RisingTransfers
AI DNA Similarity

Best Alternatives to Ivan Perišić

Players most similar to Ivan Perišić (Attacker, €1.3M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Ivan Perišić

  1. 1.David Neres 83% DNA match·Napoli€28.0M
  2. 2.Ayoze Pérez82% DNA match·Villarreal€6.0M
  3. 3.Milot Rashica81% DNA match·Beşiktaş€4.0M

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

RT

Intelligence Verdict

Big ChancesTop 3%
???Bottom 13%

Perišić remains the ultimate tactical chameleon, a high-functioning veteran who has successfully...

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Playing Style Analysis

Complete Forward

Perišić remains the ultimate tactical chameleon, a high-functioning veteran who has successfully transitioned from a vertical speedster into a cerebral, high-volume playmaker in the Eredivisie. While the pace of his Inter Milan days has naturally slowed, his efficiency has spiked; sitting in the top 5% of the league for both assists and passes per 90, he is effectively running the game from an advanced station rather than just finishing it. The counterintuitive insight lies in his defensive metrics: for an elite attacker, his ball-winning is freakish, ranking in the top 10% for interceptions and tackles, proving he is a defensive asset as much as a creative one. The three most similar players to Ivan Perišić by playing style are:

  • David Neres (83% match)A Inside Forward. Statistically, he stands out as an elite creator (1.7 key passes/90), a regular goalscorer (0.39 goals/90) and creates high-quality scoring opportunities (0.66 big chances/90). Note: this profile is based on 687 minutes of playing time this season.
  • Ayoze Pérez(82% match)A Complete Forward. Statistically, he stands out as a capable chance creator (1.5 key passes/90), a regular goalscorer (0.37 goals/90), a prolific assist provider (0.37 assists/90) and draws fouls effectively (2.1/90). However, he loses possession under pressure (2.3 dispossessed/90).
  • Milot Rashica(81% match)Rashica has evolved from a purebred transitional speedster into a blue-collar creative engine who defies the typical "luxury" winger archetype. While his 72% pass accuracy suggests a lack of refinement, the data reveals a high-risk, high-reward playmaker who sits in the top 5% of the Super Lig for key passes (2.11/90) and assists (0.32/90). The counterintuitive insight here is his defensive ferocity; despite being an attacker, he ranks in the top 5% for both ground duel win rate (83.3%) and tackles won, functioning more like a defensive end in a high-pressing system than a traditional wide man.

Transfer Intelligence

David Neres  delivers 83% of the same playing style, at a 2054% premium over Ivan Perišić, with 0.39 goals per 90 at age 29. That's 134% of Ivan Perišić'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 →

I
Comparison Base
Ivan Perišić
AttackerCroatia€1.3M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
D
David Neres 
Napoli · Serie A
Brazil29yContract 2028
G/900.39
A/900.00
Inside ForwardSmall Sample
vs Perišić: €27M more expensive · 8y younger · +0.10 G/90
83% match
€28.0M
#2
A
Ayoze Pérez
Villarreal · La Liga
Spain32yContract 2028
G/900.37
A/900.37
Complete ForwardSmall Sample
Last 5: → Stable
vs Perišić: 5y younger · +0.08 G/90
82% match
€6.0M
#3
M
Milot Rashica
Beşiktaş · Super Lig
Kosovo29yContract 2027
G/900.09
A/900.28
Small Sample
Last 5: ↓ Dip
vs Perišić: 8y younger · -0.20 G/90
81% match
€4.0M
#4
A
Alassane Pléa
PSV · Eredivisie
France33yContract 2028
G/900.52
A/900.23
Complete ForwardProlific
81% match
€3.0M
#5
V
Virgil Misidjan
NEC Nijmegen · Eredivisie
Netherlands32y
G/900.17
A/900.34
Small Sample
81% match
€3.0M
#6
D
Dennis Man
PSV · Eredivisie
Romania27yContract 2029
G/900.38
A/900.38
Inside ForwardProlific
Last 5: ↓ Dip
80% match
€13.0M
#7
S
Sébastien Haller
FC Utrecht · Eredivisie
Ivory Coast31y
G/900.10
A/900.30
Last 5: ↓ Dip
81% match
€31.0M
#8
E
Esmir Bajraktarevic
PSV · Eredivisie
United States21yContract 2029
G/900.34
A/900.34
Inside Forward
Last 5: ↑ Hot
81% match
€5.0M
#9
A
Antony
Real Betis · La Liga
Brazil26yContract 2030
G/900.36
A/900.25
Inside Forward
Last 5: ↑ Hot
81% match
€95.0M
#10
A
Anis Hadj Moussa
Feyenoord · Eredivisie
Algeria24yContract 2030
G/900.33
A/900.22
Inside ForwardDribbler
Last 5: ↓ Dip
80% match
€20.0M
#11
R
Ricky van Wolfswinkel
FC Twente · Eredivisie
Netherlands37y
G/900.39
A/900.00
Target Man
Last 5: ↓ Dip
81% match
€3.5M
#12
A
Andrej Ilic
FC Union Berlin · Bundesliga
Serbia26yContract 2027
G/900.20
A/900.60
Target Man
81% match
€6.0M

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 Ivan Perišić.

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Frequently Asked Questions

Who are the best alternatives to Ivan Perišić?
The top alternatives to Ivan Perišić based on AI DNA playing style analysis include: David Neres , Ayoze Pérez, Milot Rashica, Alassane Pléa, 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 Ivan Perišić in 2026?
Players with a similar profile to Ivan Perišić in 2026 include David Neres  (€28.0M), Ayoze Pérez (€6.0M), Milot Rashica (€4.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Ivan Perišić play and who plays similarly?
Ivan Perišić plays as a Attacker. Players with a comparable positional profile include David Neres  (Brazil, €28.0M); Ayoze Pérez (Spain, €6.0M); Milot Rashica (Kosovo, €4.0M); Alassane Pléa (France, €3.0M).
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.