RisingTransfers
AI DNA Similarity

Best Alternatives to Suat Serdar

Players most similar to Suat Serdar (Midfielder, €4.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Suat Serdar

  1. 1.Lazar Samardžić86% DNA match·Atalanta€15.0M
  2. 2.Medon Berisha85% DNA match·Lecce€6.0M
  3. 3.Leon Avdullahu84% DNA match·TSG Hoffenheim€17.0M

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

RT

Intelligence Verdict

Tackles WonTop 11%
???Bottom 0%

A Box-to-Box....

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Playing Style Analysis

Box-to-BoxSmall Sample

A Box-to-Box. Statistically, he stands out as active in the tackle (2.3 tackles/90), penetrates with forward passing (8.9 final-third passes/90), heavily involved in play (50 touches/90) and active off the ball (2.7 press score/90), contributing to defensive transitions. Note: this profile is based on 543 minutes of playing time this season. The three most similar players to Suat Serdar by playing style are:

  • Lazar Samardžić(86% match)A Creator. Statistically, he stands out as naturally left-footed, an elite creator (2.5 key passes/90), a reliable supplier (0.23 assists/90), central to possession (71 touches/90), switches play with precision (6.1 long balls/90, 80% accuracy) and top 10% creator in the league. However, he loses possession under pressure (2.0 dispossessed/90).
  • Medon Berisha(85% match)A Creator. Statistically, he stands out as an elite creator (2.0 key passes/90), a regular goalscorer (0.27 goals/90), a prolific assist provider (0.41 assists/90), active in the tackle (2.5 tackles/90), wins the physical battle (56% duel success), creates high-quality scoring opportunities (0.68 big chances/90), heavily involved in play (58 touches/90), uses long balls frequently (6.4/90), active off the ball (2.5 press score/90), contributing to defensive transitions and top 20% creator in the league. Note: this profile is based on 660 minutes of playing time this season.
  • Leon Avdullahu(84% match)A Metronome. Statistically, he stands out as active in the tackle (2.0 tackles/90), reads the game exceptionally (1.5 interceptions/90), meticulous in distribution (88% pass accuracy), heavily involved in possession (66 passes/90), wins the ball cleanly (1.9 successful tackles/90), central to possession (77 touches/90), uses long balls frequently (5.4/90) and a high-intensity presser (press score 3.5/90), constantly disrupting opposition build-up. Note: this profile is based on 529 minutes of playing time this season.

Transfer Intelligence

Lazar Samardžić delivers 86% of the same playing style, at a 233% premium over Suat Serdar, with 2.64 key passes per 90 at age 24.

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

S
Comparison Base
Suat Serdar
MidfielderGermany€4.5M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
L
Lazar Samardžić
Atalanta · Serie A
Germany24yContract 2029
KP/902.64
G/900.22
CreatorCreative
Last 5: → Stable
vs Serdar: €11M more expensive · 5y younger
86% match
€15.0M
#2
M
Medon Berisha
Lecce · Serie A
Albania22yContract 2028
KP/902.04
G/900.27
CreatorCreative
vs Serdar: 7y younger
85% match
€6.0M
#3
L
Leon Avdullahu
TSG Hoffenheim · Bundesliga
Switzerland22yContract 2029
KP/901.02
G/900.00
MetronomeSmall Sample
Last 5: ↑ Hot
vs Serdar: €13M more expensive · 7y younger
84% match
€17.0M
#4
V
Vincenzo Grifo
SC Freiburg · Bundesliga
Italy33yContract 2027
KP/901.69
G/900.50
CreatorCreative
Last 5: → Stable
84% match
€5.0M
#5
F
Filip Kostić
Juventus · Serie A
Serbia33yContract 2026
KP/901.95
G/900.59
CreatorCreative
84% match
€3.5M
#6
F
Florent Hadergjonaj
Alanyaspor · Super Lig
Kosovo31yContract 2027
KP/901.78
G/900.25
CreatorCreative
Last 5: → Stable
84% match
€5.0M
#7
D
Danel Sinani
St. Pauli · Bundesliga
Luxembourg29y
KP/902.13
G/900.43
CreatorCreative
84% match
€3.0M
#8
J
Jurgen Ekkelenkamp
Udinese · Serie A
Netherlands26yContract 2029
KP/901.41
G/900.19
Creator
Last 5: → Stable
84% match
€7.0M
#9
R
Ramiz Zerrouki
FC Twente · Eredivisie
Algeria27y
KP/901.56
G/900.11
MetronomeCreative
Last 5: ↑ Hot
83% match
€7.2M
#10
B
Bryan Cristante
Roma · Serie A
Italy31yContract 2027
KP/900.88
G/900.05
Metronome
Last 5: ↓ Dip
83% match
€7.0M
#11
R
Reda Belahyane
Lazio · Serie A
France21yContract 2029
KP/900.35
G/900.00
Ball-WinnerSmall Sample
84% match
€8.5M
#12
N
Nikola Vlašić
Torino · Serie A
Croatia28yContract 2027
KP/901.59
G/900.20
CreatorCreative
Last 5: → Stable
83% match
€9.0M

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 Suat Serdar.

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Frequently Asked Questions

Who are the best alternatives to Suat Serdar?
The top alternatives to Suat Serdar based on AI DNA playing style analysis include: Lazar Samardžić, Medon Berisha, Leon Avdullahu, Vincenzo Grifo, Filip Kostić. 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 Suat Serdar in 2026?
Players with a similar profile to Suat Serdar in 2026 include Lazar Samardžić (€15.0M), Medon Berisha (€6.0M), Leon Avdullahu (€17.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Suat Serdar play and who plays similarly?
Suat Serdar plays as a Midfielder. Players with a comparable positional profile include Lazar Samardžić (Germany, €15.0M); Medon Berisha (Albania, €6.0M); Leon Avdullahu (Switzerland, €17.0M); Vincenzo Grifo (Italy, €5.0M).
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.