2.9 press score/90), contributing to defensive transitions. Note: this profile is based on 482 minutes of playing time this season.

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