Ariel D. Amaral

I’m a PhD candidate in the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. My research characterizes large-scale extragalactic magnetic fields, from the circumgalactic to the intergalactic medium, using broadband radio polarimetry and Bayesian inference in Python. Since magnetic fields aren’t directly visible, the work is fundamentally about extracting faint, indirect signals from large and noisy datasets. This research has been published across six peer-reviewed papers and cited over 100 times.

I’ve submitted my thesis (June 9, 2026), and after I defend (scheduled for August 2026), I’m transitioning into sports data science with a focus on hockey analytics. I’m particularly interested in player evaluation, performance modelling, and data-driven strategy. The pivot is less of a leap than it sounds: the quantitative work I do every day (Bayesian modelling, signal decomposition, validation testing, communicating uncertainty) is the same toolkit that drives modern sports analytics.

I’ve been a sports fan my whole life with hockey being my absolute favourite, but I follow most other major sports too, particularly anything Toronto-based. The data side of sports is more recent for me, and it’s quickly become the work I want to do next. I’m currently building a hockey analytics portfolio to that end.

This site is where I’m collecting my research, projects, and writing as I work through this transition. You’ll find a mix of academic publications, sports analytics work as I build it out, and other things I’m interested in along the way. Always happy to hear from people working at the intersection of sports and data.