Best Alternatives to Roman Macek

Top 3 Alternatives to Roman Macek

1. 1.Robin Fellhauer83% DNA match·FC Augsburg€3.0M
2. 2.M. Madsen83% DNA match·FC København€4.0M
3. 3.Niklas Dorsch83% DNA match·Heidenheim€3.0M

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

Playing Style Analysis

A Metronome. Statistically, he stands out as a capable chance creator (1.2 key passes/90), an aggressive ball-winner (2.7 tackles/90), reads the game exceptionally (1.6 interceptions/90), wins the physical battle (62% duel success), heavily involved in possession (66 passes/90), penetrates with forward passing (11.4 final-third passes/90), central to possession (83 touches/90), draws fouls effectively (2.3/90), uses long balls frequently (8.5/90), a high-intensity presser (press score 3.6/90), constantly disrupting opposition build-up and top 10% tackler in the league. The three most similar players to Roman Macek by playing style are:

• Robin Fellhauer(83% match) — A Box-to-Box. Statistically, he stands out as active in the tackle (1.9 tackles/90), heavily involved in play (58 touches/90) and active off the ball (2.5 press score/90), contributing to defensive transitions. Note: this profile is based on 761 minutes of playing time this season.
• M. Madsen(83% match) — A Creator. Statistically, he stands out as an elite creator (1.5 key passes/90), a regular goalscorer (0.20 goals/90), an aggressive ball-winner (3.3 tackles/90), heavily involved in play (67 touches/90), active off the ball (2.5 press score/90), contributing to defensive transitions and top 10% tackler in the league.
• Niklas Dorsch(83% match) — A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (2.9 tackles/90), meticulous in distribution (87% pass accuracy), wins the physical battle (60% duel success), heavily involved in play (50 touches/90), active off the ball (2.9 press score/90), contributing to defensive transitions and top 10% tackler in the league.

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