Best Alternatives to Mandela Keita

Top 3 Alternatives to Mandela Keita

1. 1.Manu Koné87% DNA match·Roma€50.0M
2. 2.Ismaël Koné86% DNA match·Sassuolo€14.0M
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 Ball-Winner. Statistically, he stands out as an aggressive ball-winner (2.9 tackles/90), meticulous in distribution (89% pass accuracy), wins the physical battle (65% duel success), wins the ball cleanly (1.8 successful tackles/90), active off the ball (2.6 press score/90), contributing to defensive transitions and top 10% tackler in the league. The three most similar players to Mandela Keita by playing style are:

• Manu Koné(87% match) — A Box-to-Box. Statistically, he stands out as active in the tackle (1.9 tackles/90), meticulous in distribution (89% pass accuracy), heavily involved in play (65 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
• Ismaël Koné(86% match) — A Box-to-Box. Statistically, he stands out as a regular goalscorer (0.34 goals/90), meticulous in distribution (92% pass accuracy), wins the physical battle (64% duel success) and heavily involved in play (52 touches/90). Note: this profile is based on 797 minutes of playing time this season.
• 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 →