arXiv subjects

Zach Rosenof

Publications and source records attributed to Zach Rosenof.

3 recordsLinked to original sources

arXiv2025

Optimizing for Rotisserie Fantasy Basketball

Previous work on fantasy basketball has established methods for optimizing team construction for head-to-head formats. This has been facilitated by the straightforwardness of calculating the objective function for those formats, given that underlying performance distributions are known. Rotisserie has not been optimized in the same way because even with the assumption that performance distributions are known, directly calculating the most natural objective function is intractable. This work introduces a system for making a tractable approximation of that objective function. The resulting simplified objective function aligns well with the traditional wisdom that balanced teams are preferable for the format, because it contains an implicit mechanism that rewards teams for being balanced. Integrating this new objective function into established optimization methods is shown to perform well in the context of simulated seasons.

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arXiv2024

Dynamic quantification of player value for fantasy basketball

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.

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arXiv2024

Improving Algorithms for Fantasy Basketball

Fantasy basketball has a rich underlying mathematical structure which makes optimal drafting strategy unclear. A central issue for category leagues is how to aggregate a player's statistics from all categories into a single number representing general value. It is shown that under a simplified model of fantasy basketball, a novel metric dubbed the "G-score" is appropriate for this purpose. The traditional metric used by analysts, "Z-score", is a special case of the G-score under the condition that future player performances are known exactly. The distinction between Z-score and G-score is particularly meaningful for head-to-head formats, because there is a large degree of uncertainty in player performance from one week to another. Simulated fantasy basketball seasons with head-to-head scoring provide evidence that G-scores do in fact outperform Z-scores in that context.

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