As some of you may remember, during the US election we ran big monte carlo simulations using Intrade data to try and forecast the result of the Electoral College vote — it was pretty successful all up, predicting 364 EVs when the actual result was 365. But we’ll get into just how it all went when the yanks can finally finish their election count in the Senate races.
So I thought we’d develop a similar monte carlo simulation for our own political horserace, but instead of using Intrade or other betting data, we’ll use the polls.
Simulations are much easier than they sound — so fear not aspiring nerds! We simply take the information we know (the previous election results and the fact that in Australia we have a uniform swing of X with a standard deviation of Y) and compare it to the information we think we know (the polling data) to answer the question “Using all of this information, what is the probability of a party gaining a given number of seats if an election were held today?”
First up, the information we do know.
In Australia at each election, because of our two party system we generally experience a uniform swing toward or against the government of some given percent, and a standard deviation or some given number. If we break down previous election results by State, we get the same thing — and the standard deviations (which become important for monte carlo sims) are strangely consistent.