Best Alternatives to Thomas van den Belt

Top 3 Alternatives to Thomas van den Belt

1. 1.Jerdy Schouten86% DNA match·PSV€22.0M
2. 2.Luciano Valente85% DNA match·Feyenoord€18.0M
3. 3.Kees Smit84% DNA match·AZ€22.0M

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

Playing Style Analysis

A Box-to-Box. Statistically, he stands out as a capable chance creator (1.4 key passes/90), a regular goalscorer (0.21 goals/90), active in the tackle (1.9 tackles/90), heavily involved in play (59 touches/90) and active off the ball (2.7 press score/90), contributing to defensive transitions. The three most similar players to Thomas van den Belt by playing style are:

• Jerdy Schouten(86% match) — Schouten is the kind of midfielder who makes a team's possession game feel inevitable rather than effortful—a metronome who rarely wastes a touch and almost never wastes a pass. His 92.3% pass accuracy and 75.3 passes per 90 both sit in the Eredivisie's top five percent, meaning he isn't just accurate, he's relentlessly active with the ball. The 10.32 passes into the final third per 90 is the counterintuitive number here: for a player who reads as a deep-lying anchor, he's consistently threading the needle forward, ranking top ten in the league.
• Luciano Valente(85% match) — A Box-to-Box. Statistically, he stands out as an elite creator (1.7 key passes/90), a reliable supplier (0.22 assists/90), penetrates with forward passing (8.4 final-third passes/90), heavily involved in play (68 touches/90) and a high-intensity presser (press score 3.2/90), constantly disrupting opposition build-up.
• Kees Smit(84% match) — A Box-to-Box. Statistically, he stands out as an elite creator (1.6 key passes/90), meticulous in distribution (88% pass accuracy), heavily involved in play (67 touches/90) and active off the ball (2.5 press score/90), contributing to defensive transitions. However, he loses possession under pressure (1.6 dispossessed/90).

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