Player report
Rafael Yilmaz
MID · Kelsford · Demo Premier League · 23 years · France
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 threat73
- Chance creation41
- Finishing8
- Defending92
- Availability85
Per 90, percentile ranked
- Goals0.24
- Assists0.06
- Shots1.5
- Shots on target0.3
- Key passes0.8
- Expected goals0.31
- Expected assists0.06
- Tackles1.4
- Interceptions1.5
- Clearances2.6
Elite defending midfielder (92th percentile), with above average goal threat (73th). Finishing is the clear gap at the 8th percentile. Played 70% of available minutes.
Season
Key strengths
- Defending is a clear strength92th percentile among MIDs in this division
- Elite volume of clearances2.61 per 90, 98th percentile
Key risks and limitations
- Finishing is a clear weakness8th percentile among MIDs in this division
Valuation history
Comparable players
| Player | Similarity | Value | Age |
|---|---|---|---|
| Samir GallagherNorthfleet | 82% | €15.3m | 18 |
| Viktor FontaineOakhurst | 79% | €27.1m | 18 |
| Wilson ChevalierRookwood | 78% | €941k | 23 |
| Andre CastileBrackenford | 77% | €1.66m | 19 |
| Samir NakamuraBrackenford | 77% | €4.67m | 29 |
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
- 56%of 72% ceiling
- Sample
- 1517 min · 18 apps
- Last fixture
- 23 Jan
Percentiles are taken against MIDs 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.