Player report
Omar Yilmaz
FWD · Fallowfield · Demo Premier League · 27 years · Nigeria
Valuation
Values are model estimates on an uncalibrated scale. They are not predictions of a transfer fee and have not been validated against observed fees. Model 0.3.0-uncalibrated.
Performance profile
Player DNA
- Goal threat87
- Chance creation74
- Finishing51
- Defending62
- Availability49
Per 90, percentile ranked
- Goals0.41
- Assists0.25
- Shots2.8
- Shots on target1.2
- Key passes1.4
- Expected goals0.39
- Expected assists0.21
- Tackles0.7
- Interceptions0.2
- Clearances0.8
Strong goal threat forward (87th percentile), with above average chance creation (74th). Played 50% of available minutes.
Season
Key strengths
- Goal threat is a clear strength87th percentile among FWDs in this division
- Elite volume of shots on target1.16 per 90, 91th percentile
Key risks and limitations
- Low model confidence43% against a ceiling of 72%. The valuation range is correspondingly wide.
Valuation history
Comparable players
| Player | Similarity | Value | Age |
|---|---|---|---|
| Diogo WhitlockLangmere | 83% | €179k | 32 |
| Adam XimenesBrackenford | 79% | €42.2m | 25 |
| Goran OkaforIlkeston Park | 79% | €48.0m | 23 |
| Diogo BarrosRookwood | 79% | €5.94m | 29 |
| Adam ZielinskiEastmarsh | 78% | €24.1m | 31 |
Comparable transfers
Not available. The feed carries no observed transfer fees and no contract expiry dates, so there is nothing to build a comparable-fee distribution from.
This section is left in rather than removed, because its absence is itself a finding: any fee inference in this report would be unsupported.
Data confidence
- Model confidence
- 43%of 72% ceiling
- Sample
- 1086 min · 13 apps
- Last fixture
- 23 Jan
Percentiles are taken against FWDs in the Demo Premier League with 270 or more minutes. The model holds no contract, injury, wage or transfer data, and no progressive passing, carrying or pressing metrics.
Methodology
Weekly output is weighted by position, discounted by minutes as a credibility multiplier, and compared against an exponentially weighted average of the player’s own previous weeks — so the model moves on surprise rather than output. That becomes a percentile within the position group, then adjusted for age, league strength, position and availability.
An estimated value is not a transfer fee. Full methodology.