Best Alternatives to Artem Dovbyk

Top 3 Alternatives to Artem Dovbyk

1. 1.Anastasios Douvikas 88% DNA match·Como€10.0M
2. 2.Romelu Lukaku85% DNA match·Napoli€15.0M
3. 3.Jonathan David84% DNA match·Juventus€35.0M

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

Playing Style Analysis

Dovbyk arrived in Serie A and immediately reminded everyone why Roma paid serious money for a striker who makes the unglamorous look ruthless. His 0.70 goals per 90 and 2.58 key passes per 90 both land in the top 5% of Serie A forwards—a rare double that separates genuine attacking weapons from volume merchants. The counterintuitive detail: his below-average dribbling and ground duel numbers (bottom half of the league) suggest a player who looks limited in tight spaces, yet his aerial dominance—top 10% for aerials won—and smart positional instincts mean he rarely needs to beat men to be dangerous. The three most similar players to Artem Dovbyk by playing style are:

• Anastasios Douvikas (88% match) — A Poacher. Statistically, he stands out as a capable chance creator (1.3 key passes/90), a proven goalscorer (0.67 goals/90) and top 10% scorer in the league. However, he loses possession under pressure (1.9 dispossessed/90).
• Romelu Lukaku(85% match) — A Complete Forward. Statistically, he stands out as naturally left-footed, a capable chance creator (1.1 key passes/90), a proven goalscorer (0.44 goals/90), a prolific assist provider (0.31 assists/90) and top 20% scorer in the league. However, he loses possession under pressure (1.7 dispossessed/90).
• Jonathan David(84% match) — A Complete Forward. Statistically, he stands out as comfortable with both feet, an elite creator (1.9 key passes/90), a constant goal threat (2.9 shots/90), a regular goalscorer (0.39 goals/90), a prolific assist provider (0.39 assists/90) and creates high-quality scoring opportunities (0.52 big chances/90). Note: this profile is based on 692 minutes of playing time this season.

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