A recent trend among sports fans along both sides of the letterman jacket is that of Daily Fantasy Sports (DFS). The DFS industry has been under legal scrutiny recently, due to the view that daily sports data is too random to make its prediction skillful. Therefore, a common view is that it constitutes online gambling. This thesis proves that DFS, as it pertains to Baseball, is significantly more predictable than random chance, and thus does not constitute gambling.
We propose a system which generates daily lists of lineups for Fanduel Daily Fantasy Baseball contests. The system consists of two components: one for predicting player scores for every player on a given day, and one for generating lists of the best combinations of players (lineups) using the predicted player scores. The player score prediction component makes use of deep neural network models, including a Long Short-Term Memory recurrent neural network, to model daily player performance over the 2016 and 2017 MLB seasons. Our results indicate this to be a useful prediction tool, even when not paired with the lineup generation component of our system.
We build off of previous work to develop two models for lineup generation, one completely novel, dependent on a set of player predictions. Our evaluations show that these lineup generation models paired with player predictions are significantly better than random, and analysis shows insights into key aspects of the lineup generation process.
","abstract_html":"<p>A recent trend among sports fans along both sides of the letterman jacket is that of Daily Fantasy Sports (DFS). The DFS industry has been under legal scrutiny recently, due to the view that daily sports data is too random to make its prediction skillful. Therefore, a common view is that it constitutes online gambling. This thesis proves that DFS, as it pertains to Baseball, is significantly more predictable than random chance, and thus does not constitute gambling.</p> <p>We propose a system which generates daily lists of lineups for Fanduel Daily Fantasy Baseball contests. The system consists of two components: one for predicting player scores for every player on a given day, and one for generating lists of the best combinations of players (lineups) using the predicted player scores. The player score prediction component makes use of deep neural network models, including a Long Short-Term Memory recurrent neural network, to model daily player performance over the 2016 and 2017 MLB seasons. Our results indicate this to be a useful prediction tool, even when not paired with the lineup generation component of our system.</p> <p>We build off of previous work to develop two models for lineup generation, one completely novel, dependent on a set of player predictions. Our evaluations show that these lineup generation models paired with player predictions are significantly better than random, and analysis shows insights into key aspects of the lineup generation process.</p>","abstract_has_math":false,"creators":["Smith, Ryan"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Alex Dekhtyar","Computer Science","College of Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06-01T07:00:00Z","date_published":"2021-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:37Z","subjects":["Baseball Integer Programming Fanduel","Artificial Intelligence and Robotics","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2021.48"],"render_values":[{"text":"10.15368/theses.2021.48","href":"https://doi.org/10.15368/theses.2021.48","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2605","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alex Dekhtyar","Computer Science","College of Engineering"]},{"key":"dc:creator","label":"Author","values":["Smith, Ryan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2021-06-06T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Baseball Integer Programming Fanduel","Artificial Intelligence and Robotics","Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2605","10.15368/theses.2021.48"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A recent trend among sports fans along both sides of the letterman jacket is that of Daily Fantasy Sports (DFS). The DFS industry has been under legal scrutiny recently, due to the view that daily sports data is too random to make its prediction skillful. Therefore, a common view is that it constitutes online gambling. This thesis proves that DFS, as it pertains to Baseball, is significantly more predictable than random chance, and thus does not constitute gambling.
We propose a system which generates daily lists of lineups for Fanduel Daily Fantasy Baseball contests. The system consists of two components: one for predicting player scores for every player on a given day, and one for generating lists of the best combinations of players (lineups) using the predicted player scores. The player score prediction component makes use of deep neural network models, including a Long Short-Term Memory recurrent neural network, to model daily player performance over the 2016 and 2017 MLB seasons. Our results indicate this to be a useful prediction tool, even when not paired with the lineup generation component of our system.
We build off of previous work to develop two models for lineup generation, one completely novel, dependent on a set of player predictions. Our evaluations show that these lineup generation models paired with player predictions are significantly better than random, and analysis shows insights into key aspects of the lineup generation process.
"]},{"key":"dc:title","label":"Title","values":["Investigating Daily Fantasy Baseball: An Approach to Automated Lineup Generation"]}]}],"canonical_facts":{"dc:contributor":["Alex Dekhtyar","Computer Science","College of Engineering"],"dc:creator":["Smith, Ryan"],"dc:date.available":["2021-06-06T07:00:00Z"],"dc:description.abstract":["A recent trend among sports fans along both sides of the letterman jacket is that of Daily Fantasy Sports (DFS). The DFS industry has been under legal scrutiny recently, due to the view that daily sports data is too random to make its prediction skillful. Therefore, a common view is that it constitutes online gambling. This thesis proves that DFS, as it pertains to Baseball, is significantly more predictable than random chance, and thus does not constitute gambling.
We propose a system which generates daily lists of lineups for Fanduel Daily Fantasy Baseball contests. The system consists of two components: one for predicting player scores for every player on a given day, and one for generating lists of the best combinations of players (lineups) using the predicted player scores. The player score prediction component makes use of deep neural network models, including a Long Short-Term Memory recurrent neural network, to model daily player performance over the 2016 and 2017 MLB seasons. Our results indicate this to be a useful prediction tool, even when not paired with the lineup generation component of our system.
We build off of previous work to develop two models for lineup generation, one completely novel, dependent on a set of player predictions. Our evaluations show that these lineup generation models paired with player predictions are significantly better than random, and analysis shows insights into key aspects of the lineup generation process.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2605","10.15368/theses.2021.48"],"dc:subject":["Baseball Integer Programming Fanduel","Artificial Intelligence and Robotics","Data Science"],"dc:title":["Investigating Daily Fantasy Baseball: An Approach to Automated Lineup Generation"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:37Z"}