RisingTransfers
AI DNA Similarity

Best Alternatives to Dave Kwakman

Players most similar to Dave Kwakman (Midfielder, N/A) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Dave Kwakman

  1. 1.Tyler Onyango99% DNA match·Everton
  2. 2.Sem Scheperman98% DNA match·Heracles Almelo
  3. 3.José Luís Rocha Tavares97% DNA match·Santa Clara

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

RT

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 (3.8 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 (2.3/90), 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 717 minutes of playing time this season. The three most similar players to Dave Kwakman by playing style are:

  • Tyler Onyango(99% match)A Ball-Winner. Statistically, he stands out as a prolific assist provider (0.25 assists/90), an aggressive ball-winner (3.4 tackles/90), wins the ball cleanly (2.9 successful tackles/90), heavily involved in play (59 touches/90), active off the ball (2.7 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.
  • Sem Scheperman(98% match)A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.8 tackles/90), meticulous in distribution (86% pass accuracy), wins the physical battle (62% duel success), wins the ball cleanly (2.3 successful tackles/90), active off the ball (2.0 press score/90), contributing to defensive transitions and top 10% tackler in the league.
  • José Luís Rocha Tavares(97% match)A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.1 tackles/90), wins the physical battle (55% duel success), wins the ball cleanly (2.1 successful tackles/90), heavily involved in play (58 touches/90), draws fouls effectively (3.0/90), a high-intensity presser (press score 3.0/90), constantly disrupting opposition build-up and top 10% tackler in the league. However, he prone to committing fouls (3.5/90).

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

D
Comparison Base
Dave Kwakman
MidfielderNetherlandsN/A
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Similar Players — Ranked by DNA Similarity

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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 Dave Kwakman.

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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: Tyler Onyango, Sem Scheperman, José Luís Rocha Tavares, Gonçalo Nogueira, Juanpe. 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 Tyler Onyango (N/A), Sem Scheperman (N/A), José Luís Rocha Tavares (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 Tyler Onyango (England, N/A); Sem Scheperman (Netherlands, N/A); José Luís Rocha Tavares (Cape Verde, N/A); Gonçalo Nogueira (Portugal, 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.