{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/151676"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/151676","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Measuring Grit in NFL Cornerbacks using Statistical Analysis","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Kingston, Cole"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Hosoi, Anette"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-06","date_published":"2023-06","updated_at":"2026-07-22T22:22:31Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/151676","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hosoi, Anette"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Measuring Grit in NFL Cornerbacks using Statistical Analysis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hosoi, Anette"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Kingston, Cole"],"dc:date.accessioned":["2023-07-31T19:58:13Z"],"dc:date.available":["2023-07-31T19:58:13Z"],"dc:date.issued":["2023-06"],"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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/151676"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Measuring Grit in NFL Cornerbacks using Statistical Analysis"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:22:31Z"}