Mike Lopez, associate editor of the Journal of Quantitative Analysis in Sports, is a senior director of football data and analytics at the National Football League and a former assistant Statistics professor at Skidmore College. In 2020, he was honored with the American Statistical Association’s Statistics in Sports Significant Contributor Award.
When were you first interested in statistics applications in sports?
Each time we used to stay at hotels as a kid, I would open USA Today and look at two things. First was the sports page, and second was the infographic they would always show in the corner on the front cover. So, sports and statistics have always been two of my passions—I just had to wait a little while until there were careers in the intersection of those fields.
What are your professional duties as a sports statistician?
Our job is to use data to enhance the game of football, and that covers anything that relates to the on-field tendencies of players, coaches, and teams. We have metrics relating to game excitement, fairness and equity, player health and safety, officiating, and pace of play. We also play a role in innovation regarding the future of the game.
What path did you take to become a sports statistician?
I never had the goal of being a sports statistician. Instead, once I realized my passion for analyzing data, my focus was more generally on building skills that could translate to careers in analyzing or teaching statistics. That path was a mathematics major at Bates College, high-school teacher (and assistant football coach!), six years of grad school at the University of Massachusetts-Amherst and Brown, and then four years at Skidmore College as an assistant professor. None of those roles were explicitly designed to end in a sports statistician role, but between the public speaking, subject-specific expertise in football, ability to teach and work with other researchers, technical/coding skills, and, most importantly, evolution of the sports world, the stars aligned.
Tell us about your typical day at work.
We have a football analytics team of seven folks right now, and so most of my time involves ensuring the short- and long-term success of that group. We are a mix of both junior analysts and successful data scientists covering a variety of disciplines within the game. I will also spend a good amount of time coding—working to build new metrics, improve old ones, or create reports or presentations based off our work. In season, we are responsible for reporting on the season as it progresses, and we work with the NFL’s Competition Committee each offseason when it comes to rules changes and ways to enhance the game.
What skills and academic training (e.g., college courses) are valuable to sports statisticians?
Here are a couple avenues for folks to think about:
Most statistics or data science courses will in some way aid a career in sports data. One of the first ways I got started in the field was by taking the methods I learned that week in grad school and seeing if there was a corresponding way to answer a sports question with them. Between the Poisson (goals and penalties) and binomial (win/loss, made/missed) distributions, hierarchical modeling (players as random intercepts), spatial statistics (heat maps), random forests (win probability models), and survival analysis (time until an injury), sports is an excellent sandbox to play in while improving your statistics acumen. Even better, most sports data sets are free and can be found online.
