RisingTransfers
AI DNA Similarity

Best Alternatives to Mirnes Pepić

Players most similar to Mirnes Pepić (Midfielder, €25.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Mirnes Pepić

  1. 1.Aleksandar Pavlovic84% DNA match·FC Bayern München€75.0M
  2. 2.Oscar Højlund83% DNA match·Eintracht Frankfurt€7.5M
  3. 3.Ezequiel Fernández83% DNA match·Bayer 04 Leverkusen€25.0M

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

RT

Intelligence Verdict

???Bottom 0%

Pepić arrives in the Bundesliga as something of an enigma—a midfielder accumulating minutes...

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Playing Style Analysis

Small Sample

Pepić arrives in the Bundesliga as something of an enigma—a midfielder accumulating minutes cautiously at an A-tier club, his profile still forming beneath the surface of a partial data window. With only 339 minutes logged, drawing firm conclusions feels premature, yet the early signals demand honest scrutiny. His pass accuracy sits in the bottom 10% of Bundesliga midfielders, a damning figure at first glance—though in limited appearances, often as a substitute inheriting chaotic game states, inflated error rates are almost structurally inevitable. The three most similar players to Mirnes Pepić by playing style are:

  • Aleksandar Pavlovic(84% match)A Metronome. Statistically, he stands out as comfortable with both feet, a capable chance creator (1.4 key passes/90), active in the tackle (1.8 tackles/90), meticulous in distribution (95% pass accuracy), heavily involved in possession (102 passes/90), penetrates with forward passing (12.1 final-third passes/90), central to possession (115 touches/90), switches play with precision (5.8 long balls/90, 80% accuracy) and active off the ball (2.5 press score/90), contributing to defensive transitions.
  • Oscar Højlund(83% match)A Balanced Midfielder. Statistically, he stands out as an aggressive ball-winner (2.6 tackles/90), meticulous in distribution (89% pass accuracy) and heavily involved in play (60 touches/90).
  • Ezequiel Fernández(83% match)A Ball-Winner. Statistically, he stands out as naturally left-footed, an aggressive ball-winner (2.9 tackles/90), meticulous in distribution (90% pass accuracy), wins the physical battle (56% duel success), heavily involved in possession (75 passes/90), central to possession (93 touches/90), draws fouls effectively (2.2/90), a high-intensity presser (press score 3.3/90), constantly disrupting opposition build-up and top 10% tackler in the league.

Transfer Intelligence

Aleksandar Pavlovic delivers 84% of the same playing style, at a 200% premium over Mirnes Pepić, with 1.30 key passes per 90 at age 22.

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

M
Comparison Base
Mirnes Pepić
MidfielderMontenegro€25.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
A
Aleksandar Pavlovic
FC Bayern München · Bundesliga
Germany22yContract 2029
KP/901.30
G/900.08
Metronome
Last 5: → Stable
vs Pepić: €50M more expensive · 8y younger
84% match
€75.0M
#2
O
Oscar Højlund
Eintracht Frankfurt · Bundesliga
Denmark21yContract 2029
KP/900.32
G/900.00
Balanced MidfielderDefensive
vs Pepić: €18M cheaper · 9y younger
83% match
€7.5M
#3
E
Ezequiel Fernández
Bayer 04 Leverkusen · Bundesliga
Argentina23yContract 2030
KP/901.03
G/900.00
Ball-WinnerDefensive
Last 5: ↓ Dip
vs Pepić: 7y younger
83% match
€25.0M
#4
F
Fabian Rieder
FC Augsburg · Bundesliga
Switzerland24yContract 2030
KP/901.14
G/900.13
CreatorSmall Sample
Last 5: → Stable
83% match
€8.0M
#5
S
Sandi Lovrić
Hellas Verona · Serie A
Slovenia28yContract 2027
KP/900.94
G/900.00
Creator
82% match
€6.0M
#6
T
Tom Bischof
FC Bayern München · Bundesliga
Germany20yContract 2029
KP/901.01
G/900.00
CreatorCreative
Last 5: ↑ Hot
83% match
€40.0M
#7
E
Elvis Rexhbecaj
FC Augsburg · Bundesliga
Kosovo28yContract 2026
KP/901.02
G/900.00
Balanced Midfielder
83% match
€3.5M
#8
F
Felix Nmecha
Borussia Dortmund · Bundesliga
Germany25yContract 2028
KP/900.81
G/900.12
Box-to-BoxSmall Sample
Last 5: ↓ Dip
82% match
€45.0M
#9
A
Albert Sambi Lokonga
Hamburger SV · Bundesliga
Belgium26yContract 2028
KP/901.32
G/900.50
Box-to-Box
82% match
€8.0M
#10
J
Jens Stage
Werder Bremen · Bundesliga
Denmark29yContract 2026
KP/900.93
G/900.25
Box-to-Box
82% match
€14.0M
#11
J
Jan Schöppner
Heidenheim · Bundesliga
Germany26yContract 2028
KP/900.63
G/900.27
Balanced MidfielderSmall Sample
82% match
€4.0M
#12
D
Dominik Szoboszlai
Liverpool · Premier League
Hungary25yContract 2028
KP/902.00
G/900.18
CreatorCreative
Last 5: → Stable
81% match
€100.0M

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 Mirnes Pepić.

Ask AI about Mirnes Pepić

Frequently Asked Questions

Who are the best alternatives to Mirnes Pepić?
The top alternatives to Mirnes Pepić based on AI DNA playing style analysis include: Aleksandar Pavlovic, Oscar Højlund, Ezequiel Fernández, Fabian Rieder, Sandi Lovrić. 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 Mirnes Pepić in 2026?
Players with a similar profile to Mirnes Pepić in 2026 include Aleksandar Pavlovic (€75.0M), Oscar Højlund (€7.5M), Ezequiel Fernández (€25.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mirnes Pepić play and who plays similarly?
Mirnes Pepić plays as a Midfielder. Players with a comparable positional profile include Aleksandar Pavlovic (Germany, €75.0M); Oscar Højlund (Denmark, €7.5M); Ezequiel Fernández (Argentina, €25.0M); Fabian Rieder (Switzerland, €8.0M).
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.