Best Alternatives to Jens Odgaard
Players most similar to Jens Odgaard (Midfielder, €15.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.
Top 3 Alternatives to Jens Odgaard
- 1.Kristian Thorstvedt88% DNA match·Sassuolo€10.0M
- 2.Albert Grønbæk88% DNA match·Hamburger SV€8.0M
- 3.Jurgen Ekkelenkamp87% DNA match·Udinese€7.0M
Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.
Intelligence Verdict
“A Creator....”
See Full Verdict + Share Card →Playing Style Analysis
A Creator. Statistically, he stands out as naturally left-footed, a regular goalscorer (0.33 goals/90) and active off the ball (2.1 press score/90), contributing to defensive transitions. However, he loses possession under pressure (2.1 dispossessed/90). The three most similar players to Jens Odgaard by playing style are:
- Kristian Thorstvedt(88% match) — A Box-to-Box. Statistically, he stands out as a regular goalscorer (0.27 goals/90), a prolific assist provider (0.27 assists/90), reads the game exceptionally (2.0 interceptions/90), heavily involved in play (58 touches/90), draws fouls effectively (2.1/90) and active off the ball (2.4 press score/90), contributing to defensive transitions. Note: this profile is based on 673 minutes of playing time this season.
- Albert Grønbæk(88% match) — A Creator. Statistically, he stands out as an elite creator (2.1 key passes/90), a regular goalscorer (0.22 goals/90), active in the tackle (2.4 tackles/90), creates high-quality scoring opportunities (0.78 big chances/90) and top 10% creator in the league. However, he can be exposed in 1v1 situations and loses possession under pressure (1.6 dispossessed/90).
- Jurgen Ekkelenkamp(87% match) — A Creator. Statistically, he stands out as a capable chance creator (1.4 key passes/90) and active in the tackle (2.1 tackles/90).
Transfer Intelligence
Kristian Thorstvedt delivers 88% of the same playing style, at 33% lower cost (€10.0M vs €15.0M), with 0.67 key passes per 90 at age 27.
Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →
Similar Players — Ranked by DNA Similarity
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