Best Alternatives to Mads Enggård

Top 3 Alternatives to Mads Enggård

1. 1.M. Madsen88% DNA match·FC København€4.0M
2. 2.Yannik Engelhardt86% DNA match·Borussia Mönchengladbach€6.0M
3. 3.Morten Hjulmand85% DNA match·Sporting CP€45.0M

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

Playing Style Analysis

A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.0 tackles/90), wins the ball cleanly (1.8 successful tackles/90), heavily involved in play (60 touches/90), uses long balls frequently (5.2/90) and a high-intensity presser (press score 3.3/90), constantly disrupting opposition build-up. The three most similar players to Mads Enggård by playing style are:

• M. Madsen(88% 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.
• Yannik Engelhardt(86% match) — A Box-to-Box. Statistically, he stands out as a regular goalscorer (0.22 goals/90), active in the tackle (2.2 tackles/90), heavily involved in play (61 touches/90) and active off the ball (2.4 press score/90), contributing to defensive transitions. Note: this profile is based on 816 minutes of playing time this season.
• Morten Hjulmand(85% match) — Hjulmand is the kind of midfielder who wins football matches without ever appearing on a highlight reel—a defensive engine wrapped inside a genuine ball-progressor. Operating in Liga Portugal, he ranks in the top 5% for both duel win rate and press intensity, which tells you something important: this isn't selective aggression, it's sustained, systemic dominance across ninety minutes. His 89% pass accuracy and 8.11 progressive passes per ninety further expose the counterintuitive truth—the man built like a destroyer actually drives play forward more consistently than most creative midfielders in the division.

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