Best Alternatives to Kevin Danso

Top 3 Alternatives to Kevin Danso

1. 1.Malick Thiaw88% DNA match·Newcastle United€45.0M
2. 2.Jaka Bijol88% DNA match·Leeds United€18.0M
3. 3.Sepp van den Berg87% DNA match·Brentford€28.0M

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

Playing Style Analysis

A Ball-Playing CB. Statistically, he stands out as active in the tackle (1.9 tackles/90), commanding in the air (8.3 clearances/90), meticulous in distribution (86% pass accuracy), wins the physical battle (62% duel success), central to possession (78 touches/90), dominant in the air (4.8 aerials won/90, 63%) and uses long balls frequently (6.0/90). The three most similar players to Kevin Danso by playing style are:

• Malick Thiaw(88% match) — Thiaw has quietly become one of the Premier League's most complete defensive profiles — a towering centre-back who doesn't just win his battles, he wins them cleanly. His aerial win rate of 65.1% and duel success of 64.2% both sit in the league's top 20%, but the counterintuitive story is his attacking output: 0.16 goals per 90 and 0.98 shots per 90 place him in the top 10% among defenders, suggesting a genuine threat from set-pieces that opponents routinely underestimate. His pass accuracy of 90.6% reflects a ball-player comfortable in possession-heavy systems, and his above-average delivery into the final third adds genuine build-up value.
• Jaka Bijol(88% match) — A Ball-Playing CB. Statistically, he stands out as commanding in the air (7.0 clearances/90), meticulous in distribution (85% pass accuracy), wins the physical battle (56% duel success), strong in aerial duels (3.2 aerials won/90) and uses long balls frequently (6.3/90).
• Sepp van den Berg(87% match) — A Ball-Playing CB. Statistically, he stands out as commanding in the air (6.2 clearances/90), meticulous in distribution (88% pass accuracy), wins the physical battle (66% duel success) and dominant in the air (4.6 aerials won/90, 68%).

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