Stats Chat

Team Ratings for Week 12

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 56 matches played, 40 of which were correctly predicted, a success rate of 71.4%.
Here are the predictions for last week’s games.

Predictions for Week 12

Here are the predictions for Week 12. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 3

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 6 matches played, 5 of which were correctly predicted, a success rate of 83.3%.
Here are the predictions for last week’s games.

Predictions for Round 3

Here are the predictions for Round 3. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

The idea of cutting down on lattes to be able to afford a house has cropped up again. The proximate cause is a Newshub story that doesn’t quite go there — but it does talk about rent costs and mortgage rates and about satisfying a home lender under the new CCFA credit provisions, so it’s pretty close.

Now, first, I will agree that there are almost certainly people out there who haven’t emotionally grasped that buying 200 flat whites, one per day, costs (say) $900 that you could have spent on a $900 thing instead.  I don’t know if those people are likely to be helped by the story, but maybe it’s worth a try. At the level of housing, though, $900 in a year — or even two coffees every single day, for (say) $3300 — gets you nowhere in comparison with housing price inflation.  The same is true for avocados — maybe avocado toast in a cafe costs more than a coffee, but you don’t have it every day.

You might say that coffee (or avocado) is just one example, and that the point is to pay continuous and obsessive attention to shaving the costs of everything you buy. But to keep up with the rising cost of a mortgage deposit many people would have to save more than their entire discretionary income; shaving pennies isn’t going to get you there.

Perhaps most importantly, though, these approaches can’t work for most people because the housing crisis in New Zealand isn’t due to a shortage of money to spend on housing. We’re collectively spending too much money on housing. Cutting down on coffee or avocado or any other discretionary spending, so as to put more money into the real-estate sector, isn’t going to make housing more affordable on average, even if everyone does it.

This showed up on my Twitter feed this morning, originally from here. It triggered a certain amount of wailing and gnashing of teeth from Americans.

The basic pattern looks plausible; about two-thirds of the US population vaccinated. If you look carefully at the graph, you see something else: the ‘not vaccinated’ group are broken down by attitude. This can’t be an all-ages picture: if anyone is doing large-scale surveys of attitudes to Covid vaccination among six-year-olds around the world it’s (a) a revolution in survey methods that we should know more about and (b) not all that relevant to whether the six-year-olds get vaccinated.

As the description at the link says, this was based on a survey of adults. It was supposed to be nationally representative samples of adults. It clearly wasn’t. Based on doses delivered, the USA reached 75% vaccination for adults by October; Australia is currently over 95% in adults.  The qualitative message might be true, but the numbers aren’t right.

We saw recently how two big non-random US surveys had overestimated vaccination rates, the opposite problem. Why do people do this when we already know the answer? The surveys are (potentially) useful because they ask other questions: they can break down vaccination by other attitudes and circumstance of the respondent, which the CDC data cannot do. It still matters if the answers are right, though.

Team Ratings for Round 2

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 3 matches played, 3 of which were correctly predicted, a success rate of 100%.
Here are the predictions for last week’s games.

Predictions for Round 2

Here are the predictions for Round 2. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

• Covid was the leading cause of death “in the line of duty” for US law enforcement officers. (Also, the report giving the numbers had this graph
  
  The ‘Covid-19’ section of the bar, which should be nearly two-thirds of the total, is less than half)
• An attempt to check up on a sample of India’s forest-planting program
• Story on vaping from the Herald: it appears an Asthma and Respiratory Foundation survey gets very different results from the NZ Health Survey on the proportion of teens who vape daily.  That’s pretty concerning: 5.8% vape daily vs 20% ‘addicted’ means at least one of the surveys is badly wrong.
• People are more likely to trust pretty graphics (separately from other reasons to trust the information)
• Overestimating ‘incidental’ Covid diagnoses in London hospitals

The Herald has a story about ACC injury claims in Auckland during the lockdown.

Popular hobbies and chores like spring gardening, riding new e-bikes, lawn mowing, running and climbing ladders to paint resulted in 113,960 accident claims by Aucklanders during last year’s 107-day lockdown.

Reading on, you find that the e-bikes were involved in only one twentieth of one percent of that total (and not broken out  by new and old), but there’s a reasonable amount of detail given

There are usually two questions to ask about an ACC-based story like this one. First, are the injuries actually attributable to whatever it is the story is about? Second, is there an actual increase in injuries.  Usually the answers are “yes”, and “no, respectively: Christmas day brings injuries that are clearly Christmas-attributable, but fewer in total than a normal work day.  Usually the first question can be answered from the story, and the second takes some additional research.

This story is unusual in that we genuinely seem to have an increase in injuries, though as usual it takes some work to find this out.  Looking at historical ACC data, there were 245,149 claims for Auckland in the 2018/2019 financial year, the last year of the Before Times. Over 107 days that would scale down to about 72000, so we’re seeing nearly a 60% increase!

My guess is that the pattern would be different in other regions — it will depend on who was staying home and how risky their usual job would be.  Compared to the times before Covid, the lockdown would have increased some risks and decreased others; in Auckland the increase seems to have been larger than the decrease.

In the Herald today, Covid 19: Officials predict when Auckland Omicron outbreak will happen

They say 2-4 weeks. That looks to me like an estimate of the midpoint of the uncertainty: we’re thinking of a uncertainty distribution, and the middle is at about 3 weeks, say 2-4 weeks.  It doesn’t look like a prediction range.

The reason it doesn’t look like a prediction range is that the time until an Omicron outbreak isn’t like boiling an egg, it’s like getting in a minor car crash. Every day, there’s a small risk that Omicron will escape the MIQ controls and get out into the community.  The risk is higher (per day) than it was for Delta, because Omicron is more transmissible and because we have a lot more cases in MIQ than we ever had before. It may be lower because we may have fixed up gaps in MIQ that let cases in before (though we don’t actually know how the August outbreak started).

Lots of processes in the world work roughly like this: there’s a small chance at each point in time, but there isn’t any memory — getting through today doesn’t make tomorrow safer or less safe.  The distribution of times to the first event in a memoryless process with a median of three weeks looks like this:

If the experts are right that 3 weeks is the 50/50 bet for how long we have, the range of possibilities will be much broader than 2-4 weeks. If we’re very lucky, the outbreak could be 2-3 months away. If we’re very unlucky, it could start today. Or yesterday.

Team Ratings for Round 1

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Predictions for Round 1

Here are the predictions for Round 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

• Last June, the FDA approved a medication for Alzheimer’s Disease, although a lot of people, including their external advisory committee, thought there wasn’t convincing evidence that it works.  The Centers for Medicare & Medicaid Services, the organisation that would be paying for a lot of this treatment, have decided they need some randomised trials showing that the treatment is safe and effective, so they have said they will only fund the treatment in trials.
• According to Pew Research, “Overall, about half of U.S. adults (48%) say that most things in society can be clearly divided into good and evil, while the other half (50%) say that most things in society are too complicated to be categorized this way.” This is another example of a survey question where only differences or changes are really meaningful and we can’t straightforwardly interpret the absolute value. I mean, how about “eggplant” or (for something more obviously socially constructed) “driving on the left-hand side of the road”.  I would have said a lot of things in society have no particular moral valence, but also it’s not clear how you’d categorise “most” in cases where “good or evil or complicated” is a genuine question. The differences between groups are still potentially interesting, as might be changes over time.
• Not precisely ‘statistics’ or ‘in the media’, but a YouTube video of Adam Savage (Mythbusters) talking about measurement via Graeme Edgeler
• I gave a talk about StatsChat at a conference on undergraduate statistics and maths teaching