For the fifth consecutive year, Squiggle carefully recorded all public preseason ladder predictions made by experts and media pundits, and scored how accurate those predictions turned out to be.
Not all well-known media names do preseason ladder predictions. In fact, only a minority seem willing to put their name to an actual ladder, as opposed to safer, more vague statements about which teams might rise or fall. This year, we found fifty of them. All should be applauded. But also scored.
Since this is the fifth year, we’ve built a reasonable idea of how reliable people tend to be in the long-term; that is, whether a great prediction this year means that person is likely to come back with another the year after. But before we get to that, here’s how 2023 shook out:
Horn led for most of the back end of the year, thanks to his faith in Collingwood and Port Adelaide. But Pierik closed in the final round with a ladder that correctly tipped 6 out of 8 finalists, and placed eight teams within a rung of their actual position. There might have been a fair bit of luck, given the closeness of the middle of the pack, and how easily it might have been different. But it was a great ladder. The rating system judges them different but equally good, so Horn and Pierik share the honours.
Best ladder by a model: Squiggle
It was a poor year for models, who went very heavy on Geelong and underestimated Collingwood. None made the top 10, while many fell to the bottom half of the 50 predictions. The best was Squiggle’s own in-house model, which was ranked 14th.
The betting odds weren’t a great guide, either, with an aggregate of where punters were putting their money landing 25th, right in the middle.
Worst ladder: 2022 + Pythagorus
To be fair, this wasn’t Max Laughton’s own prediction (which is ranked 30th). It was, instead, a completely reasonable application of Pythagorean wins to adjust what the 2022 ladder “should” have looked like. But since it was supposed to tell us what the ladder would look like in 2023, too, I included it. And it did badly: worse, in fact, than if no adjustment was performed at all, and we guessed that this year’s ladder would look the same as last year’s.
With only half the Top 8 correct, no team in the exact right spot, and five teams out by at least 8 rungs, it ties with Damien Barrett’s 2019 entry as the worst ladder prediction we’ve recorded.
Long-Term Performance Award
Last year, we lauded Peter Ryan, who over four years maintained an average rank of 8th, including topping the list in 2022. Unfortunately, Ryan had a shocker this year, finishing near the bottom, mostly because of a failure of optimism in Port Adelaide, Carlton, GWS, and St Kilda.
The best long-term performers, counting everyone who made a prediction in at least 3 of the last 5 years, are:
|14.6||Sam McClure (The Age)|
|15.4||Peter Ryan (The Age)|
|15.6||Jake Niall (The Age)|
|16.0||Riley Beveridge (AFL Media)|
|16.0||Nat Edwards (AFL Media)|
|16.3||Sarah Olle (AFL Media)|
|16.3||Daniel Cherney (The Age)|
|17.0||Michael Gleeson (Code Sports)|
|18.2||Jon Pierik (The Age)|
Each year there are about 50 expert predictions, so it’s challenging to remain even in the top half on a long-term basis. Or, put another way, ladder predictions seem to be a bit of a crapshoot, with not much evidence that someone who made a good prediction this year will be able to do it again next year.
Squiggle is, frankly, killing it here, outperforming the whole football industry by a significant margin over the tracked period. You can judge for yourself whether this is due to the brilliance of the Squiggle algorithm or the awfulness of the average media ladder prediction. And, to be sure, a big reason for this project was the suspicion that a lot of ladder predictions got tossed around each year by people who didn’t expect anyone to look at them again after March.
Live Running Predictions
Squiggle also tracks ladder predictions made throughout the year by various models, including our own. This year, Squiggle narrowly beat out Glicko Ratings, Matter of Stats, Wheelo Ratings, and AFLalytics.