Player report
Omar Whitlock
MID · Oakhurst · Demo Premier League · 31 years · Wales
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 threat48
- Chance creation92
- Finishing60
- Defending61
- Availability82
Per 90, percentile ranked
- Goals0.06
- Assists0.25
- Shots1.9
- Shots on target0.4
- Key passes1.3
- Expected goals0.06
- Expected assists0.19
- Tackles1.3
- Interceptions1.2
- Clearances1.4
Elite chance creation midfielder (92th percentile), with above average defending (61th). Played 67% of available minutes.
Season
Key strengths
- Chance creation is a clear strength92th percentile among MIDs in this division
- Elite volume of key passes1.30 per 90, 96th percentile
Key risks and limitations
- No statistical red flags in the data we holdThis is not the same as no risk: we hold no contract, injury, wage or transfer data, and nobody has watched the player.
Valuation history
Comparable players
| Player | Similarity | Value | Age |
|---|---|---|---|
| Viktor VasquezKelsford | 84% | €4.22m | 20 |
| Emil BrennanOakhurst | 84% | €5.24m | 24 |
| Emil QuintanaCalderbrook | 82% | €19.7m | 30 |
| Andre EkstromFallowfield | 82% | €13.1m | 29 |
| Noah CastileCalderbrook | 80% | €1.09m | 34 |
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
- 68%of 72% ceiling
- Sample
- 1457 min · 19 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.