Player report
Goran Ardal
MID · Langmere · Demo Premier League · 21 years · Netherlands
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 threat98
- Chance creation24
- Finishing30
- Defending11
- Availability13
Per 90, percentile ranked
- Goals0.46
- Assists0.00
- Shots2.3
- Shots on target0.9
- Key passes0.7
- Expected goals0.47
- Expected assists0.03
- Tackles0.5
- Interceptions0.9
- Clearances1.6
Elite goal threat midfielder (98th percentile). Defending is the clear gap at the 11th percentile. Availability is the headline risk: 18% of available minutes played.
Season
Key strengths
- Goal threat is a clear strength98th percentile among MIDs in this division
- Elite volume of shots on target0.92 per 90, 98th percentile
Key risks and limitations
- Defending is a clear weakness11th percentile among MIDs in this division
- Availability is the headline risk18% of available minutes; 17 matchweeks missed
- Small sample393 minutes across 7 appearances. Percentiles are published but thinly evidenced.
Valuation history
Comparable players
| Player | Similarity | Value | Age |
|---|---|---|---|
| Wilson ZielinskiNorthfleet | 86% | €24.9m | 24 |
| Lucas OkaforBrackenford | 81% | €12.1m | 21 |
| Hugo EkstromIlkeston Park | 80% | €51.5m | 21 |
| Rafael EkstromGranthorpe | 78% | €274k | 32 |
| Pedro EriksenOakhurst | 77% | €10.8m | 28 |
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
- 48%of 72% ceiling
- Sample
- 393 min · 7 apps
- Last fixture
- 16 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.