⬡ AI DNA · Live season

Randers FC· 2025/2026 · Superliga · PARTIAL

Best Alternatives to Sabil Hansen

MidfielderDenmark20yN/AFull profile →

Ball-WinnerDefensive

0.70KP/90

0.35G/90

0.00A/90

Based on 1,212 min · 2025/2026 · Superliga · PARTIAL

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

DNA matchPrioritizes Superliga & top-tier peers

Top 3 alternatives to Sabil Hansen

Rocco Ascone97% match
Nordsjælland · Superliga
Yves Bissouma98% match
Tottenham Hotspur · Premier League · €15.0M
Juanpe98% match
Málaga · La Liga

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

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Playing Style Analysis

Ball-WinnerDefensive

A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (3.9 tackles/90), reads the game exceptionally (2.0 interceptions/90), wins the physical battle (57% duel success), wins the ball cleanly (2.1 successful tackles/90), central to possession (71 touches/90), active off the ball (3.0 press score/90), contributing to defensive transitions and top 10% tackler in the league. The three closest DNA matches to Sabil Hansen:

Rocco Ascone(97% match) — A Ball-Winner. Statistically, he stands out as an elite creator (1.8 key passes/90), an aggressive ball-winner (5.2 tackles/90), wins the physical battle (61% duel success), penetrates with forward passing (8.2 final-third passes/90), wins the ball cleanly (3.3 successful tackles/90), central to possession (71 touches/90), uses long balls frequently (5.1/90), a high-intensity presser (press score 3.4/90), constantly disrupting opposition build-up, top 10% creator in the league and top 10% tackler in the league.
Yves Bissouma(98% match) — A Ball-Winner. Statistically, he stands out as an aggressive ball-winner (2.7 tackles/90), meticulous in distribution (88% pass accuracy), wins the physical battle (68% duel success), heavily involved in play (61 touches/90) and active off the ball (2.8 press score/90), contributing to defensive transitions.
Juanpe(98% match) — A Ball-Winner. Statistically, he stands out as a reliable supplier (0.18 assists/90), an aggressive ball-winner (4.1 tackles/90), reads the game exceptionally (1.9 interceptions/90), wins the physical battle (61% duel success), penetrates with forward passing (10.1 final-third passes/90), wins the ball cleanly (2.4 successful tackles/90), heavily involved in play (70 touches/90), uses long balls frequently (9.0/90), 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

Rocco Ascone

Nordsjælland · Superliga

France22y

KP/902.16

G/900.11

Ball-WinnerCreativeSteady form

vs Hansen: 2y older

97% match

N/A

Compare Hansen vs Ascone →

Ask about replacements for Hansen

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Frequently Asked Questions

Who are the best alternatives to Sabil Hansen?▼

The top alternatives to Sabil Hansen based on AI DNA playing style analysis include: Rocco Ascone, Yves Bissouma, Juanpe, Guido Rodríguez, 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 Sabil Hansen in 2026?▼

Players with a similar profile to Sabil Hansen in 2026 include Rocco Ascone (N/A), Yves Bissouma (€15.0M), Juanpe (N/A). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.

What position does Sabil Hansen play and who plays similarly?▼

Sabil Hansen plays as a Midfielder. Players with a comparable positional profile include Rocco Ascone (France, N/A); Yves Bissouma (Mali, €15.0M); Juanpe (Spain, N/A); Guido Rodríguez (Argentina, €6.0M).

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.

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