RisingTransfers
AI DNA Similarity

Best Alternatives to Mikayil Faye

Players most similar to Mikayil Faye (Defender, €10.3M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Mikayil Faye

  1. 1.Oliver Fobassam87% DNA match·Zwickau
  2. 2.Ousmane Diao86% DNA match·FC Midtjylland
  3. 3.Terence Kongolo86% DNA match·NAC Breda€4.4M

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

Playing Style Analysis

Ball-Playing CBBall-PlayingSmall Sample

A Ball-Playing CB. Statistically, he stands out as naturally left-footed, active in the tackle (1.8 tackles/90), reads the game exceptionally (1.6 interceptions/90), meticulous in distribution (89% pass accuracy), wins the physical battle (56% duel success), heavily involved in possession (68 passes/90), central to possession (84 touches/90) and uses long balls frequently (5.8/90). Note: this profile is based on 688 minutes of playing time this season. The three most similar players to Mikayil Faye by playing style are:

  • Oliver Fobassam(87% match)Oliver Fobassam Nawe is a Ball-Playing CB. Progressive defender comfortable on the ball. Statistically, he stands out as reads the game exceptionally (1.6 interceptions/90) and wins the physical battle (62% duel success). (Limited sample: 278 mins)
  • Ousmane Diao(86% match)Ousmane Diao is a Ball-Playing CB. Progressive defender comfortable on the ball. Statistically, he stands out as active in the tackle (2.0 tackles/90), commanding in the air (5.4 clearances/90), meticulous in distribution (86% pass accuracy), wins the physical battle (58% duel success), central to possession (70 touches/90) and uses long balls frequently (8.0/90).
  • Terence Kongolo(86% match)A Ball-Playing CB. Statistically, he stands out as active in the tackle (2.0 tackles/90), commanding in the air (6.0 clearances/90), reads the game exceptionally (2.1 interceptions/90), meticulous in distribution (87% pass accuracy), wins the physical battle (74% duel success), strong in aerial duels (3.2 aerials won/90) and active off the ball (2.0 press score/90), contributing to defensive transitions. Note: this profile is based on 878 minutes of playing time this season.

Transfer Intelligence

Terence Kongolo delivers 86% of the same playing style, at 57% lower cost (€4.4M vs €10.3M), with 2.47 tackles won per 90 at age 32.

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

M
Comparison Base
Mikayil Faye
DefenderSenegal€10.3M
Full profile →

Similar Players — Ranked by DNA Similarity

Ask AI: Why are these players similar?

Our 768-dimension Player DNA model matches playing style, physical profile, pressing intensity, and tactical fit. Ask the AI to explain exactly what makes these players statistically similar to Mikayil Faye.

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Frequently Asked Questions

Who are the best alternatives to Mikayil Faye?
The top alternatives to Mikayil Faye based on AI DNA playing style analysis include: Oliver Fobassam, Ousmane Diao, Terence Kongolo, Christopher Wooh, Emil Christensen. These players were matched using Rising Transfers' 768-dimension DNA model across playing style, pressing intensity, and tactical fit — not just position or market value.
Which players are similar to Mikayil Faye in 2026?
Players with a similar profile to Mikayil Faye in 2026 include Oliver Fobassam (N/A), Ousmane Diao (N/A), Terence Kongolo (€4.4M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Mikayil Faye play and who plays similarly?
Mikayil Faye plays as a Defender. Players with a comparable positional profile include Oliver Fobassam (Germany, N/A); Ousmane Diao (Senegal, N/A); Terence Kongolo (Netherlands, €4.4M); Christopher Wooh (France, €4.5M).
How does Rising Transfers find similar players?
Rising Transfers uses a proprietary 768-dimension Player DNA model trained on 3.2 million match events. Each player is represented as a vector across 35+ per-90 metrics including pressing intensity, passing footprint, dribbling profile, and defensive contribution. Similarity is measured using cosine distance — the same technique used in state-of-the-art AI systems — making it the most precise player comparison tool available publicly.