In this Major Qualifying Project, we worked alongside the online daily fantasy sports company DraftKings to build an algorithm that would predict which of the company’s contests would be profitable for them. Namely, our goal was to detect contests at risk of not filling to their maximum number of entrants by four hours before the contest closed. We combined categorical and numerical header data provided by DraftKings for hundreds of thousands of previous contests using modern data science techniques such as ensemble methods. We then utilized parameter estimation techniques such as linear regression and the Kalman Filter to model the time series data of entrants into a given contest. Finally, we fed these parameters and the predictions they generated, alongside the header data previously mentioned, into a Random Forest algorithm that provided our final prediction as to whether a contest would fill or not. The algorithm we developed outperformed previous methodologies involving only portions of the aforementioned data.