Best Alternatives to Marc Casadó

Top 3 Alternatives to Marc Casadó

1. 1.Marc Aguado87% DNA match·Elche€3.0M
2. 2.Fermín López86% DNA match·FC Barcelona€70.0M
3. 3.Jon Moncayola86% DNA match·Osasuna€7.0M

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

Playing Style Analysis

A Metronome. Statistically, he stands out as a capable chance creator (1.1 key passes/90), meticulous in distribution (91% pass accuracy), heavily involved in possession (80 passes/90), penetrates with forward passing (8.3 final-third passes/90), central to possession (93 touches/90) and active off the ball (2.1 press score/90), contributing to defensive transitions. However, he prone to committing fouls (2.6/90). The three most similar players to Marc Casadó by playing style are:

• Marc Aguado(87% 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.
• Fermín López(86% match) — A Creator. Statistically, he stands out as an elite creator (1.8 key passes/90), a constant goal threat (3.1 shots/90), a regular goalscorer (0.29 goals/90), a prolific assist provider (0.51 assists/90), a dynamic dribbler (2.1/90), active in the tackle (2.3 tackles/90), meticulous in distribution (87% pass accuracy), creates high-quality scoring opportunities (0.80 big chances/90), heavily involved in play (64 touches/90), active off the ball (2.3 press score/90), contributing to defensive transitions and top 20% creator in the league.
• Jon Moncayola(86% match) — A Midfielder. Statistically, he stands out as heavily involved in play (60 touches/90), uses long balls frequently (6.4/90) and active off the ball (2.5 press score/90), contributing to defensive transitions.

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