AI DNA Similarity
Best Alternatives to Lucas Paquetá
Players most similar to Lucas Paquetá (Midfielder, €35.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Lucas Paquetá
- 1.Bruno Fernandes85% DNA match·Manchester United€40.0M
- 2.Casemiro83% DNA match·Manchester United€8.0M
- 3.Enzo Fernández83% DNA match·Chelsea€90.0M
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
RT
Intelligence Verdict
Aerials WonTop 0%
“A Creator....”
See Full Verdict + Share Card →Playing Style Analysis
Creator
A Creator. Statistically, he stands out as naturally left-footed, a capable chance creator (1.4 key passes/90), a regular goalscorer (0.24 goals/90), active in the tackle (2.3 tackles/90), heavily involved in play (67 touches/90) and active off the ball (2.3 press score/90), contributing to defensive transitions. However, he loses possession under pressure (1.6 dispossessed/90). The three most similar players to Lucas Paquetá by playing style are:
- Bruno Fernandes(85% match) — A Creator. Statistically, he stands out as an elite creator (3.9 key passes/90), a regular goalscorer (0.25 goals/90), a prolific assist provider (0.59 assists/90), creates high-quality scoring opportunities (0.90 big chances/90), penetrates with forward passing (10.4 final-third passes/90), central to possession (78 touches/90), switches play with precision (6.9 long balls/90, 61% accuracy), active off the ball (2.5 press score/90), contributing to defensive transitions and top 10% creator in the league.
- Casemiro(83% match) — A Ball-Winner. Statistically, he stands out as a capable chance creator (1.2 key passes/90), a regular goalscorer (0.36 goals/90), an aggressive ball-winner (3.2 tackles/90), penetrates with forward passing (8.4 final-third passes/90), central to possession (74 touches/90), uses long balls frequently (5.5/90), active off the ball (2.7 press score/90), contributing to defensive transitions and top 10% tackler in the league.
- Enzo Fernández(83% match) — A Creator. Statistically, he stands out as an elite creator (2.0 key passes/90), a regular goalscorer (0.28 goals/90), meticulous in distribution (87% pass accuracy), penetrates with forward passing (8.5 final-third passes/90), central to possession (76 touches/90) and top 10% creator in the league.
Transfer Intelligence
Bruno Fernandes delivers 85% of the same playing style, at a 14% premium over Lucas Paquetá, with 3.89 key passes per 90 at age 31.
Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →
Similar Players — Ranked by DNA Similarity
#1
B
Bruno Fernandes
Manchester United · Premier League
Portugal31yContract 2027
KP/903.89
G/900.25
CreatorCreative
Last 5: → Stablevs Paquetá: 3y older
85% match
€40.0M
#2
C
Casemiro
Manchester United · Premier League
Brazil34yContract 2026
KP/901.22
G/900.36
Ball-WinnerDefensive
Last 5: → Stablevs Paquetá: €27M cheaper · 6y older
83% match
€8.0M
#3
E
Enzo Fernández
Chelsea · Premier League
Argentina25yContract 2032
KP/901.99
G/900.28
CreatorCreative
Last 5: ↑ Hotvs Paquetá: €55M more expensive · 3y younger
83% match
€90.0M
#4
F
Fábio Carvalho
Brentford · Premier League
Portugal23yContract 2029
KP/901.50
G/900.60
CreatorCreative
84% match
€12.0M
#5
B
Brenden Aaronson
Leeds United · Premier League
United States25yContract 2027
KP/901.24
G/900.15
Creator
Last 5: → Stable83% match
€15.0M
#6
B
Bernardo Silva
Manchester City · Premier League
Portugal31yContract 2026
KP/901.54
G/900.07
CreatorCreative
Last 5: ↑ Hot83% match
€27.0M
#7
R
Rodrigo Gomes
Wolverhampton Wanderers · Premier League
Portugal22yContract 2029
KP/900.34
G/900.34
Balanced MidfielderSmall Sample
Last 5: ↓ Dip83% match
€15.0M
#8
J
João Palhinha
Tottenham Hotspur · Premier League
Portugal30yContract 2026
KP/900.27
G/900.18
Ball-WinnerDefensive
Last 5: ↓ Dip83% match
€25.0M
#9
S
Sandro Tonali
Newcastle United · Premier League
Italy26yContract 2028
KP/901.12
G/900.00
Ball-Winner
Last 5: → Stable82% match
€80.0M
#10
M
Mikkel Damsgaard
Brentford · Premier League
Denmark25yContract 2030
KP/901.74
G/900.18
CreatorCreative
Last 5: → Stable82% match
€30.0M
#11
M
Mateus Fernandes
West Ham United · Premier League
Portugal21yContract 2030
KP/901.07
G/900.10
Ball-WinnerDefensive
Last 5: → Stable82% match
€32.0M
#12
F
Fred
Fenerbahçe · Super Lig
Brazil33yContract 2027
KP/902.28
G/900.13
CreatorCreative
Last 5: ↑ Hot82% match
€7.0M
⬡
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 Lucas Paquetá.
Ask AI about Lucas Paquetá →Frequently Asked Questions
Who are the best alternatives to Lucas Paquetá?▼
The top alternatives to Lucas Paquetá based on AI DNA playing style analysis include: Bruno Fernandes, Casemiro, Enzo Fernández, Fábio Carvalho, Brenden Aaronson. 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 Lucas Paquetá in 2026?▼
Players with a similar profile to Lucas Paquetá in 2026 include Bruno Fernandes (€40.0M), Casemiro (€8.0M), Enzo Fernández (€90.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Lucas Paquetá play and who plays similarly?▼
Lucas Paquetá plays as a Midfielder. Players with a comparable positional profile include Bruno Fernandes (Portugal, €40.0M); Casemiro (Brazil, €8.0M); Enzo Fernández (Argentina, €90.0M); Fábio Carvalho (Portugal, €12.0M).
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.