Research Papers

David Turkington, Megan Czasonis, Mark Kritzman, Cel Kulasekaran

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We introduce a new mathematical system for predicting outcomes of NBA draft prospects based on the outcomes of other previously drafted players. This approach, which is completely general and applicable to any sport, forms predictions as relevance-weighted averages of prior outcomes using a precise and theoretically justified assessment of relevance derived from principles of information theory. Crucially, a measure called “fit” indicates in advance the unique reliability of each individual prediction and dynamically focuses each prediction on the combinations of predictive variables and previous players that are most informative for the prediction task. Relevance-based prediction addresses complexities that are beyond the capacity of conventional prediction models, but in a way that is more transparent, more flexible, and more theoretically justified than widely used machine learning algorithms.

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