Data Science, Quantitative Research, and Soccer Analytics
Data science, quantitative research, and soccer analytics.
I’m interested in problems where understanding how the pieces of a system interact matters as much as producing an answer. That interest leads me to football research, public-interest data, and questions about people, incentives, and structures.
Independent football tracking-data research on localized defensive reorganization. Across three separate tracking environments, outward off-ball movement is associated with more subsequent local reorganization than comparable movement toward goal. An analyst demo makes the measurement and its limits concrete.
Experimental Python toolkit and football research for the PySport Analytics Cup. A protected receiver-ranking benchmark shows that transparent defensive geometry adds information beyond attacking geometry when ranking the provider-targeted receiver.
An early-stage API-to-database analytical project focused on Chicago transit reliability.
A U.S. Soccer Hackathon finalist project using Opta data, later presented by the team at the OptaPro Forum in Chicago.
M.S. Data Science candidate at the University of Colorado Boulder; B.A. in Statistics and Economics from the University of Wisconsin–Madison; Metis data science training.