Daily fantasy sports (DFS) has grown in popularity with millions of participants throughout the world. However, studies have shown that most profits from DFS contests are won by only a small percentage of players. This thesis addresses the challenges faced by DFS participants by evaluating sources that provide player projections for NBA DFS contests and by developing machine learning models that produce competitive player projections.
External sources are evaluated by constructing daily lineups based on the projections offered and evaluating those lineups in the context of all potential lineups, as well as those submitted by participants in competitive FanDuel DFS tournaments. Lineups produced by the machine learning models are also evaluated in the same manner.
This work experiments with several machine learning techniques including automated machine learning and notes the top model developed was successful in 48% of all FanDuel NBA DFS tournaments and 51% of single-entry tournaments over a two-month period, surpassing the top external source evaluated by 9 percentage points and 10 percentage points, respectively.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3461 |
| Date | 01 June 2019 |
| Creators | Evangelista, Eric C |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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