⬡ AI DNA · Live season

Lecce· 2024 · Serie A · PARTIAL

Best Alternatives to Filip Marchwinski

Attacking MidfieldPoland24y€2.5MFull profile →

Small Sample

0.00KP/90

0.00G/90

0.00A/90

Based on 353 min · 2024 · Serie A · PARTIAL

Ranked by AI DNA similarity — playing style, pressing, and tactical fit. Below: the closest replacements with per-90 stats and form.

DNA matchPrioritizes Serie A & top-tier peers

Top 3 alternatives to Filip Marchwinski

Nico Paz80% match
Como · Serie A · €65.0M
Martin Baturina80% match
Como · Serie A · €30.0M
Antonio Vergara80% match
Napoli · Serie A · €15.0M

Ranked by RT Player DNA + league/value credibility. Scroll for full list, per-90 stats, and share cards.

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Playing Style Analysis

Small Sample

Marchwiński represents the quintessential Serie A gamble: a towering Polish playmaker oscillating between moments of technical grace and the harsh physical realities of Italian football. With only 353 minutes under his belt for a struggling Tier C side, his statistical profile is currently a graveyard of efficiency, specifically his pass accuracy which languishes in the bottom 10% of the league’s midfielders. While a 6'3" frame suggests a target man, the data reveals a surprising fragility in the air, with aerial win rates falling well below the league average. The three closest DNA matches to Filip Marchwinski:

Nico Paz(80% match) — A Creator. Statistically, he stands out as naturally left-footed, an elite creator (1.6 key passes/90), a constant goal threat (3.8 shots/90), a regular goalscorer (0.37 goals/90), a reliable supplier (0.19 assists/90), a dynamic dribbler (2.1/90), an aggressive ball-winner (2.8 tackles/90), heavily involved in play (66 touches/90), active off the ball (2.5 press score/90), contributing to defensive transitions, top 20% creator in the league and top 10% tackler in the league.
Martin Baturina(80% match) — A Creator. Statistically, he stands out as an elite creator (3.6 key passes/90), a regular goalscorer (0.34 goals/90), a reliable supplier (0.17 assists/90), meticulous in distribution (88% pass accuracy), heavily involved in play (67 touches/90), draws fouls effectively (2.5/90), active off the ball (2.2 press score/90), contributing to defensive transitions and top 10% creator in the league.
Antonio Vergara(80% match) — A Creator. Statistically, he stands out as an elite creator (1.6 key passes/90), a prolific assist provider (0.29 assists/90), heavily involved in play (51 touches/90), draws fouls effectively (2.9/90), active off the ball (2.5 press score/90), contributing to defensive transitions and top 20% creator in the league.

Similarity uses per-90 performance across playing-style dimensions. How Player DNA matching works →

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Similar Players — Ranked by DNA Similarity

Nico Paz

Como · Serie A

Argentina21yContract 2028

KP/901.59

G/900.37

CreatorCreativeSteady form

vs Marchwinski: €63M more expensive · 3y younger

80% match

€65.0M

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Frequently Asked Questions

Who are the best alternatives to Filip Marchwinski?▼

The top alternatives to Filip Marchwinski based on AI DNA playing style analysis include: Nico Paz, Martin Baturina, Antonio Vergara, Daniel Maldini, Matteo Tramoni. 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 Filip Marchwinski in 2026?▼

Players with a similar profile to Filip Marchwinski in 2026 include Nico Paz (€65.0M), Martin Baturina (€30.0M), Antonio Vergara (€15.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Filip Marchwinski play and who plays similarly?▼

Filip Marchwinski plays as a Midfielder. Players with a comparable positional profile include Nico Paz (Argentina, €65.0M); Martin Baturina (Croatia, €30.0M); Antonio Vergara (Italy, €15.0M); Daniel Maldini (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.

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