RisingTransfers
AI DNA Similarity

Best Alternatives to Nikola Stulic

Players most similar to Nikola Stulic (Attacker, €4.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Nikola Stulic

  1. 1.Samuele Mulattieri84% DNA match·Sassuolo€6.0M
  2. 2.Sandro Kulenovic84% DNA match·Torino€4.0M
  3. 3.Nikola Krstović84% DNA match·Atalanta€20.0M

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

RT

Intelligence Verdict

Aerials WonTop 2%
???Bottom 0%

A Target Man....

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Playing Style Analysis

Target Man

A Target Man. Statistically, he stands out as a capable chance creator (1.1 key passes/90). The three most similar players to Nikola Stulic by playing style are:

  • Samuele Mulattieri(84% 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.
  • Sandro Kulenovic(84% match)A Complete Forward. Statistically, he stands out as a constant goal threat (3.7 shots/90), a proven goalscorer (1.07 goals/90), wins the physical battle (56% duel success), strong in aerial duels (3.0 aerials won/90) and top 10% scorer in the league. Note: this profile is based on 505 minutes of playing time this season.
  • Nikola Krstović(84% match)Krstović has quietly become one of Serie A's most dangerous forwards this season, combining a striker's ruthlessness with a surprisingly complete technical game. His 0.63 goals per 90 places him in the top 10% of the league, but the more telling figure is his shot volume—top 5% in Serie A—suggesting a player who doesn't wait for perfect chances, he manufactures them. The counterintuitive story here is his passing: 79.9% accuracy and 1.49 key passes per 90 mark him as someone who links play intelligently, not just a poacher lurking on the last shoulder.

Transfer Intelligence

Samuele Mulattieri delivers 84% of the same playing style, at a 33% premium over Nikola Stulic, and is 25 years old.

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

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Comparison Base
Nikola Stulic
AttackerSerbia€4.5M
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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 Nikola Stulic.

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Frequently Asked Questions

Who are the best alternatives to Nikola Stulic?
The top alternatives to Nikola Stulic based on AI DNA playing style analysis include: Samuele Mulattieri, Sandro Kulenovic, Nikola Krstović, Andrea Pinamonti, Andrej Ilic. 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 Nikola Stulic in 2026?
Players with a similar profile to Nikola Stulic in 2026 include Samuele Mulattieri (€6.0M), Sandro Kulenovic (€4.0M), Nikola Krstović (€20.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Nikola Stulic play and who plays similarly?
Nikola Stulic plays as a Attacker. Players with a comparable positional profile include Samuele Mulattieri (Italy, €6.0M); Sandro Kulenovic (Croatia, €4.0M); Nikola Krstović (Montenegro, €20.0M); 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.