⬡ AI DNA · Live season

FC Volendam· 2025/2026 · Eredivisie · FULL

Best Alternatives to Dave Kwakman

MidfielderNetherlands21yN/AFull profile →

Ball-WinnerDefensiveSmall Sample

0.74KP/90

0.00G/90

0.50A/90

Based on 726 min · 2025/2026 · Eredivisie · FULL

Ranked by AI DNA similarity — playing style, pressing, and tactical fit. Below: the closest replacements with per-90 stats and form.

DNA matchPrioritizes Eredivisie & top-tier peers

Top 3 alternatives to Dave Kwakman

Sem Scheperman98% match
Heracles Almelo · Eredivisie
Dyon Dorenbosch97% match
FC Eindhoven · Eredivisie
Gibson Yah96% match
FC Volendam · Eredivisie

Ranked by RT Player DNA + league/value credibility. Scroll for full list, per-90 stats, and share cards.

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Intelligence Verdict

AssistsTop 2%

“A Ball-Winner....”

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Playing Style Analysis

Ball-WinnerDefensiveSmall Sample

A Ball-Winner. Statistically, he stands out as a prolific assist provider (0.50 assists/90), an aggressive ball-winner (3.7 tackles/90), meticulous in distribution (85% pass accuracy), wins the physical battle (56% duel success), wins the ball cleanly (2.0 successful tackles/90), heavily involved in play (60 touches/90), draws fouls effectively (2.2/90), active off the ball (2.8 press score/90), contributing to defensive transitions and top 10% tackler in the league. Note: this profile is based on 726 minutes of playing time this season. The three closest DNA matches to Dave Kwakman:

Sem Scheperman(98% match) — A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.5 tackles/90), meticulous in distribution (86% pass accuracy), wins the physical battle (63% duel success), wins the ball cleanly (2.1 successful tackles/90), active off the ball (2.1 press score/90), contributing to defensive transitions and top 10% tackler in the league.
Dyon Dorenbosch(97% match) — A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.2 tackles/90), wins the ball cleanly (1.8 successful tackles/90), heavily involved in play (52 touches/90), active off the ball (2.3 press score/90), contributing to defensive transitions and top 10% tackler in the league.
Gibson Yah(96% match) — A Ball-Winner. Statistically, he stands out as a reliable supplier (0.16 assists/90), an aggressive ball-winner (3.6 tackles/90), reads the game exceptionally (1.7 interceptions/90), wins the physical battle (62% duel success), wins the ball cleanly (2.3 successful tackles/90), heavily involved in play (67 touches/90), a high-intensity presser (press score 3.5/90), constantly disrupting opposition build-up and top 10% tackler in the league.

Similarity uses per-90 performance across playing-style dimensions. How Player DNA matching works →

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Similar Players — Ranked by DNA Similarity

Sem Scheperman

Heracles Almelo · Eredivisie

Netherlands24y

KP/900.40

G/900.08

Ball-WinnerDefensiveSteady form

vs Kwakman: 3y older

98% match

N/A

Compare Kwakman vs Scheperman →

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Frequently Asked Questions

Who are the best alternatives to Dave Kwakman?▼

The top alternatives to Dave Kwakman based on AI DNA playing style analysis include: Sem Scheperman, Dyon Dorenbosch, Gibson Yah, Juanpe, Jonah Sticker. 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 Dave Kwakman in 2026?▼

Players with a similar profile to Dave Kwakman in 2026 include Sem Scheperman (N/A), Dyon Dorenbosch (N/A), Gibson Yah (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Dave Kwakman play and who plays similarly?▼

Dave Kwakman plays as a Midfielder. Players with a comparable positional profile include Sem Scheperman (Netherlands, N/A); Dyon Dorenbosch (Netherlands, N/A); Gibson Yah (Netherlands, N/A); Juanpe (Spain, N/A).

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.

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