Best Alternatives to Paul Mukairu

Top 3 Alternatives to Paul Mukairu

1. 1.Anis Hadj Moussa82% DNA match·Feyenoord€20.0M
2. 2.Anthony Musaba83% DNA match·Fenerbahçe€4.0M
3. 3.Kamaldeen Sulemana83% DNA match·Atalanta€18.0M

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

Playing Style Analysis

Mukairu is a high-volume creative engine operating from the flanks, a player whose statistical profile suggests he is far more of a refined playmaker than a traditional goal-hungry winger. While his 0.20 goals per 90 is respectable, the real value lies in his elite chance creation; sitting in the top 10% for both key passes and assists, he acts as the primary architect for Tier C attacks. He isn't just a dribbler—though his 2.50 take-ons per 90 place him among the league’s most elusive—he is a high-retention progressor who completes 78.3% of his passes. The three most similar players to Paul Mukairu by playing style are:

• Anis Hadj Moussa(82% match) — A Inside Forward. Statistically, he stands out as naturally left-footed, an elite creator (2.4 key passes/90), a constant goal threat (2.7 shots/90), a regular goalscorer (0.33 goals/90), a reliable supplier (0.22 assists/90), a dynamic dribbler (2.7/90) and creates high-quality scoring opportunities (0.58 big chances/90).
• Anthony Musaba(83% match) — A Inside Forward. Statistically, he stands out as a capable chance creator (1.1 key passes/90), a constant goal threat (3.1 shots/90), a regular goalscorer (0.31 goals/90), a reliable supplier (0.15 assists/90), a dynamic dribbler (2.1/90) and creates high-quality scoring opportunities (0.61 big chances/90). However, he loses possession under pressure (1.5 dispossessed/90).
• Kamaldeen Sulemana(83% match) — A Dynamic Forward. Statistically, he stands out as a regular goalscorer (0.22 goals/90), a reliable supplier (0.22 assists/90) and a dynamic dribbler (2.1/90). Note: this profile is based on 808 minutes of playing time this season.

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