Brendan Kumagai

Brendan Kumagai

Data Science Intern

Zelus Analytics


I am currently a Data Science Intern 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 attend Simon Fraser University as a M.Sc. in Statistics student under the supervision of Dr. Tim Swartz where I am currently doing research at the intersection of data science and sports.

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.


  • Sports Analytics and Operations
  • Machine Learning
  • Spatiotemporal Statistics
  • Bayesian Statistics


  • B.Sc. in Mathematics and Statistics, Sept 2016-Apr 2020

    McMaster University

  • M.Sc. in Statistics, Sept 2021 - Present

    Simon Fraser University

Projects & Talks

BDB '22: Punt Returns: Using the Math to Find the Path

My group’s Grand Champion submission for the 2022 NFL Big Data Bowl along with Ryker Moreau, Elijah Cavan, and Robyn Ritchie. Our work involved designing and implementing an algorithm to find the ball carrier’s optimal path on punt return plays.

SFUSASS '22: Bayesian Space-Time Models for Expected Possession Added Value (Deep Dive)

Tyrel Stokes and I present the deeper statistical details on our 1st place project from the 2021 Big Data Cup with Thibaud Châtel and Mikael Nahabedian.

WHKYHAC '21: The Shot Chain

Joint project with Tyrel Stokes, Mikael Nahabedian, and Thibaud Châtel. 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.

OTTHAC '21: Bayesian Space-Time Models for Expected Possession Added Value

My group’s 1st-place submission for the Stathletes’ Big Data Cup 2021 along with Tyrel Stokes, Mikael Nahabedian, and Thibaud Châtel. 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.

CMSAC '20: Clustering and Analyzing 5v5 NHL Shot Location Data

My 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.

UCSAS '20: Point Trend Values II

My poster presenting my updated work on Point Trend Values at the U-Conn Sports Analytics Symposium poster competition. This content is very similar to my presentation at ISOLHAC but with sounder methodology in the calculation of the metric and shared code and data. I finished the competition in a tie for 1st place with Nate Rowan.

SMWW: Getting Started in Hockey Analytics

I presented my thoughts and tips on how to get started in hockey analytics to a group of students in the Sports Management Worldwide Hockey Analytics course.

ISOLHAC '20: Point Trend Values I

In this presentation 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.