Quantifying Grit in MLB Batters

This thesis investigates the quantification of grit in Major League Baseball (MLB) batters, a crucial yet underexplored area in sports analytics traditionally gauged through qualitative assessment. Utilizing 2023 game data from the top 160 most utilized MLB batters, this study develops a Grit Score for each player based on the number of at-bats required to return to average performance after a period of below-average performance. At-bat performance is measured through Delta Runs Expected, and the at-bat group size of the window is selected by testing for correlation and consistency in player grit rankings. Results reveal significant variations in Grit Scores among batters; players identified as the most gritty generally correspond to those with top offensive performance, though grit and performance do not perfectly correlate. Furthermore, gritty batters tend to experience a higher number of hitting slumps but with shorter average lengths, regardless of the at-bat group size used to define the performance window. This research has implications in player valuation and development, team management, and scouting and drafting, suggesting that MLB teams should favor players who recover quickly from poor at-bats due to their more consistent performance and reliable offensive contributions to team success.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Yang, Angel

Advisor dc:contributor.advisor

• Hosoi, Anette

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156740

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156740