Best Alternatives to Fabio Miretti

Top 3 Alternatives to Fabio Miretti

1. 1.Nicolò Fagioli90% DNA match·Fiorentina€14.0M
2. 2.Lennon Miller87% DNA match·Udinese€8.0M
3. 3.Cesare Casadei87% DNA match·Torino€14.0M

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

Playing Style Analysis

Miretti is the rare academy product who graduated from Juventus's shadow and found his voice in the chaos of a mid-table Serie A environment. At just 544 minutes of data, the sample is partial—but what it reveals is striking. His assists rate lands in the top 5% of Serie A midfielders, a figure that flatters him only if you ignore the equally elite press intensity and shot volume sitting underneath it. The three most similar players to Fabio Miretti by playing style are:

• Nicolò Fagioli(90% match) — Fagioli is the kind of midfielder who makes a possession system hum without ever demanding the spotlight. His 90.7% pass accuracy and 2.51 key passes per 90 both rank in Serie A's top 5% among midfielders—numbers that suggest not just tidiness, but genuine creative authority in the final third. His 8.91 passes into the final third per 90 puts him comfortably in the top 10%, which is where the real story lives: the assists figure looks modest, but Fagioli is consistently threading the needle before the last action, not after it.
• Lennon Miller(87% match) — A Box-to-Box. Statistically, he stands out as a capable chance creator (1.2 key passes/90), a prolific assist provider (0.30 assists/90), an aggressive ball-winner (2.6 tackles/90), wins the physical battle (61% duel success), heavily involved in play (55 touches/90) and active off the ball (2.6 press score/90), contributing to defensive transitions. Note: this profile is based on 591 minutes of playing time this season.
• Cesare Casadei(87% match) — A Ball-Winner. Statistically, he stands out as a regular goalscorer (0.27 goals/90) and dominant in the air (4.2 aerials won/90, 60%).

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