{"id":3276,"date":"2026-05-07T21:30:18","date_gmt":"2026-05-07T21:30:18","guid":{"rendered":"https:\/\/geiselmed.dartmouth.edu\/bmds\/?p=3276"},"modified":"2026-05-07T21:30:18","modified_gmt":"2026-05-07T21:30:18","slug":"seminar-with-mark-glickman-phd","status":"publish","type":"post","link":"https:\/\/geiselmed.dartmouth.edu\/bmds\/2026\/05\/07\/seminar-with-mark-glickman-phd\/","title":{"rendered":"Seminar with Mark Glickman, PhD"},"content":{"rendered":"

The Department of Biomedical Data Science invites you to attend a seminar in person on Thursday, May 21 from 12:00-1:00pm at DHMC, Auditorium H with Mark Glickman, PhD, Senior Lecturer, Department of Statistics, Harvard University. 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.<\/p>\n

 <\/p>\n

\u201cRating competitors in games with strength-dependent tie probabilities\u201d<\/span><\/strong><\/p>\n

Thursday, May 21 from 12:00-1:00pm ET<\/p>\n

<\/strong><\/p>\n

\"\"<\/p>\n

Mark Glickman, PhD
\n<\/strong><\/span>Senior Lecturer
\nDepartment of Statistics
\nHarvard University<\/p>\n

<\/strong><\/span><\/p>\n

The presentation will take place both in person at DHMC, Auditorium H (and via Zoom).
\nPlease write to biomedical.data.science@dartmouth.edu to request the Zoom login details.<\/p>\n

 <\/p>\n

Dr. Mark Glickman is a Fellow of the American Statistical Association and Senior Lecturer on Statistics at Harvard University, as well as Senior Statistician at the Veterans Administration\u2019s Center for Health Optimization and Implementation Research. His research focuses on statistical models for rating competitors in games and sports and on statistical methods for health services research. He is widely known for inventing the Glicko and Glicko-2 rating systems, which are used internationally by many gaming organizations, and his work in this area originated in his Harvard doctoral research on probability models for competitors with time-varying abilities. A U.S. national master in chess, he has served for decades on the US Chess Ratings Committee, including many years as chair. His broader professional activities include leadership roles in the American Statistical Association, editorial work for journals including the Journal of Quantitative Analysis in Sports and the Harvard Data Science Review, and founding the Lab for Sports Analytics at Harvard. Dr. Glickman received his B.A. in Statistics from Princeton University and his M.A. and Ph.D. in Statistics from Harvard University.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

The Department of Biomedical Data Science invites you to attend a seminar in person on Thursday, May 21 from 12:00-1:00pm at DHMC, Auditorium H with Mark Glickman, PhD, Senior Lecturer, Department of Statistics, Harvard University. Competitor rating systems for head-to-head games are typically used to measure playing strength from game [\u2026] <\/p>\n

<\/div>\n

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