RisingTransfers
AI DNA Similarity

Best Alternatives to Kristijan Jakic

Players most similar to Kristijan Jakic (Midfielder, €6.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Kristijan Jakic

  1. 1.Aleksandar Pavlovic87% DNA match·FC Bayern München€75.0M
  2. 2.Xaver Schlager86% DNA match·RB Leipzig€10.0M
  3. 3.Petar Sučić86% DNA match·Inter€30.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%

A Ball-Winner....

See Full Verdict + Share Card →

Playing Style Analysis

Ball-WinnerDefensiveSmall Sample

A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.1 tackles/90), penetrates with forward passing (8.6 final-third passes/90), wins the ball cleanly (2.3 successful tackles/90), heavily involved in play (67 touches/90), uses long balls frequently (6.3/90), a high-intensity presser (press score 3.6/90), constantly disrupting opposition build-up and top 10% tackler in the league. Note: this profile is based on 470 minutes of playing time this season. The three most similar players to Kristijan Jakic by playing style are:

  • Aleksandar Pavlovic(87% match)A Metronome. Statistically, he stands out as comfortable with both feet, a capable chance creator (1.4 key passes/90), active in the tackle (1.8 tackles/90), meticulous in distribution (95% pass accuracy), heavily involved in possession (102 passes/90), penetrates with forward passing (12.1 final-third passes/90), central to possession (115 touches/90), switches play with precision (5.8 long balls/90, 80% accuracy) and active off the ball (2.5 press score/90), contributing to defensive transitions.
  • Xaver Schlager(86% match)A Box-to-Box. Statistically, he stands out as naturally left-footed, a capable chance creator (1.0 key passes/90), active in the tackle (1.9 tackles/90), meticulous in distribution (87% pass accuracy), heavily involved in play (66 touches/90) and active off the ball (3.0 press score/90), contributing to defensive transitions.
  • Petar Sučić(86% 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.

Transfer Intelligence

Aleksandar Pavlovic delivers 87% of the same playing style, at a 1150% premium over Kristijan Jakic, with 1.30 key passes per 90 at age 22.

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

K
Comparison Base
Kristijan Jakic
MidfielderCroatia€6.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
A
Aleksandar Pavlovic
FC Bayern München · Bundesliga
Germany22yContract 2029
KP/901.30
G/900.08
Metronome
Last 5: → Stable
vs Jakic: €69M more expensive · 6y younger
87% match
€75.0M
#2
X
Xaver Schlager
RB Leipzig · Bundesliga
Austria28yContract 2026
KP/900.92
G/900.18
Box-to-Box
Last 5: ↑ Hot
86% match
€10.0M
#3
P
Petar Sučić
Inter · Serie A
Croatia22yContract 2030
KP/900.90
G/900.15
Creative Playmaker
Last 5: ↑ Hot
vs Jakic: €24M more expensive · 6y younger
86% match
€30.0M
#4
R
Romano Schmid
Werder Bremen · Bundesliga
Austria26yContract 2025
KP/902.69
G/900.08
CreatorCreative
85% match
€17.0M
#5
L
Lovro Majer
VfL Wolfsburg · Bundesliga
Croatia28yContract 2028
KP/901.21
G/900.00
CreatorSmall Sample
Last 5: ↓ Dip
86% match
€15.0M
#6
S
Saša Lukić
Fulham · Premier League
Serbia29yContract 2027
KP/901.48
G/900.06
Box-to-Box
Last 5: → Stable
85% match
€12.0M
#7
N
Nicolas Seiwald
RB Leipzig · Bundesliga
Austria25yContract 2028
KP/900.58
G/900.00
Ball-WinnerDefensive
Last 5: → Stable
85% match
€22.0M
#8
S
Sandi Lovrić
Hellas Verona · Serie A
Slovenia28yContract 2027
KP/900.94
G/900.00
Creator
84% match
€6.0M
#9
N
Nikola Vlašić
Torino · Serie A
Croatia28yContract 2027
KP/901.59
G/900.20
CreatorCreative
Last 5: → Stable
84% match
€9.0M
#10
M
Maximilian Eggestein
SC Freiburg · Bundesliga
Germany29y
KP/900.56
G/900.16
Ball-WinnerDefensive
Last 5: ↓ Dip
84% match
€10.0M
#11
E
Ezequiel Fernández
Bayer 04 Leverkusen · Bundesliga
Argentina23yContract 2030
KP/901.03
G/900.00
Ball-WinnerDefensive
Last 5: ↓ Dip
85% match
€25.0M
#12
A
Atakan Karazor
VfB Stuttgart · Bundesliga
Germany29yContract 2028
KP/901.13
G/900.00
Box-to-Box
Last 5: → Stable
85% 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 Kristijan Jakic.

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Frequently Asked Questions

Who are the best alternatives to Kristijan Jakic?
The top alternatives to Kristijan Jakic based on AI DNA playing style analysis include: Aleksandar Pavlovic, Xaver Schlager, Petar Sučić, Romano Schmid, Lovro Majer. 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 Kristijan Jakic in 2026?
Players with a similar profile to Kristijan Jakic in 2026 include Aleksandar Pavlovic (€75.0M), Xaver Schlager (€10.0M), Petar Sučić (€30.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Kristijan Jakic play and who plays similarly?
Kristijan Jakic plays as a Midfielder. Players with a comparable positional profile include Aleksandar Pavlovic (Germany, €75.0M); Xaver Schlager (Austria, €10.0M); Petar Sučić (Croatia, €30.0M); Romano Schmid (Austria, €17.0M).
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.