Mark Glickman, Senior Lecturer on Statistics, Department of Statistics, Harvard University

Title: Rating competitors in games with strength-dependent tie probabilities

Abstract: Competitor rating systems for head-to-head games are typically used to measure playing strength from game outcomes. Ratings computed from these systems are often used to select top competitors for elite events, for pairing players of similar strength in online gaming, and for players to track their own strength over time. Most implemented rating systems assume only win/loss outcomes, and treat occurrences of ties as the equivalent to half a win and half a loss. However, in games such as chess, the probability of a tie (draw) is demonstrably higher for stronger players than for weaker players, so that rating systems ignoring this aspect of game results may produce strength estimates that are unreliable. We develop a new rating system for head-to-head games that explicitly acknowledges a tie as a third outcome, and that the probability of a tie may depend on the strengths of the competitors. Our approach relies on time-varying game outcomes following a Bayesian dynamic modeling framework, and that posterior updates within a time period are approximated by one iteration of Newton-Raphson evaluated at the prior mean. The approach is demonstrated on a large dataset of chess games played in International Correspondence Chess Federation tournaments.

Mark Glickman is a Senior Lecturer on Statistics at Harvard University and a recognized leader in sports analytics and statistical ranking methodology. He is best known for developing the Glicko and Glicko-2 rating systems, which extend the Elo framework by explicitly modeling uncertainty in player strength and have been adopted in chess, esports, and other competitive domains. His research spans Bayesian inference, hierarchical modeling, paired-comparison models, and decision-making under uncertainty, with applications in sports, games, public policy, and the social sciences. Dr. Glickman has played a visible leadership role in the statistics and data science community through professional society service, conference organization, and scholarly leadership. Particularly relevant to CSAS, he has been one of the organizers of the New England Symposium on Statistics in Sports (NESSIS), a long-running and highly influential forum that brings together students, researchers, and practitioners working at the interface of statistics, data science, and sports analytics. His work is widely cited for its clarity, practical relevance, and principled statistical foundations, and it has shaped how modern performance evaluation problems are formulated and solved.

Dean Oliver, Sports Analytics Pioneer, Project Specialist, Sports Statistics ESPN

Title: Why division of (micro)credit helps in sports

Abstract: At ESPN, we have a wide receiver rating, a pass blocker rating, a quarterback rating, a net points rating for various basketball leagues. A player rating is useful because everyone talks about how good (or bad) players are --- fans, analysts, coaches, medical staff --- and just about everyone does it based on their eyes. Or, frankly, based on what the media feeds them. But I worked for NBA teams and you know what? Subjective opinions aren't very useful there. You gotta have something that does something that good scouts can't match --- you have to have something that sees all the plays. You gotta have something that not only says who is good, but why, when, and how. To do that needs metrics that work at a very granular level. And you know what you need to do that? A way to divide credit at a very fine level. I'll introduce some of the math and some of the intuition behind doing this the right way.

Dean Oliver revolutionized basketball analytics with his book, Basketball on Paper. He brought unique insight to the game through a clear statistical breakdown of how the game worked. That led him to work in both management and coaching staffs in the NBA, as well as to help found ESPN Analytics, where he is now. His work covers player value, game strategy, sports psychology, and the statistical tools to investigate all of them in ways no one else has. His follow-up book, Basketball beyond Paper, documents how basketball analytics is evolving in a more tech-driven industry, as well as the challenges he faced in growing it.

Panel Discussion: Building Careers in Sports Analytics

Abstract: This panel will provide an inside look at how three industry experts built successful careers in sports analytics, sharing key insights about their journeys, the tools they use, and how the field has evolved over time. Attendees will gain valuable advice on navigating the industry, staying ahead of the curve, and leveraging analytics tools for career growth.

Lauren Poe (moderator) is an engineer on ESPN’s industry-leading Sports Analytics team. She and the team create analytical storytelling tools, like the Football and Basketball Power Index ratings used across collegiate and professional sports, and develop products and experiences to support insightful and innovative storytelling. Before moving to Connecticut to work at ESPN in 2013, the Oklahoma native and University of Oklahoma mathematics graduate started her career by blending her love of sports and numbers as a high school math teacher and coach. While at OU, she represented on the sidelines with the pom squad allowing her to experience memorable sports moments from the sidelines. Lauren and her husband, John, live in Connecticut with their dog, Watson. The Poe-Parolin family loves to spend their time celebrating Boston and Oklahoma sports---most notable being OU Softball’s NCAA Championships!

Dean Oliver revolutionized basketball analytics with his book, Basketball on Paper. He brought unique insight to the game through a clear statistical breakdown of how the game worked. That led him to work in both management and coaching staffs in the NBA, as well as to help found ESPN Analytics, where he is now. His work covers player value, game strategy, sports psychology, and the statistical tools to investigate all of them in ways no one else has. His follow-up book, Basketball beyond Paper, documents how basketball analytics is evolving in a more tech-driven industry, as well as the challenges he faced in growing it.

Alok Pattani recently became a Product Manager at Google focused on sports in Search, following a 7-year stint as a Developer Advocate at Google Cloud. He has a passion for building sports products and tools, transforming data into valuable insights, and highlighting how data science and generative AI tools can empower practitioners to achieve greater impact. Alok is an expert in sports analytics, having been a founding member and leader of ESPN's sports analytics team before joining Google. He actively provides data science consulting services to teams and leagues, empowering them to make high-stakes decisions with relevant metrics and analysis

Nick Restifo is the Director of Basketball Research for the Atlanta Hawks. He is a generalist data scientist that is particularly interested in predictive modeling and general forecasting. He has a Master's degree in Data Mining and multiple years of NBA experience within both the front office and coaching sides of basketball operations groups. Nick is from New Haven, CT originally and graduated from UConn in 2011.