Best Alternatives to Florent Hadergjonaj

Top 3 Alternatives to Florent Hadergjonaj

1. 1.Enis Bardhi86% DNA match·Konyaspor€3.0M
2. 2.Ernest Muci85% DNA match·Trabzonspor€11.0M
3. 3.Kacper Kozlowski84% DNA match·Gaziantep F.K.€5.0M

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 an elite creator (1.8 key passes/90), a regular goalscorer (0.25 goals/90), a prolific assist provider (0.34 assists/90), wins the physical battle (60% duel success), penetrates with forward passing (8.4 final-third passes/90), heavily involved in play (65 touches/90) and uses long balls frequently (6.1/90). The three most similar players to Florent Hadergjonaj by playing style are:

• Enis Bardhi(86% match) — Bardhi is a dead-ball specialist masquerading as a central cog, a player who operates with the surgical precision of a playmaker but carries the shot volume of a secondary striker. While his 33.7 passes per match suggest a modest involvement in build-up, his output is pure efficiency; he ranks in the top 5% of Super Lig midfielders for key passes and the top 10% for goals. This discrepancy reveals the counterintuitive reality of his game: Bardhi isn't a high-volume engine, but a high-leverage executioner who waits for the decisive moment to strike or slide a ball into the final third.
• Ernest Muci(85% match) — A Creator. Statistically, he stands out as an elite creator (2.4 key passes/90), a constant goal threat (3.8 shots/90), a proven goalscorer (0.58 goals/90), a reliable supplier (0.16 assists/90), meticulous in distribution (87% pass accuracy), heavily involved in play (57 touches/90) and top 10% creator in the league.
• Kacper Kozlowski(84% match) — A Creator. Statistically, he stands out as a capable chance creator (1.2 key passes/90), a regular goalscorer (0.21 goals/90), draws fouls effectively (3.1/90) and active off the ball (2.0 press score/90), contributing to defensive transitions.

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