RisingTransfers
AI DNA Similarity

Best Alternatives to Emre Can

Players most similar to Emre Can (Defender, €4.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Emre Can

  1. 1.Matthias Ginter85% DNA match·SC Freiburg€6.0M
  2. 2.Ramy Bensebaini86% DNA match·Borussia Dortmund€7.0M
  3. 3.Robin Koch85% DNA match·Eintracht Frankfurt€15.0M

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

RT

Intelligence Verdict

GoalsTop 0%
???Bottom 0%

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 a proven goalscorer (0.47 goals/90), commanding in the air (5.2 clearances/90), reads the game exceptionally (2.0 interceptions/90), meticulous in distribution (86% pass accuracy), heavily involved in possession (69 passes/90), penetrates with forward passing (10.1 final-third passes/90), central to possession (87 touches/90), uses long balls frequently (7.5/90) and a high-intensity presser (press score 3.2/90), constantly disrupting opposition build-up. Note: this profile is based on 577 minutes of playing time this season. The three most similar players to Emre Can by playing style are:

  • Matthias Ginter(85% match)A Ball-Playing CB. Statistically, he stands out as commanding in the air (7.2 clearances/90), meticulous in distribution (86% pass accuracy), strong in aerial duels (3.3 aerials won/90) and uses long balls frequently (6.8/90). Note: this profile is based on 540 minutes of playing time this season.
  • Ramy Bensebaini(86% match)A Ball-Playing CB. Statistically, he stands out as naturally left-footed, a regular goalscorer (0.30 goals/90), a reliable supplier (0.15 assists/90), commanding in the air (5.0 clearances/90), meticulous in distribution (88% pass accuracy), wins the physical battle (58% duel success), heavily involved in possession (71 passes/90), penetrates with forward passing (12.6 final-third passes/90), central to possession (89 touches/90), uses long balls frequently (6.4/90) and active off the ball (2.6 press score/90), contributing to defensive transitions.
  • Robin Koch(85% match)A Ball-Playing CB. Statistically, he stands out as a regular goalscorer (0.30 goals/90), commanding in the air (8.0 clearances/90), reads the game exceptionally (1.5 interceptions/90), meticulous in distribution (87% pass accuracy), wins the physical battle (63% duel success), dominant in the air (3.5 aerials won/90, 64%) and uses long balls frequently (7.1/90).

Transfer Intelligence

Matthias Ginter delivers 85% of the same playing style, at a 50% premium over Emre Can, with 1.14 tackles won per 90 at age 32.

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

E
Comparison Base
Emre Can
DefenderGermany€4.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
M
Matthias Ginter
SC Freiburg · Bundesliga
Germany32yContract 2027
Tkl/901.14
KP/900.43
Ball-Playing CBAerial
Last 5: ↓ Dip
85% match
€6.0M
#2
R
Ramy Bensebaini
Borussia Dortmund · Bundesliga
Algeria31yContract 2027
Tkl/903.14
KP/900.12
Ball-Playing CBBall-Playing
86% match
€7.0M
#3
R
Robin Koch
Eintracht Frankfurt · Bundesliga
Germany29yContract 2030
Tkl/901.50
KP/900.08
Ball-Playing CBAerial
vs Can: €11M more expensive · 3y younger
85% match
€15.0M
#4
J
Jaydee Canvot
Crystal Palace · Premier League
France19yContract 2029
Tkl/902.73
KP/900.23
Ball-Playing CBAerial
Last 5: ↓ Dip
85% match
€20.0M
#5
N
Nico Schlotterbeck
Borussia Dortmund · Bundesliga
Germany26yContract 2027
Tkl/902.00
KP/900.89
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€55.0M
#6
M
Maximilian Mittelstädt
VfB Stuttgart · Bundesliga
Germany29yContract 2028
Tkl/902.76
KP/901.91
Active Full-BackBall-Playing
Last 5: → Stable
85% match
€18.0M
#7
E
Eric Smith
St. Pauli · Bundesliga
Sweden29yContract 2025
Tkl/901.03
KP/900.92
Ball-Playing CBBall-Playing
85% match
€5.0M
#8
J
Josip Stanisic
FC Bayern München · Bundesliga
Croatia26yContract 2029
Tkl/902.28
KP/901.94
Active Full-BackBall-Playing
Last 5: → Stable
85% match
€35.0M
#9
L
Lukas Ullrich
Borussia Mönchengladbach · Bundesliga
Germany22yContract 2027
Tkl/901.33
KP/900.53
Active Full-Back
84% match
€7.0M
#10
J
Jonathan Tah
FC Bayern München · Bundesliga
Germany30yContract 2029
Tkl/901.28
KP/900.24
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
85% match
€30.0M
#11
K
Konrad Laimer
FC Bayern München · Bundesliga
Austria28yContract 2027
Tkl/902.63
KP/900.84
Active Full-BackBall-Playing
Last 5: → Stable
85% match
€32.0M
#12
D
David Raum
RB Leipzig · Bundesliga
Germany28yContract 2027
Tkl/901.81
KP/902.57
Active Full-BackBall-Playing
Last 5: → Stable
84% match
€22.0M

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 Emre Can.

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Frequently Asked Questions

Who are the best alternatives to Emre Can?
The top alternatives to Emre Can based on AI DNA playing style analysis include: Matthias Ginter, Ramy Bensebaini, Robin Koch, Jaydee Canvot, Nico Schlotterbeck. 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 Emre Can in 2026?
Players with a similar profile to Emre Can in 2026 include Matthias Ginter (€6.0M), Ramy Bensebaini (€7.0M), Robin Koch (€15.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Emre Can play and who plays similarly?
Emre Can plays as a Defender. Players with a comparable positional profile include Matthias Ginter (Germany, €6.0M); Ramy Bensebaini (Algeria, €7.0M); Robin Koch (Germany, €15.0M); Jaydee Canvot (France, €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.