Dynamic quantification of player value for fantasy basketball

TL;DR

This paper introduces a dynamic framework called H-scoring for evaluating player value in fantasy basketball, allowing adaptation to draft circumstances and outperforming static rankings in head-to-head formats.

Contribution

The work presents a novel dynamic algorithm framework for fantasy basketball valuation, specifically implementing H_0 for head-to-head leagues that considers draft context and strategic category punting.

Findings

H_0 outperforms static rankings in simulations.

H_0 learns to implicitly punt categories.

The framework models draft circumstances and strategic objectives.

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.