Best Alternatives to Andrei Girotto

Top 3 Alternatives to Andrei Girotto

1. 1.Filippo Terracciano85% DNA match·Cremonese€4.5M
2. 2.Jorge Herrando85% DNA match·Osasuna€3.5M
3. 3.Jozhua Vertrouwd84% DNA match·Rayo Vallecano€3.0M

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

Playing Style Analysis

Girotto functions as a deep-lying quarterback masquerading as a center-back, a profile that prioritizes surgical distribution over traditional defensive grit. His 91.5% pass accuracy puts him in the league’s elite top 10%, serving as the primary progressive engine for a Tier C side by funneling nearly six passes into the final third every ninety minutes. While his 1.52 aerials won per game suggests a physical presence, the counterintuitive reality is a dismal 33.3% success rate in duels; he is a defender who occupies the right spaces but lacks the raw combative edge to dominate them. The three most similar players to Andrei Girotto by playing style are:

• Filippo Terracciano(85% match) — A Ball-Playing CB. Statistically, he stands out as reads the game exceptionally (1.8 interceptions/90), meticulous in distribution (86% pass accuracy), wins the physical battle (61% duel success) and active off the ball (2.5 press score/90), contributing to defensive transitions.
• Jorge Herrando(85% match) — A Ball-Playing CB. Statistically, he stands out as naturally left-footed, commanding in the air (5.8 clearances/90) and meticulous in distribution (88% pass accuracy). Note: this profile is based on 892 minutes of playing time this season.
• Jozhua Vertrouwd(84% match) — A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.8 tackles/90), meticulous in distribution (91% pass accuracy), heavily involved in possession (63 passes/90), central to possession (75 touches/90) and active off the ball (2.0 press score/90), contributing to defensive transitions. Note: this profile is based on 498 minutes of playing time this season.

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