Some graduates of the M.Sc. program in Statistics have gone on to work in the cutting-edge field of sports analytics. Read their stories to find out how they got into this field and where they are today! 

Sarah Bailey

I started my post-high school academic career at Jacksonville University in Jacksonville, Florida. I chose the United States because I wanted to continue my track career at an NCAA Division I school. I began as a biology major but, after a year and a half, realized my passion was in Math and switched my major. I transferred and finished my undergraduate degree in mathematics at University of the Pacific. While at Pacific, I began to explore ways to combine my love of sports and math and worked with a professor on a published paper titled "A Comparison of Academic and Athletic Performance in the NCAA".

After graduating, I took an internship with the (then) San Diego Chargers doing digital media analytics. While there, I was able to better focus on my career path and ultimately decided to return to school for my Master's in Statistics at at Simon Fraser University. At SFU, I took every opportunity that I could to expand my sports statistics research, including participating in the NBA hackathon, being part of the winning team in the Hockeygraphs Data Sprint Hackathon, and working with my advisor Tim Swartz on baseball research for my M.Sc. project.

While finishing my Master's, I was hired for my now current job with the Los Angeles Rams. I was initially hired as a Sports Performance Analyst to work primarily with the athletic trainers and strength staff to analyze workload and optimize performance. However, over the years, my role has expanded, and I am now working with all data and departments on the football side. Most of my work consists of analyzing data from the personnel and medical staff. Personnel work involves assisting the scouting (both college and pro) department to identify key traits in players, project players into the NFL, and evaluate our own scouting system to see which measured variables are most important. My work on the performance side looks at the various measurements we collect to try to optimize players' gameday performance and minimize the probability of preventable injury. I also do some coaching work where I assist in the development of self-scouting reports to plan for future games and analyze prior games. 

Throughout my time at the Rams, I have grown my skills and role. I have had various opportunities to speak about my experience and to help people trying to break into this world. I am very fortunate to work for an organization that pushes the limits with data and continually looks for ways to advance on all fronts. Sports analytics is an amazing, unique opportunity for people to get into sports. It is always evolving, and I can’t wait to witness and be a part of the growth that is to come.

To learn more about Sarah and her team, watch this video and listen to this podcast.

Follow Sarah on Twitter at @sarahrunbailey

Dani Chu

I was at SFU studying Math and Education so that I could become a high school math teacher and basketball coach when I started reading articles about basketball analytics by Zach Lowe from ESPN. In September of 2014, I came across a job posting for a position as a basketball operations analyst for the Philadelphia 76ers. Unfortunately, they were asking for technical skills that were completely foreign to me. So, I wrote down all the skills they were asking for and searched for classes being offered at SFU that covered those concepts. I walked into Luke Bornn’s linear models class in the fall of 2015 not because of his outstanding work in sports analytics but because I wanted to check a skill off the list from the 76ers job posting.

When I learnt about Luke’s work, I couldn’t have imagined that SFU had other professors doing sports analytics research. But I learnt quickly that SFU has professors such as Tim Swartz, Dave Clarke, Peter Chow-White, and Peter Tingling doing work across different disciplines with a focus on sports analytics.

Through Luke and Tim, I became part of the SFU Sports Analytics Club and became co-president with statistics students Lucas Wu and Abe Adeeb in the Fall of 2016. Together we organized the first Vancouver Sports Analytics Symposium and Hackathon in the summer of 2017. This event, which we hosted again in 2018 (and intended to host in 2020 but postponed due to COVID-19), helps provide data and mentorship to students who want to learn about sports analytics. That summer, Lucas and I got to work on our first project for a sports organization. We worked not on a statistical analysis but on a tool to help the performance analyst at Canada Basketball download data more efficiently while at the U19 FIBA World Cup in Egypt. Happily, Canada won the gold medal for the first time in an international FIBA tournament. We can take no credit for the win (most of it had to do with having phenom R.J. Barrett on the roster) but participating was fun.)

During the next year, I competed in analytics competitions hosted by Hockey Graphs, Sacramento Kings, Fraser Health, and SFU BADM with SFU students, Lucas Wu, Matthew Reyers, Sarah Bailey, Sophia He, Forrest Paton, Kristen Bystrom, Nikola Surjanovic, and Conor Doyle as fantastic teammates.

As I learnt more statistical and technical skills, I began doing formal research. In the summer of 2017 I began NSERC-sponsored research with Lucas Wu under Tim Swartz’s supervision. We worked on a paper on the modified Kelly criteria. This was an extension of the Kelly criteria that was developed in 1956 for making optimal bets. This work can help bettors choose an appropriate wager size for each of their bets. I also worked with Dave Clarke and Eli Mizelman on a project for the Candian Women’s National Soccer Team and Canada Rugby 7s. I took a sports analytics directed studies class hosted by Jack Davis and Ming-Chang Tsai and worked on a project about World Championship 2000m rowing with fellow student Ryan Sheehan. We entered the projects into research competitions at the Joint Statistical Meeting, Carnegie Mellon Sports Analytics Conference, and a poster competition hosted by the SFU Undergraduate Research Journal and were very successful.

I was having so much fun doing research that I decided to continue learning and researching as a Master’s student in statistics at SFU under the supervision of Tim Swartz. I worked on a project about foul accumulation in the NBA. With Denis Beausoleil, Aaron Danielson, Lucas Wu, and Kevin Floyd, we consulted with Coach Steve Hanson and the SFU Men’s Basketball team. During my Master’s, I entered the NFL Big Data Bowl with fellow SFU graduate students Lucas Wu, Matthew Reyers, and James Thomson. We were lucky enough to be selected as finalists for the college division and to travel to the NFL Combine in Indianapolis to present our work to NFL teams. We continued the work from the competition that summer as consultants for the NFL. That summer I was also lucky enough to live in New York City and work as a graduate intern for the NBA in their Basketball Strategy and Analytics Department. I got to work on interesting problems in sports betting, referee analytics and game scheduling. I even got to attend the NBA Draft!

In January of 2020, I defended my Master's thesis and started working at the new NHL Seattle franchise as a Quantitative Analyst. Our team is focused on player evaluation at the professional and amateur level, game strategy, and expansion draft strategy. I’m putting my education to use building statistical models to help inform our decision making.

Follow Dani on Twitter at @chuurveg

Matthew Reyers

The distinguishing factor in my education was the hands-on experience. I did an undergraduate in Operations Research, which is a blend of Computer Science, Mathematics, and Statistics. Although the program is quite comprehensive with capstones and course projects, the real fun came through hackathons. I competed (poorly) at a few local hackathons. Regardless of the outcome, I found the connection between my skills and the projects I cared about. I even used my work from a hackathon as the basis for my Operations Research capstone project. Through hackathons and similar competitions, I found my passion for sports analytics. 

I went on to do my Masters in Statistics when I got to the end of my undergraduate and felt incomplete. I didn't have the pieces to complete the puzzle. The Masters, for me, put it all together. The course work, the cohort, and the faculty consistently improved my understanding of the world around me. I also learned the value of engagement. There is a tangible benefit to competing in hackathons and other miscellaneous projects, one I might argue is a bigger benefit than the education itself! The courses I completed came across as afterthoughts to employers during interviews. On the other hand, the hackathons and other projects I completed had employers calling me. The best example was the NFL Big Data Bowl; my team (consisting of Lucas Wu, Dani Chu, James Thomson, and myself) was invited to present in Indianapolis to a field of NFL Executives. Talk about a hiring fair! Win or lose, NFL executives were contacting all the presenters in the weeks after the presentations to try to attract the top talent to their team. I didn't get nearly the same engagement when I added another semester of course work to my LinkedIn page. 

I am currently finishing my Masters’ project while working remotely for Zelus Analytics. Based out of Austin, Texas, Zelus Analytics is a baseball analytics start-up focusing on building champions. The company is built around statisticians and leverages their excellence to build better models than what is currently available, providing client teams with a competitive edge.  As a statistician/data scientist, I am tasked with all the elements of development, ranging from model building to model deployment. I find myself constantly challenged and engaged by both my work and my coworkers, closely mimicking my experience during my Masters at SFU. The most interesting aspect of my work so far has been the exposure to the full life cycle of projects, as my education covered only the first step (model building). Fortunately, hackathons and side projects covered the rest.

Follow Matthew on Twitter at @Stats_By_Matt