Causal discovery and large language models in sports analytics
報告人簡介
Weining Shen is Associate Professor of Statistics at University of California, Irvine. He received his PhD from North Carolina State University and spent two years as a postdoc at the University of Texas M.D. Anderson Cancer Center. He is an associate editor for a few statistical journals including JASA, AoAS, and Statistica Sinica. Prof. Shen’s research interest includes Bayesian methods, machine learning, high-dimensional models, and applications in neuroscience, biology, sports analytics, and education assessment.
內容簡介
In this talk, I will present two recent projects in sports analytics. The first project examines the causal factors contributing to home advantage in English Premier League football, introducing a new causal discovery method capable of handling non-stationary causal structures while providing theoretical guarantees for model identifiability and estimation consistency. The second project focuses on evaluating the sports understanding of mainstream large language models, using newly introduced benchmark datasets. Our evaluation covers a range of tasks, from basic queries about rules and historical facts to complex, context-specific reasoning, as well as assessing the sports reasoning capabilities of video language models.
