Dr. Ken Regan, a leading expert in chess analytics, explores how predictive modeling can assess player performance and detect potential cheating. By analyzing patterns in move selection and comparing them to chess engines' recommendations, his approach quantifies deviations from expected human play. This method leverages Elo ratings, statistical modeling, and large datasets to estimate a player's intrinsic skill level and identify anomalies. The talk also delves into broader trends in chess performance, rating inflation, and the impact of online versus over-the-board play, offering valuable insights into the evolving chess landscape.
Bringing together the leading figures in sports analytics, business, and technology.
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