Award case lab
MVP Race
A refined four-layer race model: Basketball Value, Award Case modifiers, context/confidence signals, and structured analyst lenses in one transparent case workspace.
Refined Methodology
Each block below tells you what job a metric has. Core score inputs build the Basketball Value base; award modifiers shape the MVP ballot case; context signals explain the evidence; analyst lenses translate the data into basketball terms.
Basketball Value
Season-long on-court value from impact, efficiency, scoring load, playmaking load, team value, and availability.
Award Case
The main leaderboard rank. Starts with Basketball Value, then applies capped voter-facing modifiers.
Confidence
Coverage, sample stability, and signal agreement make uncertainty visible instead of hiding it.
Analyst Lenses
Role difficulty, scalability, game control, two-way pressure, and playoff translation standardize the qualitative layer.
MVP methodology reliability
methodology
Methodology
How the MVP case engine works
The v3 tracker separates season-long Basketball Value from voter-facing Award Case logic. The main rank is Award Case: Basketball Value plus capped modifiers for team framing, eligibility pressure, clutch, momentum, and signature games. Gravity, support burden, opponent splits, play-style translation, and coverage warnings remain labeled context signals unless their samples are stable enough to join a core pillar.
Box-First
Classic MVP index — production, efficiency, BPM/VORP/WS, team context.
Production 25% · Efficiency 20% · Impact 25% · Team 15% · Momentum 10% · Style 5%
Balanced
Default. Blends box totals with multi-metric impact consensus and clutch signal.
Prod 18 · Eff 15 · Box-Impact 15 · Impact Consensus 20 · Clutch 10 · Team 12 · Momentum 7 · Style 3
Impact-Consensus
Leans on EPM/LEBRON/RAPTOR/PIPM/DARKO consensus + clutch; tempers box-total reliance.
Impact Consensus 35 · Clutch 15 · Eff 15 · Team 15 · Prod 10 · Style 5 · Momentum 5
These legacy profiles are now sensitivity checks, not the main methodology. They help explain whether a candidate's case depends more on box totals, balanced weighting, or impact consensus.