Using Cumulative Win Probabilities to Predict NCAA Basketball Performance
Traditional ranking methods such as Average Scoring Margin (ASM) or the Ratings Percentage Index (RPI) are limited in their accuracy and usefulness because they focus on just the final score of the game, not how the game arrived at the final score. In this paper I propose a new method that looks at cumulative win probabilities over the duration of a game to measure both team and an individual player’s performance. Using five years of game-play data to generate a Win Probability Index for NCAA basketball, I am able to create an open system that allows anyone to measure the impact, in terms of win probability added, of each play. This method is more accurate than either the ASM or RPI while also providing a more detailed level of player and play specific detail. My initial design includes input adjustments for conference play, home/away/neutral site games, and a team’s strength of schedule. Outputs of this study include player rankings, team rankings, and strength of schedule rankings. Detailed explanations of my methodology and the simulations used to build the model, a comparison to existing methods, and an exploration of futures uses of this data are included.
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