[caconym_]

Election forecasting is mostly about trying to quantify the current state of play based on imperfect signals. There is theoretically a "right answer" well before the final tally, but without being able to look inside people's heads en masse you can only guess at it. Still, this is conceptually different from forecasting the behavior of a system that's actually subject to randomness or [semi-]chaotic instability, where the given uncertainties will correspond at least partly with actual nondeterminism sitting between the current state of the system and the answer.

Therefore I think it's fair to say that the weight an election forecast assigns to the actual winner is a direct indicator of the accuracy of its model. We aren't trying to guess at how a set of dice are weighted, knowing they'll only be thrown once—we're trying to get as close as we can to knowing who is going to vote and who they are going to vote for, and (absent some large disaster or upheaval) a misforecast will be largely attributable to systematic errors in our methodology.