RisingTransfers
AI DNA Similarity

Best Alternatives to Mirko Topić

Players most similar to Mirko Topić (Midfielder, €5.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Mirko Topić

  1. 1.Kristijan Jakic85% DNA match·FC Augsburg€6.0M
  2. 2.Saša Lukić84% DNA match·Fulham€12.0M
  3. 3.Yegor Yarmolyuk82% DNA match·Brentford€25.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 0%

Topić is the kind of midfielder Championship clubs quietly depend on but rarely celebrate—a press-triggering...

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Playing Style Analysis

Box-to-Box

Topić is the kind of midfielder Championship clubs quietly depend on but rarely celebrate—a press-triggering, interception-hunting engine who keeps possession ticking without ever demanding the spotlight. His press intensity ranks in the top 10% of the division, and his interception numbers sit alongside the league's best readers of the game, suggesting a player who doesn't just work hard but works intelligently. The counterintuitive detail: his 46.3 passes per 90 and 83.4% accuracy look modest on paper, yet his passes into the final third rank above average, meaning he's not just recycling—he's progressing. The three most similar players to Mirko Topić by playing style are:

  • Kristijan Jakic(85% match)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.
  • Saša Lukić(84% match)A Box-to-Box. Statistically, he stands out as a capable chance creator (1.5 key passes/90), active in the tackle (2.4 tackles/90), meticulous in distribution (85% pass accuracy), heavily involved in play (54 touches/90) and active off the ball (2.1 press score/90), contributing to defensive transitions. However, he prone to committing fouls (2.7/90).
  • Yegor Yarmolyuk(82% match)A Box-to-Box. Statistically, he stands out as active in the tackle (2.4 tackles/90), heavily involved in play (59 touches/90) and a high-intensity presser (press score 3.2/90), constantly disrupting opposition build-up.

Transfer Intelligence

Kristijan Jakic delivers 85% of the same playing style, at a 20% premium over Mirko Topić, with 0.77 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 →

M
Comparison Base
Mirko Topić
MidfielderSerbia€5.0M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
K
Kristijan Jakic
FC Augsburg · Bundesliga
Croatia28yContract 2028
KP/900.77
G/900.15
Ball-WinnerDefensive
Last 5: → Stable
vs Topić: 3y older
85% match
€6.0M
#2
S
Saša Lukić
Fulham · Premier League
Serbia29yContract 2027
KP/901.48
G/900.06
Box-to-Box
Last 5: → Stable
vs Topić: €7M more expensive · 4y older
84% match
€12.0M
#3
Y
Yegor Yarmolyuk
Brentford · Premier League
Ukraine22yContract 2031
KP/900.56
G/900.04
Box-to-Box
Last 5: ↓ Dip
vs Topić: €20M more expensive · 3y younger
82% match
€25.0M
#4
R
Ramiz Zerrouki
FC Twente · Eredivisie
Algeria27y
KP/901.56
G/900.11
MetronomeCreative
Last 5: ↑ Hot
82% match
€7.2M
#5
C
Carlos Baleba
Brighton & Hove Albion · Premier League
Cameroon22yContract 2028
KP/900.35
G/900.00
Box-to-Box
Last 5: ↑ Hot
83% match
€60.0M
#6
S
Sandi Lovrić
Hellas Verona · Serie A
Slovenia28yContract 2027
KP/900.94
G/900.00
Creator
82% match
€6.0M
#7
T
Tommaso Pobega
Bologna · Serie A
Italy26yContract 2026
KP/900.36
G/900.18
Balanced Midfielder
Last 5: ↑ Hot
83% match
€9.0M
#8
F
Freddie Potts
West Ham United · Premier League
England22yContract 2029
KP/900.62
G/900.00
Box-to-Box
Last 5: ↑ Hot
82% match
€8.0M
#9
K
Karol Linetty
Kocaelispor · Super Lig
Poland31y
KP/901.71
G/900.00
CreatorCreative
Last 5: → Stable
82% match
€7.5M
#10
I
Ivan Ilić
Torino · Serie A
Serbia25yContract 2027
KP/901.73
G/900.00
Creative Playmaker
82% match
€10.0M
#11
A
Antoni Milambo
Brentford · Premier League
Netherlands21yContract 2030
KP/901.22
G/900.14
Creator
81% match
€20.0M
#12
X
Xaver Schlager
RB Leipzig · Bundesliga
Austria28yContract 2026
KP/900.92
G/900.18
Box-to-Box
Last 5: ↑ Hot
82% match
€10.0M

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 Mirko Topić.

Ask AI about Mirko Topić

Frequently Asked Questions

Who are the best alternatives to Mirko Topić?
The top alternatives to Mirko Topić based on AI DNA playing style analysis include: Kristijan Jakic, Saša Lukić, Yegor Yarmolyuk, Ramiz Zerrouki, Carlos Baleba. 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 Mirko Topić in 2026?
Players with a similar profile to Mirko Topić in 2026 include Kristijan Jakic (€6.0M), Saša Lukić (€12.0M), Yegor Yarmolyuk (€25.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mirko Topić play and who plays similarly?
Mirko Topić plays as a Midfielder. Players with a comparable positional profile include Kristijan Jakic (Croatia, €6.0M); Saša Lukić (Serbia, €12.0M); Yegor Yarmolyuk (Ukraine, €25.0M); Ramiz Zerrouki (Algeria, €7.2M).
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.