Best Alternatives to Nasser Djiga

Top 3 Alternatives to Nasser Djiga

1. 1.Bafodé Diakité85% DNA match·AFC Bournemouth€30.0M
2. 2.Abdoulaye Ndiaye85% DNA match·Parma€6.5M
3. 3.Christian Mawissa85% DNA match·Monaco€15.0M

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

Playing Style Analysis

A Ball-Playing CB. Statistically, he stands out as commanding in the air (5.2 clearances/90), reads the game exceptionally (1.7 interceptions/90), meticulous in distribution (93% pass accuracy), wins the physical battle (66% duel success), heavily involved in possession (61 passes/90), central to possession (75 touches/90) and active off the ball (2.8 press score/90), contributing to defensive transitions. The three most similar players to Nasser Djiga by playing style are:

• Bafodé Diakité(85% match) — A Ball-Playing CB. Statistically, he stands out as commanding in the air (5.8 clearances/90), meticulous in distribution (88% pass accuracy) and uses long balls frequently (5.1/90).
• Abdoulaye Ndiaye(85% match) — A Ball-Playing CB. Statistically, he stands out as naturally left-footed, active in the tackle (1.9 tackles/90), commanding in the air (4.9 clearances/90), wins the physical battle (57% duel success), uses long balls frequently (5.3/90) and active off the ball (2.0 press score/90), contributing to defensive transitions. Note: this profile is based on 752 minutes of playing time this season.
• Christian Mawissa(85% match) — A Ball-Playing CB. Statistically, he stands out as naturally left-footed, an aggressive ball-winner (2.5 tackles/90), reads the game exceptionally (1.5 interceptions/90), meticulous in distribution (90% pass accuracy), wins the physical battle (60% duel success), heavily involved in possession (63 passes/90), wins the ball cleanly (2.0 successful tackles/90), central to possession (83 touches/90) and a high-intensity presser (press score 3.3/90), constantly disrupting opposition build-up.

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