Player report
Viktor Fontaine
MID · Oakhurst · Demo Premier League · 18 years · Senegal
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 threat65
- Chance creation16
- Finishing24
- Defending72
- Availability92
Per 90, percentile ranked
- Goals0.22
- Assists0.00
- Shots1.4
- Shots on target0.4
- Key passes0.7
- Expected goals0.24
- Expected assists0.02
- Tackles1.4
- Interceptions1.3
- Clearances1.9
Above average defending midfielder (72th percentile), with above average goal threat (65th). Chance creation is the clear gap at the 16th percentile. Played 76% of available minutes.
Season
Key strengths
- No standout axis in this dataNothing clears the 80th percentile among positional peers on the metrics we carry.
Key risks and limitations
- Chance creation is a clear weakness16th percentile among MIDs in this division
Valuation history
Comparable players
| Player | Similarity | Value | Age |
|---|---|---|---|
| Dmitri HaugenDeepdale Vale | 86% | €2.56m | 34 |
| Wilson ChevalierLangmere | 81% | €13.7m | 23 |
| Mateo LindqvistIlkeston Park | 80% | €20.7m | 22 |
| Rafael YilmazKelsford | 79% | €47.1m | 23 |
| Dmitri HaugenHollinwell | 77% | €680k | 21 |
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
- 69%of 72% ceiling
- Sample
- 1641 min · 20 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.