Mathematician and sports analytics expert Tim Chartier spoke to students and faculty at Stony Brook University as part of the Convergence Lecture Series, an event organized by the Institute for Creative Problem Solving (ICPS) in partnership with the National Museum of Mathematics (MoMath) and Brookhaven National Laboratory. The lecture, held on November 22, highlighted how mathematical thinking is increasingly influencing modern sports.
ICPS, which recently moved its tuition-free STEM program for gifted students in grades 5–10 from Brookhaven National Laboratory to Stony Brook University, serves participants from Suffolk, Nassau, Queens, and other areas. The program’s instructors come from across the region.
Earlier that day, Chartier led a workshop focused on discrete modeling for ICPS graduates enrolled in advanced courses. He stressed that approximation can be more important than exact answers—a theme he continued during his public talk.
Chartier has consulted 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.
During his presentation, Chartier shared stories about entering sports analytics when three Davidson College students asked if they could develop statistical models for their basketball team. He agreed before knowing what results would follow. “But by mid-season, the coaches were reliant on what would go on their desk after each game,” he said.
This initial project became CatStats, a student-run group providing analytics to multiple Davidson teams. Alumni have since worked with professional sports teams or won major analytics competitions.
Chartier told attendees that his main interest lies not only in athletics but also in their underlying stories: “I’m more interested in the human drama of it.”
He encouraged aspiring analysts to start working even without perfect datasets: “The best way to work in sports analytics is the Nike approach: just do it,” he said. According to Chartier, coaches value good questions over large datasets.
He described basic ways to collect sports data using tools like spreadsheets or Google Sheets’ IMPORTHTML function and noted that he and his students completed a scraping project for the U.S. Olympic & Paralympic Committee. “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 discussed how presenting findings requires understanding how coaches think: “People listen with the lens they have,” he said.
He recounted sitting courtside at Barclays Center during an Atlantic 10 basketball tournament while The New York Times observed him. A fan’s comment about game pace led him to quickly analyze statistics—an example of how analytics often starts with simple observations.
For some students, Chartier explained that analytics can open doors into both sports and mathematics itself. He described how one friend who previously felt disconnected from math now engages his children through bracket-building sessions 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 paths and technical methods such as scraping data online. Chartier encouraged experimentation and further contact: “If I don’t reply,” he said, “email me again.”
