Player report
Viktor Fontaine
MID · Rookwood · Demo Premier League · 32 years · Denmark
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 threat84
- Chance creation63
- Finishing99
- Defending88
- Availability72
Per 90, percentile ranked
- Goals0.28
- Assists0.14
- Shots1.9
- Shots on target0.7
- Key passes0.8
- Expected goals0.19
- Expected assists0.12
- Tackles1.7
- Interceptions1.5
- Clearances2.0
Elite finishing midfielder (99th percentile), with strong defending (88th). Played 59% of available minutes.
Season
Key strengths
- Finishing is a clear strength99th percentile among MIDs in this division
- Defending is a clear strength88th percentile among MIDs in this division
- Goal threat is a clear strength84th percentile among MIDs in this division
- Elite volume of shots on target0.70 per 90, 95th 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 |
|---|---|---|---|
| Caleb LindqvistIlkeston Park | 90% | €5.01m | 30 |
| Andre EkstromLangmere | 88% | €1.25m | 26 |
| Cillian DahlbergThorneside | 83% | €1.53m | 30 |
| Pedro ZielinskiEastmarsh | 82% | €6.65m | 18 |
| Emil QuintanaCalderbrook | 81% | €19.7m | 30 |
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
- 50%of 72% ceiling
- Sample
- 1277 min · 17 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.