RisingTransfers
AI DNA Similarity

Best Alternatives to Marin Pongracic

Players most similar to Marin Pongracic (Defender, €7.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Marin Pongracic

  1. 1.Sebastian Walukiewicz86% DNA match·Sassuolo€4.5M
  2. 2.Strahinja Pavlović86% DNA match·AC Milan€28.0M
  3. 3.Manuel Akanji85% DNA match·Inter€22.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 16%

Pongracic has carved out a niche in Serie A that most defenders can't claim: a ball-playing...

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Playing Style Analysis

Ball-Playing CBBall-Playing

Pongracic has carved out a niche in Serie A that most defenders can't claim: a ball-playing centre-back whose engine runs as hard as his passing. Sitting in the top 10% for both pass accuracy and pressing intensity, he's not just tidy in possession—he's actively disruptive without it, which is a rarer combination than the numbers suggest. His 61.7 passes per 90 places him in the top 5% of the league, yet the counterintuitive story here is that volume doesn't come at the cost of precision; this is a defender who genuinely moves teams up the pitch. The three most similar players to Marin Pongracic by playing style are:

  • Sebastian Walukiewicz(86% match)A Defender.
  • Strahinja Pavlović(86% match)A Ball-Playing CB. Statistically, he stands out as naturally left-footed, a regular goalscorer (0.26 goals/90) and meticulous in distribution (94% pass accuracy). Note: this profile is based on 704 minutes of playing time this season.
  • Manuel Akanji(85% match)Akanji builds play with the precision of a midfielder wearing a centre-back's shirt—his 92.2% pass accuracy sits in the top 10% of Serie A defenders, and his 67.5 passes per 90 ranks in the top 5%, making him one of the league's most active distributors from deep. His 6.43 passes into the final third per 90 places him in the top 20%, meaning his value isn't just in keeping possession clean but in genuinely advancing it. The counterintuitive read here: his aerial win rate of 44.1% looks soft, yet he wins 1.62 aerials per 90 above the positional average—he's simply not seeking duels he can avoid, which speaks to intelligence rather than deficiency.

Transfer Intelligence

Sebastian Walukiewicz delivers 86% of the same playing style, at 40% lower cost (€4.5M vs €7.5M), with 1.58 tackles won per 90 at age 26.

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

M
Comparison Base
Marin Pongracic
DefenderCroatia€7.5M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
S
Sebastian Walukiewicz
Sassuolo · Serie A
Poland26yContract 2026
Tkl/901.58
KP/900.59
Last 5: → Stable
vs Pongracic: 2y younger
86% match
€4.5M
#2
S
Strahinja Pavlović
AC Milan · Serie A
Serbia24yContract 2028
Tkl/901.66
KP/900.51
Ball-Playing CBSmall Sample
Last 5: ↑ Hot
vs Pongracic: €21M more expensive · 4y younger
86% match
€28.0M
#3
M
Manuel Akanji
Inter · Serie A
Switzerland30yContract 2026
Tkl/901.47
KP/900.20
Ball-Playing CBBall-Playing
Last 5: ↓ Dip
vs Pongracic: €15M more expensive · 2y older
85% match
€22.0M
#4
Y
Yann Bisseck
Inter · Serie A
Germany25yContract 2029
Tkl/900.98
KP/900.20
Last 5: ↓ Dip
86% match
€35.0M
#5
O
Odilon Kossounou
Atalanta · Serie A
Ivory Coast25yContract 2029
Tkl/901.33
KP/900.11
Ball-Playing CBBall-Playing
Last 5: → Stable
85% match
€22.0M
#6
S
Stefan Posch
FSV Mainz 05 · Bundesliga
Austria28yContract 2026
Tkl/901.50
KP/900.25
Physical StopperAerial
Last 5: → Stable
85% match
€5.5M
#7
S
Sascha Britschgi
Parma · Serie A
Switzerland19yContract 2030
Tkl/901.38
KP/900.86
Active Full-Back
85% match
€7.0M
#8
N
Nicolò Bertola
Udinese · Serie A
Italy23yContract 2030
Tkl/901.78
KP/900.49
Solid DefenderAerial
85% match
€6.0M
#9
O
Oumar Solet
Udinese · Serie A
France26yContract 2027
Tkl/902.13
KP/900.67
Ball-Playing CBBall-Playing
Last 5: ↑ Hot
85% match
€20.0M
#10
C
Carlos Augusto
Inter · Serie A
Brazil27yContract 2028
Tkl/902.48
KP/900.83
Active Full-BackBall-Playing
Last 5: ↓ Dip
85% match
€26.0M
#11
T
Thomas Kristensen
Udinese · Serie A
Denmark24yContract 2028
Tkl/901.08
KP/900.08
Physical StopperAerial
Last 5: ↓ Dip
85% match
€12.0M
#12
L
Luca Pellegrini
Lazio · Serie A
Italy27yContract 2027
Tkl/901.82
KP/900.91
Active Full-BackSmall Sample
85% match
€3.5M

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 Marin Pongracic.

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Frequently Asked Questions

Who are the best alternatives to Marin Pongracic?
The top alternatives to Marin Pongracic based on AI DNA playing style analysis include: Sebastian Walukiewicz, Strahinja Pavlović, Manuel Akanji, Yann Bisseck, Odilon Kossounou. 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 Marin Pongracic in 2026?
Players with a similar profile to Marin Pongracic in 2026 include Sebastian Walukiewicz (€4.5M), Strahinja Pavlović (€28.0M), Manuel Akanji (€22.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Marin Pongracic play and who plays similarly?
Marin Pongracic plays as a Defender. Players with a comparable positional profile include Sebastian Walukiewicz (Poland, €4.5M); Strahinja Pavlović (Serbia, €28.0M); Manuel Akanji (Switzerland, €22.0M); Yann Bisseck (Germany, €35.0M).
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.