Monte Carlo simulation of the 2019 Australian election

In September last year, I cobbled together a simple Monte Carlo simulation of the Federal election using:

• 2016 seat-by-seat two-party preferred (TPP) outcome data,  
• an estimate of state-by-state TPP polling outcomes from the Newspoll Quarterly,
• an estimate of the national TPP vote from my latest Bayesian poll aggregation

Back then, when the Coalition's TPP vote share was 45 per cent in the opinion polls, I estimated the most likely election outcome was a Labor win with 95 seats. I thought the Coalition would get 51 seats, and 5 seats would go to others. I noted that my Monte Carlo model did not account for the retirement effect nor the sophomore effect.

Today, I re-ran that model with updated inputs:

• the latest Coalition TPP vote share estimate (47.4 per cent) 
• the number of enrolled voters by state
• the estimate of TPP vote share at the 2016 election, applied to the new electoral boundaries
• the latest state-by-state TPP estimates from the Newspoll Quarterly
• I added Wentworth to the seats that would be held by a minor party or independent (bringing the total to six). This seat would come from the Coalition's total.

The headline result was a Labor win with 87 seats. The Coalition got 58 seats in the simulation. And 6 seats were allocated to minor parties and independents.

This compares reasonably well with the Cube Rule, which I use as a back-of-the-envelope validation check.

My results are reasonably comparable with Antony Green's election calculator (although I give three more seats to Labor than Antony). For a Labor TPP vote share of 52.6 per cent, Antony's calculator estimates Labor would win 84 seats, and the Coalition would win 61 seats. Like me, Antony allocates six seats to independents and other parties. I think the key difference is that my model automatically updates for state swings; whereas the way in which I used Antony's model was a single national swing.

My adjusted state swings have Queensland, WA and NSW as the key trouble spots for the Coalition. Victoria looks the least profitable for Labor. Note: I used the national swing for Tasmania, ACT and the NT, because I did not have any other data. (Note: my model adjusts the state swings from the Newspoll Quarterly such that the state swings are always consistent with the national swing. It does this in a way that allows some variability across simulation runs/ This variability can be seen in the next chart which provides a kernel density estimate for each state swing across the 100,000 simulation runs.)

Based on the two-party preferred data, I plot a two-party probability outcome for each seat. This is an artificial construct, as a number of seats were a two-candidate preferred outcome at the 2016 election where one of the candidates did not come from the major parties. This table includes the six seats that I have allocated to others in the totals above.

Update

I have quite different state-by-state TPP estimates compared with William Bowe from the Poll Bludger. I am looking into these differences, and may need to update this page when I better understand the source of Bowe's estimates.