Several friends and journalists have been asking me lately about the fact thatÂ the headline betting odds have Labor a 70% chance to win, while the seat-by-seat markets have LaborÂ as favourite inÂ 77 out of 150 seatsÂ (see Simon Jackman’s site for current and past odds). As I see it, there are four possible explanations for this.
- There are quite a few Labor seats where the odds put them a 40-49% chance of winning, and fewer such Coalition seats. Taking this into account, Simon has also been summing the probabilities to come up with an ALP expected seat count. Today, he says, “ALP Expected Seat Count: 79.7 out of 150 seats.Â … Computed as the sum of the 3-agency-average seat-by-seat ALP win probabilities.” So this answer gets us from a 2-seat majority to a 4-seat majority. That satisfies some people (eg. Tim Colebatch and John Quiggin), but not everyone I speak to is persuaded that a 70% chance of winning is consistent with a 4-seat margin.
- The marginal seat markets are smart money, while the headline race is dumb money. So the headline odds are too optimistic for Rudd, and the marginals markets are right.
- The headline odds are based on two factors: certainty and margin.* To understand this theory, take the analogy of a flashy sporting team and a dependable sporting team. If betting on the flashy team, you might be uncertain about whether they’ll win, but you might figure that if they win, it will be by a big margin. With the dependable team, you might be quite certain that they’ll win, but feel that it’s unlikely that they’ll win by a huge margin. So while expected margin and expected winner will typically track one another, they needn’t always do so. You might be quite confident that Rudd will form government, without necessarily expecting a landslide.
- There’s no paradox. A 70/30 race is a pretty tight one. Unlike the pollsÂ (most of which imply that the chance of a Howard win is less than 1/10,000), the betting markets still say that this is a close race.
Of these theories, #1 is really just another way of expressing the data, and #2 doesn’t square with the literature (both theoretical and empirical) on prediction market efficiency. I’m inclined to prefer #4, though I do rather like #3.
* I owe John Quiggin for pointing this out to Justin and I when he read a pre-publication draft of our last election betting paper.