FairValue

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  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
  • EnvironmentTerminalExplore, compare and track players; market, league and model views.
  • EnvironmentStoriesValuations, head-to-heads and research findings, told as stories.
  • TerminalPlayer ExplorerSearch, filter and sort the player export.
  • TerminalComparison StudioTwo to four players on one scale.
  • TerminalWatchlistsNamed collections, kept in this browser.
  • TerminalMarket LandscapeHow observed value is distributed, and where the model drifts.

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Valuation story

Inside a valuation: Idrissa Gueye

From the model's €2.2M starting point to €796k, one input at a time, and where that lands against an observed €500k.

Built from Published SHAP output for the served Model A (reports/explainability); the player was in its training data.10 chapters

Observed €500kModel A €796k (served, in-sample)Premier League · Defensive Midfield

Portrait of Idrissa Gueye

Idrissa

Gueye

DM

№ 126665Emerging €1.2M −0.92

Image: FootyLogos · image credits

Chapter 01: The valuation

Idrissa Gueye: 36, defensive midfield in Premier League. Transfermarkt's valuation is €500k, in the Emerging tier. This story follows how Model A arrives at its own figure, one input at a time.

Chapter 02: Where every estimate starts

Before it looks at this player, Model A sits at €2.2M: its average output across the players it was trained on. Every player's journey starts here. Each input then multiplies the estimate up or down.

Chapter 03: Age

Age (36.7 yrs) lowers the estimate ×0.08: from €2.2M to €183k.

Across all modelled players this input moves Model A's estimate by ×1.60 on average, in one direction or the other. Here it moves it more than usual.

Chapter 04: League

League (Premier League, England) raises the estimate ×3.15: from €183k to €577k.

Across all modelled players this input moves Model A's estimate by ×1.96 on average, in one direction or the other. Here it moves it more than usual.

Chapter 05: Appearances

Appearances (65) raises the estimate ×1.46: from €577k to €841k.

Across all modelled players this input moves Model A's estimate by ×1.28 on average, in one direction or the other. Here it moves it more than usual.

Chapter 06: Days since last match

Days since last match (39) lowers the estimate ×0.85: from €841k to €718k.

Across all modelled players this input moves Model A's estimate by ×1.11 on average, in one direction or the other. Here it moves it more than usual.

Chapter 07: European-cup minute share

European-cup minute share (0%) lowers the estimate ×0.86: from €718k to €619k.

Across all modelled players this input moves Model A's estimate by ×1.20 on average, in one direction or the other. Here it moves it less than usual.

Chapter 08: Everything else

The remaining 15 inputs are individually small. Together they raise the estimate ×1.29, to €796k.

Chapter 09: The estimate

After all 20 inputs, Model A says €796k. Transfermarkt says €500k. The residual, log(observed) − log(estimate), is −0.464: the observed value is below the estimate, so the model over-predicts by ×1.59.

The served model was trained on this player, so this is an in-sample figure; on unseen players a typical miss is ×1.50.

Chapter 10: What this is not

SHAP describes the model's arithmetic for these inputs. It is not a price, a scouting judgement or a statement about what causes value, and the order of the steps is by size, not sequence.

The same record, with Model B and the player's traits, is in the dossier.

Starting point€2.2Mobserved €500k

Every Model A valuation starts at €2.2M, the model's average output over the players it was trained on (log 14.592).

raises the estimate lowers it Model A estimate observed value (dashed line)Log scale: equal lengths are equal multiplications. Inputs are ordered by size of effect, not in any causal sequence. Published SHAP run for the served Model A; this player was in its training data.