Team Members: Bhagirath Bhardwaj, Jay Chaudhary, Xinyi (Esme) Li, Raj Patel
As students in the Boston University, MBSA'23 program, our team worked with JockMKT for their capstone program. Jock MKT is an online platform that allows users to purchase and trade shares of their favorite professional athletes. It transforms the sports market into a dynamic stock market, allowing users to make profits based on the performance of these athletes. Jock MKT has recently incorporated a new game mode, “Pick’em”. In Pick’em”, users can simply pick the over and under on certain player stats or prop picks. For the midterm milestone of this project, our goal is to identify and suggest a suitable test that can assist Jock MKT in increasing user engagement and maintaining a high user retention rate on this new game mode. To achieve this, we will carefully analyze data from the previous tests that have been conducted, along with two amplitude datasets. By doing so, we hope to obtain valuable insights that will enable Jock MKT to implement targeted strategies, ultimately optimizing the user experience and driving a higher user retention rate.
Python (Google Colab), Langchain, other open-source LLMs, and Jira Work Management.