  Elections involving a very large voter population often lead to outcomes that
surprise many. This is particularly important for the elections in which
results affect the economy of a sizable population. A better prediction of the
true outcome helps reduce the surprise and keeps the voters prepared. This
paper starts from the basic observation that individuals in the underlying
population build estimates of the distribution of preferences of the whole
population based on their local neighborhoods. The outcome of the election
leads to a surprise if these local estimates contradict the outcome of the
election for some fixed voting rule. To get a quantitative understanding, we
propose a simple mathematical model of the setting where the individuals in the
population and their connections (through geographical proximity, social
networks etc.) are described by a random graph with connection probabilities
that are biased based on the preferences of the individuals. Each individual
also has some estimate of the bias in their connections.
  We show that the election outcome leads to a surprise if the discrepancy
between the estimated bias and the true bias in the local connections exceeds a
certain threshold, and confirm the phenomenon that surprising outcomes are
associated only with {\em closely contested elections}. We compare standard
voting rules based on their performance on surprise and show that they have
different behavior for different parts of the population. It also hints at an
impossibility that a single voting rule will be less surprising for {\em all}
parts of a population. Finally, we experiment with the UK-EU referendum
(a.k.a.\ Brexit) dataset that attest some of our theoretical predictions.
