This paper presents state-level estimates of the 2016 presidential election using data from the ABC News/Washington Post tracking poll and multilevel regression with poststratification (MRP). While previous implementations of MRP for election forecasting have relied on data from prior elections to establish poststratification targets for the composition of the electorate, in this paper we estimate both turnout and vote preference from the same preelection poll. Through Bayesian estimation we are also able to capture uncertainty in both estimated turnout and vote preferences. This approach correctly predicts 50 of 51 contests, showing greater accuracy than comparison models that rely on the 2012 Current Population Survey Voting and Registration Supplement for turnout. While the model does not perfectly estimate turnout as a share of the voting age population, popular vote shares, or vote margins in each state, it is more accurate than predictions published by polling aggregators or other published MRP estimators. The paper also reports how vote preferences changed over the course of the 18-day tracking period, compares subgroup-level estimates of turnout and vote preferences with the 2016 CPS Survey and National Election Pool exit poll, and summarizes the accuracy of the approach applied to the 2000, 2004, 2008, and 2012 elections. The paper concludes by discussing how researchers can make use of this method as an alternative approach to survey weighting and likely voter modeling as well as in forecasting future elections.