Quantifying Skill and Chance: A Unified Framework for the Geometry of Games

TL;DR

This paper presents a unified quantitative framework to distinguish skill and chance in games, using a Skill-Luck Index and volatility measure, applicable to various stochastic decision systems for better game analysis and design.

Contribution

It introduces the Skill-Luck Index and volatility metrics, providing a novel, unified approach to quantify skill and chance across different games and decision systems.

Findings

Pure chance games have S = -1

Pure skill games have S = +1

Poker exhibits moderate skill dominance

Abstract

We introduce a quantitative framework for separating skill and chance in games by modeling them as complementary sources of control over stochastic decision trees. We define the Skill-Luck Index S(G) in [-1, 1] by decomposing game outcomes into skill leverage K and luck leverage L. Applying this to 30 games reveals a continuum from pure chance (coin toss, S = -1) through mixed domains such as backgammon (S = 0, Sigma = 1.20) to pure skill (chess, S = +1, Sigma = 0). Poker exhibits moderate skill dominance (S = 0.33) with K = 0.40 +/- 0.03 and Sigma = 0.80. We further introduce volatility Sigma to quantify outcome uncertainty over successive turns. The framework extends to general stochastic decision systems, enabling principled comparisons of player influence, game balance, and predictive stability, with applications to game design, AI evaluation, and risk assessment.