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Massachusetts Institute of Technology

Measuring Grit in NFL Cornerbacks using Statistical Analysis

Abstract

dc:description.abstract

Using the pass play tracking data from the 2018 National Football League (NFL) season, I compiled a Grit Score that measured cornerback responses to an adverse result to a play. I calculated this Grit Score using the results of whether a cornerback allowed their opposing receiver to catch the ball to measure change in performance. When comparing performance, I used the difference in average distance between the cornerback and opposing receiver to compile one score for each player in the NFL. I validated my calculations with Pro Football Focus Coverage Ratings and was able to classify players into 6 different categories based on talent and Grit Score. Overall, I found that most NFL players have high grit, or play consistently through adversity, which explains why they have made it to the highest level of football. NFL coaches and general managers prefer players who have increased performance following a bad event as those players tend to stay in the NFL for longer than those with decreased performance.

Degree

thesis:*
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
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Kingston, Cole
Advisor dc:contributor.advisor
  • Hosoi, Anette

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/151676
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/151676

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Kingston, Cole. Measuring Grit in NFL Cornerbacks using Statistical Analysis. Massachusetts Institute of Technology, 2023. https://hdl.handle.net/1721.1/151676