Best Alternatives to Frank Anguissa 

Top 3 Alternatives to Frank Anguissa 

1. 1.Amadou Onana87% DNA match·Aston Villa€42.0M
2. 2.Tijjani Reijnders85% DNA match·Manchester City€60.0M
3. 3.Cher Ndour86% DNA match·Fiorentina€5.0M

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

Playing Style Analysis

A Box-to-Box. Statistically, he stands out as a capable chance creator (1.3 key passes/90), heavily involved in play (60 touches/90) and active off the ball (2.1 press score/90), contributing to defensive transitions. However, he loses possession under pressure (1.5 dispossessed/90). The three most similar players to Frank Anguissa  by playing style are:

• Amadou Onana(87% match) — A Box-to-Box. Statistically, he stands out as active in the tackle (2.4 tackles/90), meticulous in distribution (87% pass accuracy), wins the physical battle (64% duel success), heavily involved in play (58 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
• Tijjani Reijnders(85% match) — A Midfielder. Statistically, he stands out as a capable chance creator (1.0 key passes/90), a constant goal threat (2.6 shots/90), a regular goalscorer (0.29 goals/90), meticulous in distribution (89% pass accuracy) and heavily involved in play (61 touches/90).
• Cher Ndour(86% match) — Ndour is the kind of midfielder Serie A quietly relies on — not the one who makes the highlights, but the one who makes the machine run. His aerial numbers tell the most interesting story: winning 50% of his duels in the air while ranking in the top 20% for aerials won per 90 suggests a player who picks his battles intelligently rather than throwing himself at everything. His pass volume is above average, and he moves the ball into the final third at a healthy clip — yet here's the contradiction: his key passes sit in the bottom 10% of the league, meaning he circulates possession efficiently without ever quite unlocking anything.

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