RisingTransfers
AI DNA Similarity

Best Alternatives to Lorenzo Colombo

Players most similar to Lorenzo Colombo (Attacker, €6.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Lorenzo Colombo

  1. 1.Samuele Mulattieri86% DNA match·Sassuolo€6.0M
  2. 2.Roberto Piccoli86% DNA match·Fiorentina€18.0M
  3. 3.Patrick Cutrone85% DNA match·Parma€5.0M

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

RT

Intelligence Verdict

Aerials WonTop 4%
???Bottom 0%

A Target Man....

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Playing Style Analysis

Target Man

A Target Man. Statistically, he stands out as naturally left-footed, a regular goalscorer (0.32 goals/90) and draws fouls effectively (2.1/90). However, he loses possession under pressure (2.4 dispossessed/90). The three most similar players to Lorenzo Colombo by playing style are:

  • Samuele Mulattieri(86% match)Mulattieri is the kind of striker who wins headers in Serie A but struggles to win the ball at his feet—a physical presence in the box who remains curiously invisible in open play. His aerial win rate sits above the league average for attackers, a legitimate weapon in the right system, and his dribble volume places him in the top 20%—suggesting more directness than his overall profile implies. The counterintuitive read here: his low pass volume isn't laziness, it's positioning; he operates in tight spaces where recycling the ball simply isn't his job.
  • Roberto Piccoli(86% match)Piccoli is the kind of striker Serie A defences respect in the air but rarely fear on the ball—a blunt instrument with genuine aerial menace in a league that still values it. His 3.06 aerial wins per 90 place him comfortably in the top 20% of attackers, and his shot volume matches that tier too, suggesting a forward who gets into positions even if he doesn't always finish them. Here's the counterintuitive part: that dismal pass accuracy—bottom 10% in the league—isn't necessarily laziness or sloppiness.
  • Patrick Cutrone(85% match)A Forward. Statistically, he stands out as a constant goal threat (2.5 shots/90). Note: this profile is based on 895 minutes of playing time this season.

Transfer Intelligence

Samuele Mulattieri delivers 86% of the same playing style, and is 25 years old.

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

L
Comparison Base
Lorenzo Colombo
AttackerItaly€6.0M
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Similar Players — Ranked by DNA Similarity

Ask AI: Why are these players similar?

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 Lorenzo Colombo.

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Frequently Asked Questions

Who are the best alternatives to Lorenzo Colombo?
The top alternatives to Lorenzo Colombo based on AI DNA playing style analysis include: Samuele Mulattieri, Roberto Piccoli, Patrick Cutrone, Andrea Pinamonti, Matija Frigan. 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 Lorenzo Colombo in 2026?
Players with a similar profile to Lorenzo Colombo in 2026 include Samuele Mulattieri (€6.0M), Roberto Piccoli (€18.0M), Patrick Cutrone (€5.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Lorenzo Colombo play and who plays similarly?
Lorenzo Colombo plays as a Attacker. Players with a comparable positional profile include Samuele Mulattieri (Italy, €6.0M); Roberto Piccoli (Italy, €18.0M); Patrick Cutrone (Italy, €5.0M); Andrea Pinamonti (Italy, €15.0M).
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.