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DEFDemo Premier LeagueSenegal18 years

Samir Ugarte

Pendlebury · #9 · UGA5

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Estimated market value

€1.02m

Model range
€481k – €1.55m
Confidence
37%
Season change
−21.5%

Movement

Last matchweek
−1.5%
Last 4 matchweeks
−0.2%
Season to date
−21.5%

Reported in matchweeks, not days — football data arrives by fixture round.

Model estimate, uncalibrated. 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.

Valuation drivers

What moves this away from the €4.00m base value

  • Performance−€2.28m
  • Age+€763k
  • League€0
  • Position−€182k
  • Availability−€1.29m

Not in this model

  • ContractNo contract expiry data in the feed
  • Market comparablesNo observed transfer fees in the feed

Availability

Read any valuation against this first

Available minutes played14% · 306 of 2160

5 appearances, 3 starts, 19 matchweeks missed.

A player who is not on the pitch drifts down the index without his performance level falling with it, and availability is a direct discount on the valuation above. An unavailable player is not the same thing as a cheap one.

Scenarios

What the model would say under a different set of facts

  • As measured€1.02mbenchmark
  • Performance +10%€1.12m+€102k · +10.0%
  • Performance +20%€1.22m+€203k · +20.0%
  • Performance −10%€914k−€102k · −10.0%
  • One year older€1.04m+€29k · +2.8%
  • Fully available€1.83m+€818k · +80.5%
  • Moves to a stronger divisionAlready in the strongest division we rate

These are counterfactuals, not forecasts. Each row is the same model run with one input changed — it says nothing about whether that change is likely. The benchmark is this player’s measured estimate of €1.02m.

Player DNA

Percentile against DEFs in the Demo Premier League

  • Goal threat8
  • Chance creation4
  • Finishing46
  • Defending72
  • Availability3

Above average defending defender (72th percentile). Chance creation is the clear gap at the 4th percentile. Availability is the headline risk: 14% of available minutes played.

Generated from the metrics above, not from scouting notes. Ball progression and pressing are absent because the feed carries no progressive passes, carries or pressures.

Valuation history

The performance index the valuation is built on, by matchweek

Samir Ugarte index by matchweek

Profile

Per-90 percentiles against DEFs

Percentiles as a table
MetricSamir Ugarte
Tackles69th
Interceptions36th
Clearances92nd
Goals26th
Expected assists4th
Key passes19th

Per 90

Rate and percentile among DEFs with 270+ minutes

  • Goals0.00
  • Assists0.00
  • Shots0.0
  • Shots on target0.0
  • Key passes0.0
  • Expected goals0.00
  • Expected assists0.00
  • Tackles2.4
  • Interceptions1.5
  • Clearances3.2

Finishing

Goals against the quality of the chances they came from, running total

Samir Ugarte cumulative goals minus expected goals
−0.1−0.10.0+0.1+0.1Matchweek

Scoring 0.00 goals more than his chances were worth. Finishing runs like this historically revert.

Comparable players

Closest percentile profiles among the same position

Compare →
PlayerSimilarityValueAgeAlike on
Dmitri KovacStanmoor97%€4.61m20goals, assists
Bruno IvesEastmarsh88%€3.99m26goals, assists
Tomas XimenesCalderbrook86%€301k31goals, assists
Idris JansenFallowfield86%€2.61m23goals, assists
Omar ToureenMarsden Cross82%€2.13m35goals, assists
Idris IvesFallowfield81%€5.36m35goals, assists

Match log

5 appearances this season

Match-by-match statistics, most recent first
MWDateOpponentMinGAShSoTxGxAKPTklIntClr
212 Janat GranthorpeL 0190000.00.00.000.000.04.02.04.0
175 Decat RookwoodL 0590000.00.00.000.000.01.00.05.0
910 Octv BrackenfordL 2390000.00.00.000.000.03.03.02.0
512 Septv Marsden CrossL 017000.00.00.000.000.00.00.00.0
222 Augv GranthorpeW 2029000.00.00.000.000.00.00.00.0

Data

Last fixture
2 Jan
Matchweeks covered
24
Model
0.3.0-uncalibrated

Comparable transfers and contract situation are not shown, because the feed carries no transfer fees and no contract expiry dates. See methodology for what the model can and cannot see.