We report on a large-scale case study of a combinatorial prediction market. We implemented a back-end pricing engine based on Dudik et al.’s (2012) combinatorial market maker, together with a wizard-like front end to guide users to constructing any of millions of predictions about the presidential, senatorial, and gubernatorial elections in the United States in 2012. Users could create complex combinations of predictions and, as a result, we obtained detailed information about the joint distribution and conditional estimates of election results. We describe our market, how users behaved, and how well our predictions compared with benchmark forecasts. We conduct a series of counterfactual simulations to investigate how our market might be improved in the future.