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Although 2016 is a highly unusual political year, elections and public opinion follow predictable statistical properties.ÌýI will review how the Presidential, Senate, and House races can be tracked and forecast from freely available polling data. Missing data can be filled in using a Google-Wide Association Study (GoogleWAS). Finally, simple statistics can be used to identify inequities such as partisan gerrymandering, and provide a tool for possible judicial relief. These examples show how statistics and machine learning can deepen an understanding ofÌýthe U.S. political scene, even under extreme circumstances. ÌýSamuel S.-H. Wang, Ph.D.,ÌýProfessor, Neuroscience Institute and Department of Molecular Biology, Princeton University