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MIDDemo Premier LeagueEngland32 years

Gabriel Ives

Oakhurst · #13 · IV13

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Estimated market value

€576k

Model range
€282k – €870k
Confidence
40%
Season change
−13.7%

Movement

Last matchweek
−1.5%
Last 4 matchweeks
−0.4%
Season to date
−13.7%

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−€1.09m
  • Age−€1.33m
  • League€0
  • Position+€69k
  • Availability−€1.07m

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 played13% · 286 of 2160

4 appearances, 3 starts, 20 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€576kbenchmark
  • Performance +10%€634k+€58k · +10.0%
  • Performance +20%€691k+€115k · +20.0%
  • Performance −10%€518k−€58k · −10.0%
  • One year older€441k−€135k · −23.4%
  • Fully available€1.05m+€477k · +82.8%
  • 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 €576k.

Player DNA

Percentile against MIDs in the Demo Premier League

  • Goal threat28
  • Chance creation96
  • Finishing46
  • Defending99
  • Availability1

Elite defending midfielder (99th percentile), with elite chance creation (96th). Goal threat is the clear gap at the 28th percentile. Availability is the headline risk: 13% 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

Gabriel Ives index by matchweek

Profile

Per-90 percentiles against MIDs

Percentiles as a table
MetricGabriel Ives
Key passes96th
Expected assists94th
Expected goals17th
Goals13th
Tackles97th
Interceptions39th

Per 90

Rate and percentile among MIDs with 270+ minutes

  • Goals0.00
  • Assists0.31
  • Shots1.9
  • Shots on target0.3
  • Key passes1.3
  • Expected goals0.02
  • Expected assists0.23
  • Tackles2.2
  • Interceptions0.9
  • Clearances2.8

Finishing

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

Gabriel Ives cumulative goals minus expected goals
−0.0−0.00.0Matchweek

Scoring 0.05 goals fewer than his chances were worth — the chances are being created.

Comparable players

Closest percentile profiles among the same position

Compare →
PlayerSimilarityValueAgeAlike on
Felix IbarraOakhurst78%€8.93m32assists, key passes
Noah CastileCalderbrook74%€1.09m34interceptions, shots
Caleb IbarraFallowfield73%€31.5m29shots on target, assists
Noah RasmussenHollinwell73%€13.6m29assists, expected assists
Emil QuintanaCalderbrook72%€19.7m30shots, expected assists
Jonas GallagherHollinwell72%€3.22m19interceptions, shots

Match log

4 appearances this season

Match-by-match statistics, most recent first
MWDateOpponentMinGAShSoTxGxAKPTklIntClr
229 Janv JarrowgateW 2090001.00.00.020.051.03.00.03.0
1414 Novat Marsden CrossD 1190002.00.00.010.011.02.01.02.0
137 Novat KelsfordD 1130001.00.00.010.010.00.01.01.0
1017 Octat EastmarshW 4076012.01.00.010.662.02.01.03.0

Data

Last fixture
9 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.