  The wisdom of the crowd has long become the de facto approach for eliciting
information from individuals or experts in order to predict the ground truth.
However, classical democratic approaches for aggregating individual
\emph{votes} only work when the opinion of the majority of the crowd is
relatively accurate. A clever recent approach, \emph{surprisingly popular
voting}, elicits additional information from the individuals, namely their
\emph{prediction} of other individuals' votes, and provably recovers the ground
truth even when experts are in minority. This approach works well when the goal
is to pick the correct option from a small list, but when the goal is to
recover a true ranking of the alternatives, a direct application of the
approach requires eliciting too much information. We explore practical
techniques for extending the surprisingly popular algorithm to ranked voting by
partial votes and predictions and designing robust aggregation rules. We
experimentally demonstrate that even a little prediction information helps
surprisingly popular voting outperform classical approaches.
