Player report
Viktor Brennan
MID · Kelsford · Demo Premier League · 35 years · England
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 threat37
- Chance creation13
- Finishing2
- Defending32
- Availability70
Per 90, percentile ranked
- Goals0.14
- Assists0.00
- Shots1.2
- Shots on target0.2
- Key passes0.6
- Expected goals0.20
- Expected assists0.02
- Tackles1.3
- Interceptions0.8
- Clearances1.3
Well-rounded midfielder without a standout axis in this data — no category clears the 37th percentile among positional peers. Played 58% of available minutes.
Season
Key strengths
- Elite volume of yellow cards0.00 per 90, 91th percentile
Key risks and limitations
- Chance creation is a clear weakness13th percentile among MIDs in this division
- Finishing is a clear weakness2th percentile among MIDs in this division
Valuation history
Comparable players
| Player | Similarity | Value | Age |
|---|---|---|---|
| Viktor XimenesQuarrydale | 85% | €644k | 29 |
| Viktor NakamuraStanmoor | 83% | €31.9m | 22 |
| Wilson GrimaldiGranthorpe | 82% | €12.9m | 25 |
| Noah HaugenStanmoor | 80% | €4.11m | 25 |
| Samir BrennanQuarrydale | 79% | €610k | 32 |
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
- 1254 min · 16 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.