Stats Chat

Team Ratings for Round 11

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 60 matches played, 29 of which were correctly predicted, a success rate of 48.3%.
Here are the predictions for last week’s games.

Predictions for Round 11

Here are the predictions for Round 11. 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 Herald has numbers from Chorus on internet data use, which is up since last October. Their data is broken down by region. I noticed that Auckland was at the top and wondered how much of this was better internet access in Auckland and how much was just larger households. Here’s a graph (click to embiggen). I had to guess that the ‘Hamilton’ region meant Waikato, and the table is missing Marlborough. Also, my data source for household  size had separate figures for Nelson and Tasman, but it should be basically right.

That’s actually more of an impact of household size than I expected. Also, I was a bit surprised that the West Coast is above the fitted line, saying that it has more internet use than you’d expect from household size, but I suppose that’s what you’d hope when people are spread out a lot.

The regression line is a bit unreliable with such a small dataset, and leaving out Auckland weakens the evidence for a relationship quite a bit (though it doesn’t actually change the fitted line very much). It’s worth thinking about alternative explanations. It’s reasonable that internet use would scale with household size (and I did think of this before looking at the data), but it could also be that Auckland has larger household size and more internet use because it’s a city

Team Ratings for Week 7

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 42 matches played, 31 of which were correctly predicted, a success rate of 73.8%.
Here are the predictions for last week’s games.

Predictions for Week 7

Here are the predictions for Week 7. 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 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 77 matches played, 59 of which were correctly predicted, a success rate of 76.6%.
Here are the predictions for last week’s games.

Predictions for Round 12

Here are the predictions for Round 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 10

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 54 matches played, 24 of which were correctly predicted, a success rate of 44.4%.
Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 10. 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.

• Is Covid Omicron going to be a gentler, kinder virus? Actually, we have no idea at all yet, as David Welch tells Jamie Morton. Worry about something else for a week or two; there’s  no shortage of world problems.  Also, see Trevor Bedford on Twitter.
• The Statistics Act (1975) is up for revision. You have until 22 December if you want to make a submission on the current Data and Statistics Bill.  If you read StatsChat, it’s possible that you do want to comment.
• Story in the Herald saying that healthy diets are better for the environment. I probably won’t write about this one in detail, but you might look at this 2015 post on a Herald piece saying healthy diets are worse for the environment.
• ” if passes weren’t going to be checked, they may not represent a justified privacy breach.” Andrew Chen (the patron saint of the NZ Covid app) in a Newsroom story on not requiring vaccine pass validation.
• Not precisely statistics or in the media, but visualisation: Assyrian low-relief carvings with (possibly) their original colours
• If you want to read a careful and thoughtful analysis of the data on ivermectin for Covid then I’d actually advise not bothering, but this is a good place if you really have to
• A nice illustration (via Twitter) of why you’d expect quite a few vaccinated Covid cases if you have a lot of vaccinated people
  

Circulating on Twitter, but originally from US News and World Report

It’s an interesting list.

Most of the people talking about it on Twitter wanted to ridicule the list without actually worrying about how it was constructed, so it didn’t come with any link or any explanation beyond the source. It’s not hard to find some information, although I haven’t been able to get full details.

There are different ways you might go about constructing a ‘racism’ ordering for countries. According to a 2013 story in the Washington Post, one ranking of basically this sort started with a question in the World Values Survey. Two researchers (I’ll let your prejudices work by saying they were Swedish economists) wanted to look at relationships between economic freedom and racism.  They needed something widely measured, and used a question about kinds of people you would not be happy with having as neighbours.  One of the options was “people of a different race”, others include “people with AIDS”, “immigrants”, “heavy drinkers”, “unmarried couple living together”, “people of a different religion” and so on.  These economists used as their metric for racism the proportion of people who would not want someone from another race as a neighbour.  If you were being pedantic, like me, you might call this a xenophobia/xenophilia score rather than a racism score. It clearly measures something relevant, but you’d expect it to miss the “Some of my best friends are black/gay/Jewish/etc” type of polite racism. This follow-up piece at the Washington Post  covers some of the other complications.

The scale based on the World Values Survey has some agreement with the current version, but it’s not the same. In particular, the USA does quite well on the World Values Survey question, but rates low on the current metric.

The current version is from a survey called “Best Countries“. It has a simpler structure. Respondents (10,068 were informed elites, 4,919 were business decision-makers and 5,817 were considered general public) rated each country on 76 attributes, one of which  was racial equity.  They were also asked whether they agreed “A country is stronger when it is more racially and ethnically diverse” but it doesn’t appear this goes into the ranking (the Danes and Swedes were below the global average on this question, though NZ and Canada were high).

So, the ranking is based on whether a sample of people around the world, targeting ‘informed elites and business decision makers’, thinks that the country is racist or not. The problem with a ranking like that is that most respondents have no actual idea of whether Denmark or Botswana or Agrabah or Paraguay is racist; they’re just going by their own prejudices and what they see in the news.  It’s quite likely that the very low rating for the US is due in part to the Black Lives Matter protests — which you could argue were a good sign not a bad sign for US attitudes on race.

From the NZ Herald, squashed-trees edition

It’s not really clear what’s going on here: the 3.75% at the bottom right vs the 3.75% at the top left.

Things are better on the NZ$ Herald website, under the headline The great divide: Why are NZ interest rates so much higher than Australia?

Here it’s clear that the label at the bottom right had just gone feral somehow and that the graph is at least plausibly correct. There’s still a bit of a problem in that, at least for the historic part of the graph, the lines should be flat where they don’t jump; there shouldn’t be any slopes. The RBNZ didn’t come out in early 2010 and say “we’re going to smoothly decrease the rate from 3 to 2.75 over the rest of the year”; that’s not how they work. Also, NZ interest rates aren’t actually “so much higher” than rates across the Tasman; they’re just projected to be higher.

Checking against this July graph from interest.co.nz basically confirms the numbers, though there is some interesting disagreement if you care about details, such as the shape of the interest rate rise and fall in 2014-15 and whether the Oz rate was above or below the NZ rate at the start of 2016

The July projections diverge less than the current predictions do: the banks aren’t actually all that good at predicting interest rates two years ahead.

The spurious slopes are still there in the graph, though in this one at least the flat bits are flat and it’s just the vertical bits that aren’t vertical. That’s even a problem on the official RBNZ website.

None of this is a criticism of the actual content of the Herald piece, which both talks about the reasons for divergence and quotes experts who don’t think the diverging forecasts will hold up

Ultimately, despite the two very divergent central bank views, the answer is somewhere in the middle and the rate tracks will move closer in the year ahead, McLeish says.

But the graph and headline don’t help

From Sophie Jones on Twitter:

If you zoom in on the fine print, that’s 50.6% of 15096 people preferring Pepsi Max over full-sugar Coca-Cola.  You could quibble about the comparison — should this be restricted to cola drinkers (or non-cola drinkers); what happened to the diet versions; how about L&P? — but it’s a comparison.

More obviously, 50.6% is very close to 50%.  You  might ask what the margin of error was for a sample of 15000. It’s more than 0.6%: these results are consistent with just a coin toss.  It might taste like victory, but only if victory doesn’t taste very distinctive.

On the other hand, Pepsi lost the cola wars in New Zealand, so the starting point might reasonably not be 50:50.  This survey doesn’t convincingly show that Pepsi Max is preferred over Coca-Cola by a majority even in blind two-way comparisons, but it does show it’s not far behind. And, in context, that’s probably worth advertising.

The Covid vaccine is safe and effective and it’s good that most eligible people are getting it. But how much protection does it give? If you look at the NZ statistics on who gets Covid, it seems to be extraordinarily effective: the chance of ending up with (diagnosed) Covid for an unvaccinated person is about 20 times higher than for a vaccinated person.

That’s probably an overestimate. People who are vaccinated are at lower risk for immunological reasons: the vaccine really works.  We’re also at lower risk for social reasons: if you’re vaccinated, your friends and family and people you interact with are also more likely to be vaccinated, so they are less likely to give you the virus. That’s partly due to equity problems in the vaccine rollout and partly just to what social-network people call homophily:  you tend to hang out with people similar to you. The immunological reason will hold true over summer; the social reason perhaps less so if people travel. 

Also, because elimination came so close to working in Auckland, the virus has been fairly effectively suppressed in most of the New Zealand population.  On top of the clustering of unvaccinated people, there’s very strong clustering of the current outbreak — it’s mostly in Auckland, but it’s not at all evenly spread within Auckland.  Even if you’re in Auckland you probably know either no-one or lots of people who have been infected.  If you’re in know no-one, you’re at lower risk– and you’re probably vaccinated. As we go from a more or less localised outbreak to many little outbreaks, this additional clustering will go away and the apparent benefit of vaccination will fall.

How much will it fall (and why am I sure)? In the USA, you’re currently about 6 times as likely to get a Covid diagnosis if you’re unvaccinated (according to the CDC). In the UK, the ratio comparing unvaccinated people to those with a Pfizer vaccination within four three months is 4-5.  That fits with the estimates of how effective the vaccine is, biologically, against Delta, plus a bit of social clustering.  The ratio in NZ will be heading that way over time.

So: vaccines, yes, but also masks and distancing and meeting people outside when you can and getting tested if you have symptoms and not going to isolated places that don’t even have enough of their own health care.  Don’t give the virus an inch.