Improving Algorithms for Fantasy Basketball

TL;DR

This paper introduces the G-score, a new metric for evaluating players in fantasy basketball, which outperforms the traditional Z-score especially in head-to-head formats with performance uncertainty.

Contribution

The paper proposes the G-score as a novel aggregation metric and demonstrates its superiority over Z-score through simulations in head-to-head fantasy basketball.

Findings

G-score outperforms Z-score in head-to-head formats

G-score accounts for performance uncertainty

Simulations validate the effectiveness of G-score

Abstract

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.