{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/129909"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/129909","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Leveraging machine learning to predict playcalling tendencies in the NFL","abstract":"In this thesis, we apply four machine learning models to NFL play-by-play data from 2009-2018 to predict whether a team will run or pass the ball on a given play. We tested our models using league-wide and team-specific data in five different situations on the field. Our best league-wide models achieved a test accuracy of 80% and our best team-specific models achieved a test accuracy of 86%. Relative to the baseline of the run-to-pass ratio, the best league-wide models achieved an increase in accuracy of 25% and the best team-specific models achieved an increase of 27%. Our models showed that the Tennessee Titans, the New York Jets, and the Cincinnati Bengals have been the most predictable offenses in the NFL over 10 years. We found that a team's in-game run-to-pass ratio and their win and score probabilities are the driving factors for offensive play-calling. Additionally, our results show that teams are more predictable later in games, and that less predictable teams tend to experience greater success offensively.","abstract_html":"In this thesis, we apply four machine learning models to NFL play-by-play data from 2009-2018 to predict whether a team will run or pass the ball on a given play. We tested our models using league-wide and team-specific data in five different situations on the field. Our best league-wide models achieved a test accuracy of 80% and our best team-specific models achieved a test accuracy of 86%. Relative to the baseline of the run-to-pass ratio, the best league-wide models achieved an increase in accuracy of 25% and the best team-specific models achieved an increase of 27%. Our models showed that the Tennessee Titans, the New York Jets, and the Cincinnati Bengals have been the most predictable offenses in the NFL over 10 years. We found that a team's in-game run-to-pass ratio and their win and score probabilities are the driving factors for offensive play-calling. Additionally, our results show that teams are more predictable later in games, and that less predictable teams tend to experience greater success offensively.","abstract_has_math":false,"creators":["Goyal, Udgam."],"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":["John V. Guttag."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:02Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/129909","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["John V. Guttag."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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We tested our models using league-wide and team-specific data in five different situations on the field. Our best league-wide models achieved a test accuracy of 80% and our best team-specific models achieved a test accuracy of 86%. Relative to the baseline of the run-to-pass ratio, the best league-wide models achieved an increase in accuracy of 25% and the best team-specific models achieved an increase of 27%. Our models showed that the Tennessee Titans, the New York Jets, and the Cincinnati Bengals have been the most predictable offenses in the NFL over 10 years. We found that a team's in-game run-to-pass ratio and their win and score probabilities are the driving factors for offensive play-calling. Additionally, our results show that teams are more predictable later in games, and that less predictable teams tend to experience greater success offensively."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. 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