RisingTransfers
AI DNA Similarity

Best Alternatives to Marko Pjaca

Players most similar to Marko Pjaca (Attacker, €1.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Marko Pjaca

  1. 1.Alassane Pléa85% DNA match·PSV€3.0M
  2. 2.Federico Bernardeschi85% DNA match·Bologna€3.5M
  3. 3.Esmir Bajraktarevic84% DNA match·PSV€5.0M

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

RT

Intelligence Verdict

InterceptionsTop 2%
???Bottom 25%

Pjaca is a high-volume creative hub masquerading as a conventional forward...

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Playing Style Analysis

Pjaca is a high-volume creative hub masquerading as a conventional forward, operating with a tactical intelligence that far outstrips his Tier C Eredivisie surroundings. While his output of 0.23 goals per 90 appears pedestrian, the underlying data reveals a metronomic playmaker who ranks in the top 10% for both passes attempted and successful dribbles. He doesn't just wait for service; he dictates the tempo, finding the final third with the precision of a deep-lying midfielder while maintaining an elite 83.3% pass accuracy. The three most similar players to Marko Pjaca by playing style are:

  • Alassane Pléa(85% match)A Complete Forward. Statistically, he stands out as a capable chance creator (1.2 key passes/90), a proven goalscorer (0.52 goals/90), a reliable supplier (0.23 assists/90), creates high-quality scoring opportunities (0.61 big chances/90) and top 20% scorer in the league.
  • Federico Bernardeschi(85% match)Bernardeschi remains the quintessential Italian "fantasista" trapped in an era of rigid systems, operating as a high-volume creative hub for a struggling Tier C side. While his goal output is merely pedestrian, his underlying metrics reveal a playmaker of elite continental caliber; he ranks in the top 5% of the league for key passes, progressive balls, and successful dribbles. This isn't just a winger hugging the touchline; his 39.7 passes per 90 and exceptional interception rate suggest a player who dictates the tempo and disrupts opposition buildup with top-tier defensive intensity.
  • Esmir Bajraktarevic(84% match)A Inside Forward. Statistically, he stands out as an elite creator (1.9 key passes/90), a constant goal threat (3.1 shots/90), a regular goalscorer (0.34 goals/90), a prolific assist provider (0.34 assists/90), meticulous in distribution (88% pass accuracy) and creates high-quality scoring opportunities (0.51 big chances/90).

Transfer Intelligence

Alassane Pléa delivers 85% of the same playing style, at a 100% premium over Marko Pjaca, with 0.52 goals per 90 at age 33. That's 257% of Marko Pjaca's output in goals per 90 — a credible like-for-like option.

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

M
Comparison Base
Marko Pjaca
AttackerCroatia€1.5M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
A
Alassane Pléa
PSV · Eredivisie
France33yContract 2028
G/900.52
A/900.23
Complete ForwardProlific
vs Pjaca: 2y older · +0.32 G/90
85% match
€3.0M
#2
F
Federico Bernardeschi
Bologna · Serie A
Italy32yContract 2027
G/900.58
A/900.00
Link-Up Forward
Last 5: → Stable
vs Pjaca: +0.38 G/90
85% match
€3.5M
#3
E
Esmir Bajraktarevic
PSV · Eredivisie
United States21yContract 2029
G/900.34
A/900.34
Inside Forward
Last 5: ↑ Hot
vs Pjaca: 10y younger · +0.14 G/90
84% match
€5.0M
#4
K
Kasper Dolberg
Ajax · Eredivisie
Denmark28yContract 2029
G/900.25
A/900.25
Last 5: ↓ Dip
83% match
€12.0M
#5
V
Virgil Misidjan
NEC Nijmegen · Eredivisie
Netherlands32y
G/900.17
A/900.34
Small Sample
83% match
€3.0M
#6
W
Weslley Patati
AZ · Eredivisie
Brazil22yContract 2030
G/900.13
A/900.13
Inside Forward
Last 5: ↓ Dip
83% match
€7.5M
#7
S
Sébastien Haller
FC Utrecht · Eredivisie
Ivory Coast31y
G/900.10
A/900.30
Last 5: ↓ Dip
83% match
€31.0M
#8
S
Sondre Ørjasæter
FC Twente · Eredivisie
Norway22y
G/900.09
A/900.22
Inside ForwardDribbler
Last 5: ↑ Hot
83% match
€5.0M
#9
A
Anis Hadj Moussa
Feyenoord · Eredivisie
Algeria24yContract 2030
G/900.33
A/900.22
Inside ForwardDribbler
Last 5: ↓ Dip
82% match
€20.0M
#10
A
Ahmed Kutucu
Galatasaray · Super Lig
Turkey26yContract 2028
G/900.49
A/900.37
Inside ForwardProlific
82% match
€4.0M
#11
D
Dzenan Pejcinovic
VfL Wolfsburg · Bundesliga
Germany21yContract 2029
G/900.63
A/900.00
Complete ForwardProlific
Last 5: ↓ Dip
83% match
€6.0M
#12
S
Szymon Wlodarczyk
Excelsior · Eredivisie
Poland23y
G/900.36
A/900.12
Target ManSmall Sample
Last 5: ↑ Hot
83% match
€3.0M

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 Marko Pjaca.

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Frequently Asked Questions

Who are the best alternatives to Marko Pjaca?
The top alternatives to Marko Pjaca based on AI DNA playing style analysis include: Alassane Pléa, Federico Bernardeschi, Esmir Bajraktarevic, Kasper Dolberg, Virgil Misidjan. 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 Marko Pjaca in 2026?
Players with a similar profile to Marko Pjaca in 2026 include Alassane Pléa (€3.0M), Federico Bernardeschi (€3.5M), Esmir Bajraktarevic (€5.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Marko Pjaca play and who plays similarly?
Marko Pjaca plays as a Attacker. Players with a comparable positional profile include Alassane Pléa (France, €3.0M); Federico Bernardeschi (Italy, €3.5M); Esmir Bajraktarevic (United States, €5.0M); Kasper Dolberg (Denmark, €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.