RisingTransfers
AI DNA Similarity

Best Alternatives to Marc Pubill

Players most similar to Marc Pubill (Defender, €28.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Marc Pubill

  1. 1.Jon Aramburu87% DNA match·Real Sociedad€15.0M
  2. 2.Eric García86% DNA match·FC Barcelona€35.0M
  3. 3.Carlos Puga86% DNA match·Málaga€20.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 25%

A Ball-Playing CB....

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Playing Style Analysis

Ball-Playing CBBall-PlayingAerialSmall Sample

A Ball-Playing CB. Statistically, he stands out as commanding in the air (5.6 clearances/90), reads the game exceptionally (1.6 interceptions/90), meticulous in distribution (89% pass accuracy), wins the physical battle (76% duel success), heavily involved in possession (60 passes/90), blocks shots courageously (1.6 blocks/90), central to possession (76 touches/90), dominant in the air (4.3 aerials won/90, 70%) and active off the ball (2.3 press score/90), contributing to defensive transitions. Note: this profile is based on 776 minutes of playing time this season. The three most similar players to Marc Pubill by playing style are:

  • Jon Aramburu(87% match)A Active Full-Back. Statistically, he stands out as an aggressive ball-winner (3.6 tackles/90), reads the game exceptionally (1.6 interceptions/90), wins the physical battle (57% duel success), wins the ball cleanly (2.1 successful tackles/90), active off the ball (2.6 press score/90), contributing to defensive transitions and top 10% tackler in the league.
  • Eric García(86% match)A Ball-Playing CB. Statistically, he stands out as a capable chance creator (1.0 key passes/90), an aggressive ball-winner (2.7 tackles/90), reads the game exceptionally (1.6 interceptions/90), meticulous in distribution (91% pass accuracy), wins the physical battle (58% duel success), heavily involved in possession (71 passes/90), penetrates with forward passing (9.6 final-third passes/90), central to possession (88 touches/90) and active off the ball (2.5 press score/90), contributing to defensive transitions.
  • Carlos Puga(86% match)A Active Full-Back. Statistically, he stands out as naturally left-footed, a capable chance creator (1.2 key passes/90), an aggressive ball-winner (3.4 tackles/90), wins the ball cleanly (2.4 successful tackles/90), active off the ball (2.9 press score/90), contributing to defensive transitions and top 10% tackler in the league.

Transfer Intelligence

Jon Aramburu delivers 87% of the same playing style, at 46% lower cost (€15.0M vs €28.0M), with 3.33 tackles won per 90 at age 23.

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

M
Comparison Base
Marc Pubill
DefenderSpain€28.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
J
Jon Aramburu
Real Sociedad · La Liga
Venezuela23yContract 2030
Tkl/903.33
KP/900.35
Active Full-Back
Last 5: ↓ Dip
vs Pubill: €13M cheaper
87% match
€15.0M
#2
E
Eric García
FC Barcelona · La Liga
Spain25yContract 2026
Tkl/902.70
KP/901.01
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
vs Pubill: €7M more expensive · 3y older
86% match
€35.0M
#3
C
Carlos Puga
Málaga · La Liga
Spain25yContract 2029
Tkl/902.01
KP/901.12
Active Full-Back
Last 5: ↑ Hot
vs Pubill: €8M cheaper · 3y older
86% match
€20.0M
#4
J
Jon Martín
Real Sociedad · La Liga
Spain20yContract 2031
Tkl/901.13
KP/900.06
Reading DefenderAerial Defender
Last 5: → Stable
86% match
€20.0M
#5
P
Pau Navarro
Villarreal · La Liga
Spain21yContract 2030
Tkl/903.50
KP/900.26
Ball-Playing CBAerial
Last 5: → Stable
86% match
€10.0M
#6
D
David Affengruber
Elche · La Liga
Austria25yContract 2026
Tkl/902.10
KP/900.30
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€9.0M
#7
A
Arnau Martínez
Girona · La Liga
Spain23yContract 2027
Tkl/902.47
KP/900.62
Active Full-BackAerial
Last 5: → Stable
85% match
€10.0M
#8
M
Murillo
Nottingham Forest · Premier League
Brazil23yContract 2029
Tkl/901.48
KP/900.38
Ball-Playing CBAerial
Last 5: → Stable
85% match
€12.0M
#9
Á
Álvaro Núñez
Celta de Vigo · La Liga
Spain25yContract 2026
Tkl/901.28
KP/901.22
Active Full-Back
Last 5: → Stable
85% match
€6.0M
#10
K
Kike Salas
Sevilla · La Liga
Spain24yContract 2029
Tkl/902.08
KP/900.22
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€6.0M
#11
O
Omar El Hilali
Espanyol · La Liga
Morocco22yContract 2027
Tkl/902.50
KP/900.51
Active Full-Back
Last 5: → Stable
85% match
€15.0M
#12
V
Vitor Reis
Girona · La Liga
Brazil20yContract 2026
Tkl/901.34
KP/900.29
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
84% match
€30.0M

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 Marc Pubill.

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Frequently Asked Questions

Who are the best alternatives to Marc Pubill?
The top alternatives to Marc Pubill based on AI DNA playing style analysis include: Jon Aramburu, Eric García, Carlos Puga, Jon Martín, Pau Navarro. 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 Marc Pubill in 2026?
Players with a similar profile to Marc Pubill in 2026 include Jon Aramburu (€15.0M), Eric García (€35.0M), Carlos Puga (€20.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Marc Pubill play and who plays similarly?
Marc Pubill plays as a Defender. Players with a comparable positional profile include Jon Aramburu (Venezuela, €15.0M); Eric García (Spain, €35.0M); Carlos Puga (Spain, €20.0M); Jon Martín (Spain, €20.0M).
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.