Best Alternatives to Torbjørn Heggem

Top 3 Alternatives to Torbjørn Heggem

1. 1.Eivind Helland86% DNA match·Bologna€7.0M
2. 2.Thomas Kristensen85% DNA match·Udinese€12.0M
3. 3.Emil Holm84% DNA match·Juventus€13.0M

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

Playing Style Analysis

Heggem is the kind of defender Serie A's mid-table sides quietly depend on — metronomic in possession, aerially competitive, and disciplined enough to keep things ticking without drama. His 90.7% pass accuracy places him in the top 20% of defenders in the division, a figure that reflects genuine composure rather than safe sideways shuffling — his 48.3 passes per 90 rank above average, suggesting real involvement in build-up. The counterintuitive detail: his above-average aerial volume and win rate make him more physically imposing than his tidy technical numbers first suggest. The three most similar players to Torbjørn Heggem by playing style are:

• Eivind Helland(86% match) — A Ball-Playing CB. Statistically, he stands out as commanding in the air (4.7 clearances/90), meticulous in distribution (87% pass accuracy), wins the physical battle (77% duel success) and active off the ball (2.0 press score/90), contributing to defensive transitions. Note: this profile is based on 828 minutes of playing time this season.
• Thomas Kristensen(85% match) — A Physical Stopper. Statistically, he stands out as commanding in the air (5.8 clearances/90) and wins the physical battle (57% duel success).
• Emil Holm(84% match) — A Active Full-Back. Statistically, he stands out as a prolific assist provider (0.55 assists/90), an aggressive ball-winner (2.7 tackles/90), wins the physical battle (55% duel success), central to possession (72 touches/90), a high-intensity presser (press score 3.2/90), constantly disrupting opposition build-up and top 10% tackler in the league. Note: this profile is based on 656 minutes of playing time this season.

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