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