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AI DNA Similarity

Best Alternatives to Mattia Valoti

Players most similar to Mattia Valoti (Midfielder, €3.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Mattia Valoti

  1. 1.Sandi Lovrić84% DNA match·Hellas Verona€6.0M
  2. 2.Tommaso Baldanzi84% DNA match·Genoa€10.0M
  3. 3.Michael Folorunsho84% DNA match·Cagliari€6.0M

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

RT

Intelligence Verdict

ShotsTop 1%
???Bottom 2%

Valoti is a statistical anomaly: a volume-shooting specialist masquerading as a central midfielder...

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Playing Style Analysis

Box-to-BoxSmall Sample

Valoti is a statistical anomaly: a volume-shooting specialist masquerading as a central midfielder for a struggling Tier C outfit. While his 414 minutes represent a partial sample, the output is undeniable; his 0.43 goals per 90 and shot volume place him in the elite top 5% of Serie A midfielders, operating more like a shadow striker than a pivot. He demands the ball constantly, ranking in the top 20% for passes attempted, yet he is a defensive ghost. The three most similar players to Mattia Valoti by playing style are:

  • Sandi Lovrić(84% match)A Creator. Statistically, he stands out as a capable chance creator (1.2 key passes/90), active in the tackle (1.9 tackles/90), meticulous in distribution (85% pass accuracy), heavily involved in play (52 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions.
  • Tommaso Baldanzi(84% match)Baldanzi is the kind of midfielder who makes a Roma coaching staff nervous when he's not on the pitch—not because he controls games, but because he finds the net at a rate that puts him in Serie A's top 20% despite playing in fragments this season. With just 278 minutes logged, every number here carries an asterisk, but the signal is consistent: 2.60 shots per 90 places him in the top 10% of league midfielders, and his 87.4% pass accuracy suggests someone who picks moments rather than floods the pitch with volume. The counterintuitive read? His below-average passes into the final third actually reflects how late he arrives into dangerous positions—as a finisher, not a creator.
  • Michael Folorunsho(84% match)A Balanced Midfielder.

Transfer Intelligence

Sandi Lovrić delivers 84% of the same playing style, at a 100% premium over Mattia Valoti, with 0.94 key passes per 90 at age 28.

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

M
Comparison Base
Mattia Valoti
MidfielderItaly€3.0M
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Similar Players — Ranked by DNA Similarity

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Our 768-dimension Player DNA model matches playing style, physical profile, pressing intensity, and tactical fit. Ask the AI to explain exactly what makes these players statistically similar to Mattia Valoti.

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Frequently Asked Questions

Who are the best alternatives to Mattia Valoti?
The top alternatives to Mattia Valoti based on AI DNA playing style analysis include: Sandi Lovrić, Tommaso Baldanzi, Michael Folorunsho, Cristian Volpato, Jari Vandeputte. These players were matched using Rising Transfers' 768-dimension DNA model across playing style, pressing intensity, and tactical fit — not just position or market value.
Which players are similar to Mattia Valoti in 2026?
Players with a similar profile to Mattia Valoti in 2026 include Sandi Lovrić (€6.0M), Tommaso Baldanzi (€10.0M), Michael Folorunsho (€6.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mattia Valoti play and who plays similarly?
Mattia Valoti plays as a Midfielder. Players with a comparable positional profile include Sandi Lovrić (Slovenia, €6.0M); Tommaso Baldanzi (Italy, €10.0M); Michael Folorunsho (Italy, €6.0M); Cristian Volpato (Italy, €10.0M).
How does Rising Transfers find similar players?
Rising Transfers uses a proprietary 768-dimension Player DNA model trained on 3.2 million match events. Each player is represented as a vector across 35+ per-90 metrics including pressing intensity, passing footprint, dribbling profile, and defensive contribution. Similarity is measured using cosine distance — the same technique used in state-of-the-art AI systems — making it the most precise player comparison tool available publicly.