RisingTransfers
AI DNA Similarity

Best Alternatives to Bernardo

Players most similar to Bernardo (Defender, €5.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Bernardo

  1. 1.Felix Agu87% DNA match·Werder Bremen€6.0M
  2. 2.Rasmus Kristensen86% DNA match·Eintracht Frankfurt€14.0M
  3. 3.Alexander Prass85% DNA match·TSG Hoffenheim€7.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 19%

A Active Full-Back....

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Playing Style Analysis

Active Full-BackBall-PlayingAerialSmall Sample

A Active Full-Back. Statistically, he stands out as naturally left-footed, active in the tackle (2.4 tackles/90), commanding in the air (6.3 clearances/90), reads the game exceptionally (1.5 interceptions/90), wins the physical battle (61% duel success), penetrates with forward passing (10.1 final-third passes/90), central to possession (83 touches/90), dominant in the air (3.2 aerials won/90, 64%), uses long balls frequently (7.0/90), active off the ball (2.9 press score/90), contributing to defensive transitions and top 10% tackler in the league. Note: this profile is based on 526 minutes of playing time this season. The three most similar players to Bernardo by playing style are:

  • Felix Agu(87% match)A Active Full-Back. Statistically, he stands out as active in the tackle (2.4 tackles/90), wins the physical battle (69% duel success) and active off the ball (2.5 press score/90), contributing to defensive transitions. Note: this profile is based on 681 minutes of playing time this season.
  • Rasmus Kristensen(86% match)A Active Full-Back. Statistically, he stands out as active in the tackle (2.5 tackles/90), commanding in the air (4.4 clearances/90), reads the game exceptionally (1.5 interceptions/90), wins the physical battle (55% duel success), penetrates with forward passing (9.0 final-third passes/90), central to possession (80 touches/90), uses long balls frequently (5.7/90), a high-intensity presser (press score 3.3/90), constantly disrupting opposition build-up and top 10% tackler in the league. Note: this profile is based on 761 minutes of playing time this season.
  • Alexander Prass(85% match)A Physical Stopper. Statistically, he stands out as naturally left-footed, a capable chance creator (1.2 key passes/90), an aggressive ball-winner (2.6 tackles/90), active off the ball (2.7 press score/90), contributing to defensive transitions and top 10% tackler in the league.

Transfer Intelligence

Felix Agu delivers 87% of the same playing style, at a 20% premium over Bernardo, with 2.49 tackles won per 90 at age 26.

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

B
Comparison Base
Bernardo
DefenderBrazil€5.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
F
Felix Agu
Werder Bremen · Bundesliga
Germany26yContract 2027
Tkl/902.49
KP/901.04
Active Full-BackSmall Sample
Last 5: → Stable
vs Bernardo: 4y younger
87% match
€6.0M
#2
R
Rasmus Kristensen
Eintracht Frankfurt · Bundesliga
Denmark28yContract 2029
Tkl/902.39
KP/900.80
Active Full-BackAerial
vs Bernardo: €9M more expensive · 2y younger
86% match
€14.0M
#3
A
Alexander Prass
TSG Hoffenheim · Bundesliga
Austria24yContract 2028
Tkl/902.22
KP/901.23
Physical Stopper
Last 5: ↓ Dip
vs Bernardo: 6y younger
85% match
€7.0M
#4
S
Stefan Posch
FSV Mainz 05 · Bundesliga
Austria28yContract 2026
Tkl/901.50
KP/900.25
Physical StopperAerial
Last 5: → Stable
85% match
€5.5M
#5
L
Loïc Badé
Bayer 04 Leverkusen · Bundesliga
France26yContract 2030
Tkl/902.37
KP/900.59
Ball-Playing CBBall-Playing
85% match
€25.0M
#6
J
Jordy Makengo
SC Freiburg · Bundesliga
France24y
Tkl/901.50
KP/900.30
Aerial
Last 5: → Stable
85% match
€6.0M
#7
J
Jair Cunha 
Nottingham Forest · Premier League
Brazil21yContract 2030
Tkl/901.61
KP/900.00
Ball-Playing CBBall-Playing
Last 5: → Stable
84% match
€12.0M
#8
L
Lorenz Assignon
VfB Stuttgart · Bundesliga
France25yContract 2029
Tkl/900.88
KP/901.50
Active Full-BackSmall Sample
Last 5: ↓ Dip
85% match
€10.0M
#9
M
Morato
Nottingham Forest · Premier League
Brazil24yContract 2029
Tkl/902.08
KP/900.15
Ball-Playing CBAerial
Last 5: ↓ Dip
85% match
€14.0M
#10
J
Jordan Torunarigha
Hamburger SV · Bundesliga
Nigeria28yContract 2028
Tkl/901.86
KP/900.23
Ball-Playing CBAerial
85% match
€4.0M
#11
F
Finn Jeltsch
VfB Stuttgart · Bundesliga
Germany19yContract 2030
Tkl/900.74
KP/900.62
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€25.0M
#12
C
Cédric Zesiger
FC Augsburg · Bundesliga
Switzerland27yContract 2029
Tkl/901.10
KP/900.32
Ball-Playing CBAerial
84% match
€5.0M

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 Bernardo.

Ask AI about Bernardo

Frequently Asked Questions

Who are the best alternatives to Bernardo?
The top alternatives to Bernardo based on AI DNA playing style analysis include: Felix Agu, Rasmus Kristensen, Alexander Prass, Stefan Posch, Loïc Badé. 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 Bernardo in 2026?
Players with a similar profile to Bernardo in 2026 include Felix Agu (€6.0M), Rasmus Kristensen (€14.0M), Alexander Prass (€7.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Bernardo play and who plays similarly?
Bernardo plays as a Defender. Players with a comparable positional profile include Felix Agu (Germany, €6.0M); Rasmus Kristensen (Denmark, €14.0M); Alexander Prass (Austria, €7.0M); Stefan Posch (Austria, €5.5M).
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.