Comparing baseball players across eras via novel Full House Modeling
Shen Yan, Adrian Burgos, Jr., Christopher Kinson, Daniel J. Eck
The Annals of Applied Statistics · 2025 · Volume 19 · Issue 2
Abstract
A new methodological framework suitable for era-adjusting baseball statistics is developed in this article. Within this methodological framework specific models are motivated. We call these models Full House Models. Full House Models work by balancing the achievements of Major League Baseball (MLB) players within a given season and the size of the MLB talent pool from which a player came. We demonstrate the utility of Full House Models in an application of comparing baseball players’ performance statistics across eras. Our results reveal a new ranking of baseball’s greatest players which include several modern players among the top all-time players. Modern players are elevated by Full House Modeling because they come from a larger talent pool. We present sensitivity and multiverse analyses to examine how changes in modeling inputs, including the estimate of the talent pool, affect the results.
Keywords
era-adjusted baseball statistics, nonparametric estimation, order statistics, tail-probability modeling
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