RisingTransfers
AI DNA Similarity

Best Alternatives to Gvidas Gineitis

Players most similar to Gvidas Gineitis (Midfielder, €5.5M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Gvidas Gineitis

  1. 1.Karol Linetty86% DNA match·Kocaelispor€7.5M
  2. 2.Martin Baturina85% DNA match·Como€18.0M
  3. 3.Mandela Keita84% DNA match·Parma€12.0M

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

RT

Intelligence Verdict

Chances MissedTop 0%
???Bottom 0%

A Creator....

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Playing Style Analysis

CreatorCreativeDefensiveSmall Sample

A Creator. Statistically, he stands out as naturally left-footed, an elite creator (2.1 key passes/90), an aggressive ball-winner (2.6 tackles/90), creates high-quality scoring opportunities (0.64 big chances/90), wins the ball cleanly (1.8 successful tackles/90), heavily involved in play (51 touches/90) and top 10% creator in the league. However, he prone to committing fouls (2.8/90). The three most similar players to Gvidas Gineitis by playing style are:

  • Karol Linetty(86% match)Linetty is the kind of midfielder who quietly threads a match together without ever demanding the spotlight — a Polish journeyman who has found a second wind in the Turkish top flight by doing the unglamorous work exceptionally well. His key pass rate lands in the top 30% of Super Lig midfielders, yet his dribble attempts sit in the bottom 10%, which tells you everything: he moves the ball, not himself. The counterintuitive read here is that his low duel win rate — genuinely bottom 10% — matters less than it looks, because his interception numbers suggest he's positioning smartly enough to avoid the contest entirely.
  • Martin Baturina(85% match)A Creator. Statistically, he stands out as an elite creator (4.1 key passes/90), a proven goalscorer (0.51 goals/90), a prolific assist provider (0.31 assists/90), meticulous in distribution (88% pass accuracy), creates high-quality scoring opportunities (0.72 big chances/90), heavily involved in play (70 touches/90), draws fouls effectively (2.4/90), active off the ball (2.2 press score/90), contributing to defensive transitions and top 10% creator in the league. However, he loses possession under pressure (1.6 dispossessed/90).
  • Mandela Keita(84% match)A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (2.9 tackles/90), meticulous in distribution (89% pass accuracy), wins the physical battle (65% duel success), wins the ball cleanly (1.8 successful tackles/90), active off the ball (2.6 press score/90), contributing to defensive transitions and top 10% tackler in the league.

Transfer Intelligence

Karol Linetty delivers 86% of the same playing style, at a 36% premium over Gvidas Gineitis, with 1.71 key passes per 90 at age 31.

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

G
Comparison Base
Gvidas Gineitis
MidfielderLithuania€5.5M
Full profile →

Similar Players — Ranked by DNA Similarity

#1
K
Karol Linetty
Kocaelispor · Super Lig
Poland31y
KP/901.71
G/900.00
CreatorCreative
Last 5: → Stable
vs Gineitis: 9y older
86% match
€7.5M
#2
M
Martin Baturina
Como · Serie A
Croatia23yContract 2030
KP/904.11
G/900.51
CreatorCreative
Last 5: → Stable
vs Gineitis: €13M more expensive
85% match
€18.0M
#3
M
Mandela Keita
Parma · Serie A
Belgium24yContract 2029
KP/900.34
G/900.07
Ball-WinnerDefensive
vs Gineitis: €7M more expensive · 2y older
84% match
€12.0M
#4
A
Alberto Grassi
Cremonese · Serie A
Italy31y
KP/900.31
G/900.00
Box-to-BoxSmall Sample
Last 5: ↑ Hot
85% match
€8.4M
#5
P
Patrizio Masini
Genoa · Serie A
Italy25yContract 2028
KP/900.84
G/900.00
Ball-WinnerDefensive
Last 5: → Stable
85% match
€6.0M
#6
N
Niccolò Pisilli
Roma · Serie A
Italy21yContract 2029
KP/900.94
G/900.27
Chance Creator
Last 5: ↓ Dip
84% match
€12.0M
#7
S
Samuele Ricci
AC Milan · Serie A
Italy24yContract 2029
KP/901.61
G/900.00
Ball-WinnerCreative
Last 5: ↑ Hot
84% match
€25.0M
#8
A
Andrea Cambiaso
Juventus · Serie A
Italy26yContract 2029
KP/902.38
G/900.00
CreatorCreative
Last 5: ↑ Hot
83% match
€30.0M
#9
B
Bryan Cristante
Roma · Serie A
Italy31yContract 2027
KP/900.88
G/900.05
Metronome
Last 5: ↓ Dip
83% match
€7.0M
#10
P
Peer Koopmeiners
AZ · Eredivisie
Netherlands26yContract 2028
KP/901.10
G/900.09
Ball-WinnerDefensive
Last 5: → Stable
84% match
€10.0M
#11
M
Michel Aebischer
Pisa · Serie A
Switzerland29yContract 2026
KP/901.05
G/900.00
Balanced Midfielder
Last 5: ↑ Hot
83% match
€4.0M
#12
H
Hugo Magnetti
Brest · Ligue 1
France27yContract 2027
KP/900.48
G/900.10
Balanced Midfielder
Last 5: → Stable
83% 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 Gvidas Gineitis.

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Frequently Asked Questions

Who are the best alternatives to Gvidas Gineitis?
The top alternatives to Gvidas Gineitis based on AI DNA playing style analysis include: Karol Linetty, Martin Baturina, Mandela Keita, Alberto Grassi, Patrizio Masini. 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 Gvidas Gineitis in 2026?
Players with a similar profile to Gvidas Gineitis in 2026 include Karol Linetty (€7.5M), Martin Baturina (€18.0M), Mandela Keita (€12.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Gvidas Gineitis play and who plays similarly?
Gvidas Gineitis plays as a Midfielder. Players with a comparable positional profile include Karol Linetty (Poland, €7.5M); Martin Baturina (Croatia, €18.0M); Mandela Keita (Belgium, €12.0M); Alberto Grassi (Italy, €8.4M).
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.