RisingTransfers
AI DNA Similarity

Best Alternatives to Christopher Operi

Players most similar to Christopher Operi (Defender, €2.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Christopher Operi

  1. 1.Mustafa Eskihellaç86% DNA match·Trabzonspor€3.0M
  2. 2.Mert Müldür86% DNA match·Fenerbahçe€5.0M
  3. 3.Levent Mercan86% DNA match·Fenerbahçe€5.0M

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

RT

Intelligence Verdict

AssistsTop 7%
???Bottom 0%

Operi is the Super Lig’s quintessential "hidden engine," a Tier C defender operating with the...

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Playing Style Analysis

Active Full-Back

Operi is the Super Lig’s quintessential "hidden engine," a Tier C defender operating with the creative output of a high-level playmaker. While most full-backs in Turkey are content with recycling possession, Operi lives in the final third, ranking in the top 20% for progressive passes (6.01/90) and an elite top 10% for assists. His 66.7% duel win rate suggests a defensive steel that belies his offensive license, yet the counterintuitive reality is that his 1.07 interceptions per minute—only league average—point to a player who thrives on physical confrontation rather than elite spatial anticipation. The three most similar players to Christopher Operi by playing style are:

  • Mustafa Eskihellaç(86% match)A Active Full-Back. Statistically, he stands out as a reliable supplier (0.16 assists/90), meticulous in distribution (87% pass accuracy), wins the physical battle (59% duel success), draws fouls effectively (2.3/90) and active off the ball (2.7 press score/90), contributing to defensive transitions.
  • Mert Müldür(86% match)Müldür occupies a fascinating tactical grey zone—a defender who consistently threatens the opposition goal more than he's troubled by his own. His tackles won rate sits in the top 5% of Super Lig defenders, meaning he's not just positioning well; he's actively winning the ball back at an elite frequency for this league. Pair that with top-20% key passes and shots per 90, and you have someone functioning more like an advanced wing-back than a conventional fullback.
  • Levent Mercan(86% match)A Active Full-Back. Statistically, he stands out as naturally left-footed, an elite creator (1.6 key passes/90), a reliable supplier (0.24 assists/90), active in the tackle (2.0 tackles/90), meticulous in distribution (85% pass accuracy), wins the physical battle (58% duel success), central to possession (77 touches/90) and active off the ball (2.6 press score/90), contributing to defensive transitions.

Transfer Intelligence

Mustafa Eskihellaç delivers 86% of the same playing style, at a 20% premium over Christopher Operi, with 1.66 tackles won per 90 at age 29.

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

C
Comparison Base
Christopher Operi
DefenderFrance€2.5M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
M
Mustafa Eskihellaç
Trabzonspor · Super Lig
Turkey29yContract 2027
Tkl/901.66
KP/900.98
Active Full-Back
Last 5: → Stable
86% match
€3.0M
#2
M
Mert Müldür
Fenerbahçe · Super Lig
Turkey27yContract 2027
Tkl/902.98
KP/901.18
Physical StopperSmall Sample
Last 5: ↓ Dip
vs Operi: 2y younger
86% match
€5.0M
#3
L
Levent Mercan
Fenerbahçe · Super Lig
Germany25yContract 2028
Tkl/901.98
KP/901.58
Active Full-Back
Last 5: → Stable
vs Operi: 4y younger
86% match
€5.0M
#4
R
Roland Sallai
Galatasaray · Super Lig
Hungary28yContract 2028
Tkl/901.79
KP/900.88
Active Full-Back
Last 5: → Stable
85% match
€12.0M
#5
Ü
Ümit Akdağ
Alanyaspor · Super Lig
Romania22yContract 2028
Tkl/901.24
KP/900.62
Ball-Playing CBAerial
Last 5: ↑ Hot
85% match
€4.0M
#6
E
Eren Elmalı
Galatasaray · Super Lig
Turkey25yContract 2028
Tkl/901.95
KP/901.51
Active Full-Back
Last 5: ↑ Hot
85% match
€5.0M
#7
J
Jayden Oosterwolde
Fenerbahçe · Super Lig
Netherlands25yContract 2028
Tkl/901.25
KP/900.28
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€16.0M
#8
L
Louis Oppie
St. Pauli · Bundesliga
Germany23y
Tkl/900.99
KP/900.17
Small Sample
84% match
€3.0M
#9
K
Kojo Peprah Oppong
Nice · Ligue 1
Ghana21yContract 2029
Tkl/902.21
KP/900.23
Ball-Playing CBAerial
Last 5: → Stable
84% match
€4.0M
#10
M
Matthieu Udol
Lens · Ligue 1
France30yContract 2028
Tkl/901.38
KP/901.63
Active Full-BackSmall Sample
Last 5: ↑ Hot
83% match
€5.0M
#11
R
Ricardo Esgaio
Fatih Karagümrük · Super Lig
Portugal32y
Tkl/902.71
KP/900.52
Last 5: ↑ Hot
84% match
€5.5M
#12
F
Ferdi Kadıoğlu
Brighton & Hove Albion · Premier League
Turkey26yContract 2028
Tkl/901.71
KP/900.64
Last 5: ↓ Dip
83% match
€28.0M

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 Christopher Operi.

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Frequently Asked Questions

Who are the best alternatives to Christopher Operi?
The top alternatives to Christopher Operi based on AI DNA playing style analysis include: Mustafa Eskihellaç, Mert Müldür, Levent Mercan, Roland Sallai, Ümit Akdağ. 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 Christopher Operi in 2026?
Players with a similar profile to Christopher Operi in 2026 include Mustafa Eskihellaç (€3.0M), Mert Müldür (€5.0M), Levent Mercan (€5.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Christopher Operi play and who plays similarly?
Christopher Operi plays as a Defender. Players with a comparable positional profile include Mustafa Eskihellaç (Turkey, €3.0M); Mert Müldür (Turkey, €5.0M); Levent Mercan (Germany, €5.0M); Roland Sallai (Hungary, €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.