Player report
Caleb Jokinen
DEF · Kelsford · Demo Premier League · 26 years · Nigeria
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 threat43
- Chance creation25
- Finishing100
- Defending40
- Availability86
Per 90, percentile ranked
- Goals0.11
- Assists0.00
- Shots0.1
- Shots on target0.1
- Key passes0.0
- Expected goals0.07
- Expected assists0.01
- Tackles1.8
- Interceptions1.8
- Clearances2.5
Elite finishing defender (100th percentile). Chance creation is the clear gap at the 25th percentile. Played 76% of available minutes.
Season
Key strengths
- Finishing is a clear strength100th percentile among DEFs in this division
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 |
|---|---|---|---|
| Bertrand ZielinskiEastmarsh | 93% | €1.74m | 34 |
| Wilson XimenesAshcombe | 88% | €4.51m | 30 |
| Diogo DahlbergCalderbrook | 87% | €42.1m | 21 |
| Idris KovacQuarrydale | 82% | €10.2m | 24 |
| Kai GrimaldiFallowfield | 81% | €29.7m | 29 |
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
- 1634 min · 20 apps
- Last fixture
- 23 Jan
Percentiles are taken against DEFs 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.