I am a Data Scientist on the Hockey Research & Development team at Zelus Analytics where we develop advanced statistical and machine learning models using cutting-edge data to improve our understanding of the game of hockey and provide clients with objective, data-informed insights. Additionally, I recently graduated from Simon Fraser University with a M.Sc. in Statistics student under the supervision of Dr. Tim Swartz where I did research at the intersection of data science and sports with a focus on the NHL draft.
Previously, I graduated with a B.Sc. in Mathematics and Statistics from McMaster University in April 2020 and completed Data Analyst and Data Scientist internships with the McMaster Visual Neuroscience Lab, the Canadian Tire Sports Analytics team and Stathletes.
My career objective is to apply data science methods to develop innovative and original research in a field that I am passionate about such as neuroscience, biomechanics, conservation and sustainability, and especially sports with hopes of becoming a leader at the intersection of data science and hockey in the future.
B.Sc. in Mathematics and Statistics, Sept 2016-Apr 2020
M.Sc. in Statistics, Sept 2021 - Dec 2022
Simon Fraser University
This project began as joint work with Ryker Moreau and Kimberly Kroetch and developed into my thesis project for my M.Sc. in Statistics under the supervision of Dr. Tim Swartz. We adapt the rank-ordered logit model - a sequence of conditional multinomial logit models - to create a novel way to forecast the outcome of the NHL draft using draft rankings from hockey experts and previous NHL draft results.
This project was joint work with Tyrel Stokes, Kimberly Kroetch, Gurashish Bagga, and Liam Welsh for the 2022 NYRA/NYTHA Big Data Derby competition. We leverage the horse racing tracking data provided in the competition to develop Bayesian B-spline regression models to forecast the probability that each horse will finish in each possible finishing placement. Our team was awarded 1st place out of 108 submissions.
This project was joint work along with Ryker Moreau, Elijah Cavan, and Robyn Ritchie for the 2022 NFL Big Data Bowl. Our work involved designing and implementing an algorithm to find the ball carrier’s optimal path on punt return plays. We were awarded 1st place out of 268 total submissions.
Joint project with Tyrel Stokes, Mikael Nahabedian, and Thibaud Châtel presented at WHKYHAC 2021. Our work here goes beyond the traditional expected goals model by building out a chain of Bayesian logistic models to analyze shooter talent and the context of shots in women’s hockey.
This project was joint work along with Tyrel Stokes, Mikael Nahabedian, and Thibaud Châtel for the 2021 Stathletes’ Big Data Cup. Our work involved quantifying the value of each event in offensive entry-to-exit sequences through the use of various Bayesian spatiotemporal models and a Markov Decision Process. Our team was awarded 1st place out of 71 submissions.
A poster presenting my work on clustering 5v5 NHL shot maps at the 2020 Carnegie Mellon Sports Analytics Conference poster competition. I find 10 clusters of players based off of their shooting patterns and analyzing the composition of these clusters.
In this project, I create a new metric called Point Trend Values to quantify trends in a player’s cumulative point production and explore the predictive power of this metric on OHL and WHL forwards. I was awarded a tie for 1st place at the 2020 U-Conn Sports Analytics Symposium (UCSAS) Poster Competition.