• Utilized a variety of prediction strategies to determine the number of yards a rusher gains in an NFL play based off initial play characteristics and player positions
• Main strategy built around native algorithms for Pursuit Vectors and realistic physics simulations in Python
• Merged original data from NFL with Madden skill data to enhance realism of simulations
• Combined simulation predictions with Machine Learning based methods such as KNN Regression to strengthen overall model
• Worked in tandem with fellow Senior Undergraduate Researcher and Machine Learning Professor