Recreations in Randomness: From Glicko Rating to World Cup

This month’s episode of the Harvard Data Science Review Podcast explores the rapidly evolving world of sports analytics and how advances in data science are transforming the way we understand competition. We are joined by Harvard statistician Mark Glickman, creator of the Glicko rating system, and sports statistician Stephanie Kovalchik to discuss the technologies, models, and data driving modern sports. From real-time player tracking and probabilistic rating systems to AI-assisted coaching and predictive modeling, the conversation examines how statistical methods continue to shape decision-making on and off the field. Glickman and Kovalchik also explore why traditional statistical models remain central to sports analytics, how access to high-quality data continues to limit innovation, and what emerging AI tools may—and may not—bring to the future of the field. The episode concludes with a look at Recreations in Randomness, HDSR’s column on the many ways data science enriches our recreational lives, and an invitation for readers to contribute new perspectives on the growing role of data in sports, hobbies, and beyond. Our guests: Mark Glickman is a senior lecturer on statistics at Harvard University; a senior statistician at the Center for Healthcare Organization and Implementation Research (CHOIR), a Veterans Administration Center of Innovation; and co-editor of HDSR’s Recreations in Randomness column. Stephanie Kovalchik is a senior manager of data science at Teamworks, where she develops data-driven solutions to enhance athlete performance and decision-making. She is also co-editor of HDSR’s Recreations in Randomness column.