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The Elo rating system is a highly successful ranking algorithm for games ofskill where, by construction, one team wins and the other loses. A primarylimitation of the original Elo algorithm is its inability to predictinformation beyond a match's win-loss probability. Specifically, the victor isawarded the same point bounty if he beats a team by 1 point or 10 points; onlythe rating difference between the team and its opponent affects the matchbounty. In this work, we explain that Elo ratings and predictions can benaturally extended to include margin-of-victory information by simplyredefining "what it means to win." We create ratings for each value of themargin-of-victory and use these ratings to predict the full distribution ofpoint spread outcomes for matches which have not yet been played.

Predicting outcomes for games of skill by redefining what it means to win