Probability of victory vs vote share

We are going to be talking a lot about probabilities on this blog. These are very different to common quantities used in polling, like the two-party preferred, or other measures of vote share. It’d be good to look at how misinterpreting the probabilities can lead to wrong conclusions.

We get our data from the Sportsbet website. They have betting odds for all 150 electorates. For example, here is the link for the betting prices for each Victorian seat. From the betting odds, we can then calculate the probability of each party winning the seat.

The wrong approach

The natural response would be to think that the task of making predictions is then very easy. Just count up the seats that the Coalition has greater than 50% chance of winning and compare it to the number of seats the ALP has greater than 50% chance of winning. This could then form the predicted number of seats each party will win based on the betting market. And the party with more than 76 seats is predicted to win government.

But this is wrong.

To demonstrate why, here is a simple example. Imagine there are 3 seats so a majority of 2 is needed to win the election. Say the ALP has a 51% chance of winning seat 1 and 2, but 0% chance of winning seat 3. The simple approach would predict the ALP to win 2 seats and the Coalition would win 1 seat. But this ignores the fact that there is a lot of uncertainty around who will win seat 1 and 2.

What is the right approach?

We know the ALP is definitely going to lose seat 3. The only way for it to win the election is to win both seat 1 and 2.

Probability of ALP winning the election = Pr (ALP >= 2 seats) = 26.01% (0.51 * 0.51 because the two events are independent).

But the coalition can win the election a number of ways. It has seat 3 locked up. It can win the election by winning:

  1. seat 1 only; OR
  2. seat 2 only; OR
  3. win both seat 1 and 2

The associated maths is:

  1. Probability of winning seat 1 only (ie. lose seat 2) is 24.99% (0.49 * 0.51)
  2. Probability of winning seat 2 only (ie. lose seat 1) is 24.99% (0.51 * 0.49)
  3. Probability of winning seat 1 and 2 is 24.01% (0.49 * 0.49)

Summary of Results

Probability of Coalition winning  the election = 73.99% (24.99% + 24.99% + 24.01%)

Pr(Coalition wins exactly 2 seats) = 49.98%

Pr(Coalition wins exactly 3 seats) = 24.01%

What explains the difference between the two approaches? The wrong approach confuses probability of victory with the proportion of the vote won. Saying the ALP has a 51% chance of winning a seat is not the same as predicting they will win 51% of the vote. In fact, a party could have a 99% chance of victory, and only be expected to win 51% of the vote (since 51% is enough to win you the seat); or it could have a 1% chance of victory and be expected to win 49% of the vote. The two terms are only loosely related.

Instead of having 3 seats, and probabilities of the ALP winning the seat at 51%, 51% and 0%, we have data for 150 seats and the corresponding probabilities. We then create a probability mass function (PMF) which gives the probability the ALP (or the Coalition) will win a certain number of seats. Later posts will outline how we generate the PMF and what they currently look like.


2 thoughts on “Probability of victory vs vote share

  1. Pingback: election lab | Our final predictions: probabilities for every seat

  2. Pingback: election lab | Our final predictions: how did they go?

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