Player report
Diogo Kovac
FWD · Brackenford · Demo Premier League · 22 years · Germany
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 threat39
- Chance creation21
- Finishing28
- Defending77
- Availability79
Per 90, percentile ranked
- Goals0.17
- Assists0.06
- Shots2.6
- Shots on target0.6
- Key passes1.3
- Expected goals0.18
- Expected assists0.07
- Tackles0.6
- Interceptions0.6
- Clearances0.7
Strong defending forward (77th percentile). Chance creation is the clear gap at the 21th percentile. Played 72% of available minutes.
Season
Key strengths
- Elite volume of yellow cards0.00 per 90, 90th 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 |
|---|---|---|---|
| Diogo KovacGranthorpe | 92% | €23.8m | 27 |
| Mateo VasquezAshcombe | 86% | €4.95m | 27 |
| Noah ToureenMarsden Cross | 85% | €50.1m | 24 |
| Wilson ArdalDeepdale Vale | 83% | €4.98m | 32 |
| Hugo VasquezKelsford | 82% | €35.5m | 25 |
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
- 72%of 72% ceiling
- Sample
- 1550 min · 21 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.