RisingTransfers
AI DNA Similarity

Best Alternatives to Mario Pašalić

Players most similar to Mario Pašalić (Midfielder, €7.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Mario Pašalić

  1. 1.Nikola Vlašić88% DNA match·Torino€9.0M
  2. 2.Petar Sučić88% DNA match·Inter€30.0M
  3. 3.Ivan Ilić87% DNA match·Torino€10.0M

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

RT

Intelligence Verdict

Big ChancesTop 7%
???Bottom 11%

Pašalić has quietly become one of Serie A's most efficient midfield connectors...

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Playing Style Analysis

Creative Playmaker

Pašalić has quietly become one of Serie A's most efficient midfield connectors — a player whose value lives entirely in the spaces between the headlines. His pass accuracy sits in the top 10% of the league, but the more telling number is his 64 passes per 90, which ranks in the top 5% — this isn't a man recycling possession sideways, it's a midfielder who genuinely orchestrates. His 0.22 assists per 90 (top 10%) confirms the end product follows the volume. The three most similar players to Mario Pašalić by playing style are:

  • Nikola Vlašić(88% match)A Creator. Statistically, he stands out as an elite creator (1.6 key passes/90), a regular goalscorer (0.20 goals/90), active in the tackle (1.9 tackles/90), heavily involved in play (50 touches/90) and active off the ball (2.6 press score/90), contributing to defensive transitions.
  • Petar Sučić(88% match)Sučić has quietly built a case as one of Serie A's more quietly effective midfield operators — a Croatian with a continental pedigree who does more with the ball than his modest goal contributions suggest. His 52.2 passes per 90 places him in the league's top 20%, and that volume carries real quality: 87.9% accuracy, 6.36 progressive passes into the final third, and 1.50 key passes per 90 all land in the top tier. Here's the counterintuitive part — his 1.09 tackles won per 90 sits in the top 30%, yet his overall duel win rate is below average, meaning he picks his battles intelligently rather than throwing himself into everything.
  • Ivan Ilić(87% match)Ilić is the kind of midfielder who makes the game look slower than it is—not because he's languid, but because he's almost always in the right place before the problem arrives. His interceptions and key passes both sit in the top 20% of Serie A midfielders, a rare pairing that signals genuine two-way intelligence rather than a specialist's one-trick value. Here's the counterintuitive part: his duel win rate sits in the bottom 10%, which looks damning until you realize that a midfielder winning this many interceptions rarely needs to win duels—he's already cut the pass off.

Transfer Intelligence

Nikola Vlašić delivers 88% of the same playing style, at a 29% premium over Mario Pašalić, with 1.59 key passes per 90 at age 28.

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

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Comparison Base
Mario Pašalić
MidfielderCroatia€7.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
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Nikola Vlašić
Torino · Serie A
Croatia28yContract 2027
KP/901.59
G/900.20
CreatorCreative
Last 5: → Stable
vs Pašalić: 3y younger
88% match
€9.0M
#2
P
Petar Sučić
Inter · Serie A
Croatia22yContract 2030
KP/900.90
G/900.15
Creative Playmaker
Last 5: ↑ Hot
vs Pašalić: €23M more expensive · 9y younger
88% match
€30.0M
#3
I
Ivan Ilić
Torino · Serie A
Serbia25yContract 2027
KP/901.73
G/900.00
Creative Playmaker
vs Pašalić: 6y younger
87% match
€10.0M
#4
S
Sandi Lovrić
Hellas Verona · Serie A
Slovenia28yContract 2027
KP/900.94
G/900.00
Creator
87% match
€6.0M
#5
B
Bryan Cristante
Roma · Serie A
Italy31yContract 2027
KP/900.88
G/900.05
Metronome
Last 5: ↓ Dip
86% match
€7.0M
#6
S
Samuele Ricci
AC Milan · Serie A
Italy24yContract 2029
KP/901.61
G/900.00
Ball-WinnerCreative
Last 5: ↑ Hot
86% match
€25.0M
#7
M
Manu Koné
Roma · Serie A
France24yContract 2029
KP/900.99
G/900.11
Box-to-Box
Last 5: → Stable
86% match
€50.0M
#8
K
Kristjan Asllani
Beşiktaş · Serie A
Albania24yContract 2026
KP/900.99
G/900.22
Ball-WinnerSmall Sample
Last 5: → Stable
86% match
€13.0M
#9
M
Manuel Locatelli
Juventus · Serie A
Italy28yContract 2028
KP/901.88
G/900.00
MetronomeDefensive
Last 5: ↓ Dip
85% match
€25.0M
#10
N
Nicolò Fagioli
Fiorentina · Serie A
Italy25yContract 2028
KP/902.74
G/900.06
Elite Playmaker
Last 5: ↓ Dip
86% match
€14.0M
#11
N
Niccolò Pisilli
Roma · Serie A
Italy21yContract 2029
KP/900.94
G/900.27
Chance Creator
Last 5: ↓ Dip
85% match
€12.0M
#12
L
Lewis Ferguson
Bologna · Serie A
Scotland26yContract 2028
KP/900.76
G/900.00
Chance Creator
Last 5: ↓ Dip
85% match
€18.0M

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 Mario Pašalić.

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Frequently Asked Questions

Who are the best alternatives to Mario Pašalić?
The top alternatives to Mario Pašalić based on AI DNA playing style analysis include: Nikola Vlašić, Petar Sučić, Ivan Ilić, Sandi Lovrić, Bryan Cristante. 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 Mario Pašalić in 2026?
Players with a similar profile to Mario Pašalić in 2026 include Nikola Vlašić (€9.0M), Petar Sučić (€30.0M), Ivan Ilić (€10.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mario Pašalić play and who plays similarly?
Mario Pašalić plays as a Midfielder. Players with a comparable positional profile include Nikola Vlašić (Croatia, €9.0M); Petar Sučić (Croatia, €30.0M); Ivan Ilić (Serbia, €10.0M); Sandi Lovrić (Slovenia, €6.0M).
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.