RisingTransfers
AI DNA Similarity

Best Alternatives to Tolgay Arslan

Players most similar to Tolgay Arslan (Midfielder, €3.2M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Tolgay Arslan

  1. 1.Danel Sinani83% DNA match·St. Pauli€3.0M
  2. 2.Luis Hasa82% DNA match·Napoli€3.0M
  3. 3.Enis Bardhi81% DNA match·Konyaspor€3.0M

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

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Intelligence Verdict

Chances MissedTop 0%
???Bottom 0%

Arslan is a technical anomaly in the J-League, operating as a high-volume ball-carrier with the...

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Playing Style Analysis

Chance CreatorGoal-Scoring Midfielder

Arslan is a technical anomaly in the J-League, operating as a high-volume ball-carrier with the predatory instincts of a second striker. While his 40.3 passes per 90 suggest a standard central presence, his output in the final third is elite; he sits in the top 5% of the league for both shots (2.76) and goals (0.40), effectively acting as a late-arriving goal threat rather than a traditional tempo-setter. The data reveals a fascinating contradiction: he is a dribbling specialist who ranks in the top 5% for take-ons, yet his defensive awareness is alarmingly poor, ranking in the bottom 10% for reading the game. The three most similar players to Tolgay Arslan by playing style are:

  • Danel Sinani(83% match)A Creator. Statistically, he stands out as naturally left-footed, an elite creator (2.0 key passes/90), a regular goalscorer (0.25 goals/90), heavily involved in play (61 touches/90), active off the ball (2.1 press score/90), contributing to defensive transitions and top 10% creator in the league. However, he loses possession under pressure (1.6 dispossessed/90).
  • Luis Hasa(82% match)A Box-to-Box. Statistically, he stands out as a capable chance creator (1.5 key passes/90), a regular goalscorer (0.20 goals/90), a reliable supplier (0.16 assists/90), meticulous in distribution (89% pass accuracy), heavily involved in play (61 touches/90), draws fouls effectively (2.7/90) and active off the ball (2.8 press score/90), contributing to defensive transitions.
  • Enis Bardhi(81% match)Bardhi is a dead-ball specialist masquerading as a central cog, a player who operates with the surgical precision of a playmaker but carries the shot volume of a secondary striker. While his 33.7 passes per match suggest a modest involvement in build-up, his output is pure efficiency; he ranks in the top 5% of Super Lig midfielders for key passes and the top 10% for goals. This discrepancy reveals the counterintuitive reality of his game: Bardhi isn't a high-volume engine, but a high-leverage executioner who waits for the decisive moment to strike or slide a ball into the final third.

Transfer Intelligence

Danel Sinani delivers 83% of the same playing style, with 2.13 key passes per 90 at age 29.

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

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Comparison Base
Tolgay Arslan
MidfielderGermany€3.2M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
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Danel Sinani
St. Pauli · Bundesliga
Luxembourg29y
KP/902.13
G/900.43
CreatorCreative
vs Arslan: 6y younger
83% match
€3.0M
#2
L
Luis Hasa
Napoli · Serie A
Italy22yContract 2026
KP/900.90
G/900.36
Box-to-Box
Last 5: ↑ Hot
vs Arslan: 13y younger
82% match
€3.0M
#3
E
Enis Bardhi
Konyaspor · Super Lig
Macedonia30yContract 2027
KP/902.44
G/900.22
CreatorCreative
Last 5: → Stable
vs Arslan: 5y younger
81% match
€3.0M
#4
J
Joel Chima Fujita
St. Pauli · Bundesliga
Japan24y
KP/900.52
G/900.00
Box-to-BoxDefensive
Last 5: ↓ Dip
82% match
€8.0M
#5
D
Diogo Gonçalves
Konyaspor · Super Lig
Portugal29yContract 2026
G/900.81
A/900.00
Left WingCreator
Last 5: → Stable
80% match
€3.0M
#6
C
Can Uzun
Eintracht Frankfurt · Bundesliga
Turkey20yContract 2029
KP/901.58
G/900.88
CreatorCreative
81% match
€45.0M
#7
K
Kodai Sano
Japan · Eredivisie
Japan22yContract 2028
KP/901.49
G/900.09
Box-to-Box
Last 5: → Stable
80% match
€6.0M
#8
S
Salih Özcan
Borussia Dortmund · Bundesliga
Turkey28yContract 2026
KP/901.26
G/900.00
MetronomeSmall Sample
81% match
€3.5M
#9
A
Ao Tanaka
Leeds United · Premier League
Japan27yContract 2028
KP/901.52
G/900.15
CreatorCreative
Last 5: → Stable
81% match
€10.0M
#10
S
Sam Larsson
Fatih Karagümrük · Super Lig
Sweden33y
KP/902.11
G/900.06
CreatorCreative
Last 5: → Stable
81% match
€5.0M
#11
L
Leon Avdullahu
TSG Hoffenheim · Bundesliga
Switzerland22yContract 2029
KP/901.02
G/900.00
MetronomeSmall Sample
Last 5: ↑ Hot
80% match
€17.0M
#12
C
Charles De Ketelaere
Atalanta · Serie A
Belgium25yContract 2028
KP/902.53
G/900.06
CreatorCreative
Last 5: → Stable
80% match
€35.0M

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 Tolgay Arslan.

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Frequently Asked Questions

Who are the best alternatives to Tolgay Arslan?
The top alternatives to Tolgay Arslan based on AI DNA playing style analysis include: Danel Sinani, Luis Hasa, Enis Bardhi, Joel Chima Fujita, Diogo Gonçalves. 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 Tolgay Arslan in 2026?
Players with a similar profile to Tolgay Arslan in 2026 include Danel Sinani (€3.0M), Luis Hasa (€3.0M), Enis Bardhi (€3.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Tolgay Arslan play and who plays similarly?
Tolgay Arslan plays as a Midfielder. Players with a comparable positional profile include Danel Sinani (Luxembourg, €3.0M); Luis Hasa (Italy, €3.0M); Enis Bardhi (Macedonia, €3.0M); Joel Chima Fujita (Japan, €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.