The World Handicap System describes each course with two parameters (Course Rating and Slope Rating) but each player with only one (Handicap Index). Because tournament outcomes depend on both scoring mean and dispersion, this asymmetry produces systematic unfairness in calibrated proof-of-concept simulations: the most-advantaged player type wins up to about 1.8 times its fair share of net competitions. A score-history normal version of the rule brings canonical-course win shares to roughly 0.83-1.12 of the equal-win benchmark. It uses two player-side quantities that are already latent in posted score differentials: a Player Rating for scoring level and a Player Volatility for round-to-round dispersion. Estimating volatility is noisier than estimating an average, but it is not a new data collection problem; the inputs are the same scoring histories already used to compute Handicap Index. A simpler affine shortcut, built from Player Rating and a pooled Player Slope, is algebraically analogous to the existing course-side formula but is much more calibration-sensitive (0.58-1.85 in the current simulator; external validation needed). We derive the normal benchmark and affine approximation from a mechanism-design formulation and prove an alignment theorem: under an affine mean-dispersion trace, the affine rule exactly aligns the mean-score and Player-Rating objectives and approximately aligns tournament-winning incentives when the quantile approximation is accurate.
Daniel A. B. Dias (Sun,) studied this question.
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