Mathematician and sports analytics expert Tim Chartier spoke to students and faculty at Stony Brook University on November 22 as part of the Convergence Lecture Series. The event was organized by the Institute for Creative Problem Solving (ICPS) in collaboration with the National Museum of Mathematics (MoMath) and Brookhaven National Laboratory.
The ICPS program, now based at Stony Brook after relocating from Brookhaven National Laboratory, offers tuition-free STEM education to gifted students in grades 5 through 10 across Long Island. The program attracts participants from Suffolk, Nassau, Queens, and other areas.
Earlier in the day, Chartier led a workshop focused on discrete modeling for advanced ICPS students. He emphasized that approximation can be more valuable than exactness when working with data—an idea that carried over into his public lecture.
Chartier’s background includes consulting for organizations such as the NFL, NBA, NASCAR, ESPN, and The New York Times. MoMath CEO Cindy Lawrence introduced him by recalling his early involvement with the museum: “He immediately had ideas and joined our advisory council and literally helped us build the original MoMath,” she said. She also mentioned his diverse interests beyond mathematics.
During his talk, Chartier reflected on how he became involved in sports analytics after three Davidson College students approached him about building statistical models for their basketball team. “But by mid-season, the coaches were reliant on what would go on their desk after each game,” he said.
This initiative developed into CatStats, a student-run group that supports coaching strategies at Davidson College. Alumni have moved on to roles in professional sports and data analysis.
Chartier explained that his interest is not limited to athletics but extends to the narratives within them: “I’m more interested in the human drama of it.”
He encouraged students interested in analytics to begin right away without waiting for perfect datasets. “The best way to work in sports analytics is the Nike approach: just do it,” he said. According to Chartier, coaches and employers prioritize insightful questions over large datasets.
He demonstrated simple methods for collecting sports data using spreadsheets or Google Sheets’ IMPORTHTML function. He recounted how he and his students worked with the U.S. Olympic & Paralympic Committee on a web scraping project. “You can ask a lot of great analytics questions, and I believe you’re a sports analyst even if you just ask the question,” he said.
Chartier also discussed challenges related to communicating findings: “People listen with the lens they have,” he said. Understanding how coaches think is essential for effective analysis.
He described an experience at Barclays Center during an Atlantic 10 basketball tournament while being observed by The New York Times. A fan’s comment prompted him to analyze game pace—a reminder that meaningful analytics often start with simple observations.
For some attendees, sports analytics may provide a path into athletics; for others, it could spark new interest in mathematics itself. Chartier shared an example of a friend who began engaging with math through bracket-building activities inspired by Chartier’s models: “That was one of the first times he sat at the dinner table and ever talked math with his kids,” he said.
The event concluded with audience questions about career options and technical skills like scraping techniques. Chartier encouraged ongoing exploration: “If I don’t reply,” he said, “email me again.”