Comparing baseball players across eras via novel Full House Modeling

Autor:	Yan, Shen, Burgos Jr., Adrian, Kinson, Christopher, Eck, Daniel J.
Rok vydání:	2022
Předmět:	Statistics - Applications Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Sensitivity and multiverse analyses which investigate the how results change with changes to modeling inputs including the estimate of the talent pool are presented. Comment: Results and additional supplements can be accessed on our website: https://eckeraadjustment.web.illinois.edu/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.11332 Zobrazit plný text záznamu View this record from Arxiv