The Problem with Win Probability

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Abstract: In this paper, we highlight three current issues with win-probability models: i) lack of context, ii) no measure of prediction uncertainty, and iii) no publicly available datasets or models against which to compare. To remedy the last issue, we are releasing our NBA play-by-play dataset and base win-probability model to the research community (see https://www.stats.com/data-science/). To address the issue of context, we developed a neural network architecture which uses team rosters and game states to encode the on-court lineup for a given matchup. The addition of the lineup encoding allows for substantial improvements in model accuracy over existing methods (88% vs. 75%). To capture the uncertainty of our predictions, we moved from match-outcome prediction to final score difference prediction, providing a measure of uncertainty by estimating the likelihood of all possible scores. In addition to capturing the uncertainty of a given match outcome prediction, this approach allows for interactive story-telling applications by enabling the exploration of “alternative outcomes”- for example, what if Kawhi Leonard did not get injured during Game 1 of the Western Conference Finals. In the future, by complementing score prediction with a recurrent architecture, we should see the score distributions collapse, thereby allowing us to determine points of no return in a match, and determine what decisions are irreversible or lead to uncertainty growth in the inferred outcome prediction.

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