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: Takefusa Kubo

From the model's €2.2M starting point to €15.6M, one input at a time, and where that lands against an observed €20M.

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

Observed €20MModel A €15.6M (served, in-sample)LaLiga · Right Winger

Takefusa

Kubo

RW

№ 405398Elite €10.1M +0.68

Chapter 01: The valuation

Takefusa Kubo: 25, right winger in LaLiga. Transfermarkt's valuation is €20M, in the Elite 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: League

League (LaLiga, Spain) raises the estimate ×1.83: from €2.2M to €4M.

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 less than usual.

Chapter 04: Appearances

Appearances (79) raises the estimate ×1.81: from €4M to €7.2M.

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 05: Age

Age (25.0 yrs) raises the estimate ×1.40: from €7.2M to €10.1M.

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 less than usual.

Chapter 06: Days since last match

Days since last match (22) raises the estimate ×1.15: from €10.1M to €11.6M.

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: Club points per game

Club points per game (1.21) lowers the estimate ×0.87: from €11.6M to €10.1M.

Across all modelled players this input moves Model A's estimate by ×1.38 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 ×1.54, to €15.6M.

Chapter 09: The estimate

After all 20 inputs, Model A says €15.6M. Transfermarkt says €20M. The residual, log(observed) − log(estimate), is +0.249: the observed value is above the estimate, so the model under-predicts by ×1.28.

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 €20M

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.