Best Alternatives to Kevin Agudelo

Top 3 Alternatives to Kevin Agudelo

1. 1.Marc Aguado84% DNA match·Elche€3.0M
2. 2.Alex Freeman82% DNA match·Villarreal€3.5M
3. 3.Federico Redondo83% DNA match·Elche€4.0M

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

Playing Style Analysis

A Creator. Statistically, he stands out as an elite creator (2.1 key passes/90), a dynamic dribbler (2.7/90), heavily involved in play (54 touches/90) and top 10% creator in the league. However, he loses possession under pressure (2.0 dispossessed/90). The three most similar players to Kevin Agudelo by playing style are:

• Marc Aguado(84% match) — A Ball-Winner. Statistically, he stands out as active in the tackle (1.9 tackles/90), reads the game exceptionally (1.8 interceptions/90), meticulous in distribution (91% pass accuracy), wins the physical battle (63% duel success), heavily involved in play (57 touches/90) and active off the ball (2.2 press score/90), contributing to defensive transitions.
• Alex Freeman(82% match) — A Active Full-Back. Statistically, he stands out as an elite creator (1.6 key passes/90), a regular goalscorer (0.21 goals/90), wins the physical battle (58% duel success) and active off the ball (2.4 press score/90), contributing to defensive transitions.
• Federico Redondo(83% match) — Redondo is the quintessential "volume controller" whose surgical distribution and relentless defensive work rate suggest a player far more physically imposing than his duel success rate implies. Operating at a Tier C club, he is effectively the entire engine room, ranking in the top 5% for both passes per 90 (84.3) and tackles won (1.91), a rare statistical profile that combines the elegance of a deep-lying playmaker with the grit of a ball-winner. The counterintuitive insight lies in his goalscoring; despite sitting in the bottom 10% for shot volume, his 0.27 goals per 90 (top 5%) reveals a lethal efficiency when he does arrive late in the box.

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