Professor Yanir Rubinstein's MATH 440: “Geometry and Data Science of Sports Analytics” trains students for real-world careers in professional sports.
Growing up, Jason Lott (B.S. ’25, computer science and mathematics) was always fascinated by sports. So, when he was scrolling through Testudo during his junior year at the University of Maryland to sign up for classes, he was immediately attracted to MATH 498R: “Experiential Learning: Mathematics of Sports Performance Analytics,” designed and taught by Mathematics Professor Yanir Rubinstein.
Lott needed a math elective to fulfill his major requirements, and with his lifelong passion for major league baseball, the class sounded like a perfect fit.
“It didn’t seem like the usual math class,” he recalled. “It was about sports analytics, and I’ve always been interested in athletes’ stats. I felt like I had to take it. And obviously, it’s worked out for me, considering where I ended up after graduation.”
Today, Lott works as a junior software engineer in the Baltimore Orioles' baseball analytics department. He helps maintain the systems that house the club's data and supports an internal website and mobile app used by coaches, scouts, analysts and players to improve their performance. Several types of data, from players’ batting averages to sprint speed, become the foundation for important decisions made by the team’s management, like training optimization or roster development.
“We store data about everything you could imagine—pretty much any stat you can think of surrounding the sport of baseball, including historical records going back many years,” Lott explained. “All those stats are used by analysts trying to gain any type of edge on the field or in the front office.”
Lott credits the experiential math course for helping him navigate his career path toward the Orioles and a professional sports job he always wanted. He said the class, which tied together his background in both computer science and math, allowed him to directly explore complex, real-life scenarios in sports—something that no other college course had ever done.
A math course unlike any other
First piloted in spring 2023, the UMD sports analytics course grew out of Rubinstein’s UMD Grand Challenges Award and his broader effort to connect students and the wider community to STEM through sports. After two successful pilot runs, the course was approved as a permanent offering for all undergraduate students. Beginning fall 2026, it will be offered as MATH 440: “Geometry and Data Science of Sports Analytics.”
For Rubinstein, the new class marks a milestone for the Department of Mathematics.
“We’ve built a data science curriculum that is from a mathematical perspective,” Rubinstein said. “With the training our students receive here, they are able to open more doors after they graduate.”
MATH 440 is structured less like a traditional math class and more like an industry internship. Instead of homework, quizzes and exams, students work in teams on semester-long research projects that simulate what it’s like to work for a professional sports team. Rather than being told which model or strategy to use, students experiment and figure out what works for themselves. By the end of the course, students learn how to wrangle real data and present findings in a clear, understandable way just like it’s done in a real analytics job.
In 2024, when Lott took the analytics class, the assigned sport was basketball. He and his partner were tasked with figuring out if machine learning could reverse-engineer the formulas behind popular basketball statistics.
“We had one simple stat where you can look up the formula, so we could compare our results against the real thing,” Lott explained. “The harder challenge was taking a black-box formula—a proprietary stat where the creator keeps the calculation secret—and trying to learn how it was calculated.”
The course also introduced students to using APIs (a programming tool that allows developers, researchers and fans to automatically extract and integrate NBA data). Lott and his partner learned to pull live data, including individual player statistics and historical team records, from the NBA's API for their project.
“Knowing how to extract data from an API and save it is a big part of my job now,” Lott said. “I use the skills I developed from the class at work every day, and I’ll probably continue to for years to come.”
For Rubinstein, the course is more than getting math students into the major leagues. Seeing students like Lott thrive in the sports industry reflects a bigger goal: feeding young people’s interest in math by showing them that it has applications they can use in the real world. Students from the course also present their projects in K-12 schools across the D.C., Maryland and Virginia, as part of Rubinstein’s Mobile Mathematics Motion Lab. Rubinstein’s ultimate goal is to combine teaching, research and community engagement in a way that changes perceptions of what mathematics—and a university mathematics department—can do.
“Oftentimes, students lose their interest or passion for math at young ages because they have misconceptions about what math is and where they can use it,” Rubinstein said. “What my team is doing isn’t really outreach or mathematics education in the usual sense. We’re teaching things that we’re not usually teaching because we want to connect mathematics to people’s real lives.”
