Dynamic quantification of player value for fantasy basketball

Abstract: Previous work on fantasy basketball quantifies player value for category leagues without taking draft circumstances into account. Quantifying value in this way is convenient, but inherently limited as a strategy, because it precludes the possibility of dynamic adaptation. This work introduces a framework for dynamic algorithms, dubbed "H-scoring", and describes an implementation of the framework for head-to-head formats, dubbed $H_0$. $H_0$ models many of the main aspects of category league strategy including category weighting, positional assignments, and format-specific objectives. Head-to-head simulations provide evidence that $H_0$ outperforms static ranking lists. Category-level results from the simulations reveal that one component of $H_0$'s strategy is punting a subset of categories, which it learns to do implicitly.