Best Alternatives to David Keltjens

Top 3 Alternatives to David Keltjens

1. 1.Oussama Targhalline83% DNA match·Feyenoord€8.0M
2. 2.Robin Fellhauer82% DNA match·FC Augsburg€3.0M
3. 3.Hugo Magnetti82% DNA match·Brest€6.0M

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

Playing Style Analysis

A Balanced Midfielder. Statistically, he stands out as active in the tackle (2.2 tackles/90), wins the physical battle (59% duel success) and heavily involved in play (51 touches/90). The three most similar players to David Keltjens by playing style are:

• Oussama Targhalline(83% match) — Targhalline is the kind of midfielder who wins games in the spaces most fans never notice—a relentless ground-level disruptor who ranks in the Eredivisie's top 5% for duel success and top 10% for both interceptions and tackles won. His shot volume sits in the bottom 10% of the league, which looks damning until you realise his game is built entirely around controlling territory rather than claiming it for himself—he wins the ball, moves it accurately at 85.2%, and consistently threads passes into the final third at a top-20% clip. The counterintuitive truth is that his low key pass numbers don't signal a lack of ambition; they signal a player who transitions quickly rather than loitering in creative zones.
• Robin Fellhauer(82% 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.
• Hugo Magnetti(82% match) — A Balanced Midfielder. Statistically, he stands out as active in the tackle (2.4 tackles/90), reads the game exceptionally (1.5 interceptions/90), meticulous in distribution (87% pass accuracy), heavily involved in play (53 touches/90) and a high-intensity presser (press score 3.2/90), constantly disrupting opposition build-up.

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