expected-value-and-kellylisted
Install: claude install-skill deciqAI/knowledge-skills
# Expected Value and the Kelly Criterion
## Overview
Two questions decide most repeated bets: **is this bet good?** (EV) and **how big?** (Kelly). Most professional ruin comes from positive-EV bets sized wrong. **EV = p · W − q · L.** If EV ≤ 0, do not bet. **Kelly f\* = (bp − q) / b** maximizes long-term geometric growth (Kelly, Bell Labs, 1956). Full Kelly requires casino-grade certainty; default to half- or quarter-Kelly for estimated edges.
Neighbors: [first-principles](../first-principles/SKILL.md) · [occams-razor](../occams-razor/SKILL.md) · [second-order-thinking](../second-order-thinking/SKILL.md) · [inversion](../inversion/SKILL.md) · [regret-minimization](../regret-minimization/SKILL.md) (for non-repeating life decisions).
## When to Use
- Decision **repeats many times** — capital allocation, position sizing, VC portfolio, ad spend, A/B test budget
- *How big to bet* matters as much as *whether to bet*; you have a **measurable or estimable edge**
- Someone says: "expected value," "EV," "Kelly," "optimal bet size," "how much should we put on this?"
**When NOT to use:** one-shot life decisions → [regret-minimization](../regret-minimization/SKILL.md); negative-EV bets (don't bet); unestimable probabilities; correlated bets without portfolio adjustment.
## Coaching Novices (Adaptive Front Door)
**Engine mode:** user has a concrete repeated bet → run The Process directly. **Coach mode:** user is unfamiliar → guide step by step.
In Coach mode, respond one step at