Stats Chat

Team Ratings for Round 1

I have decided to not give home ground advantage to the Warriors for any games played at their Australian base on the Central Coast of New South Wales. I will treat all those games as being played on neutral ground.

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.

Q: Is four a lot?

A: Depends on the context. Lockdowns, yes. Incursions per 46000 people through MIQ, no.

Q: We’ve had four lockdowns in Auckland, right?

A: Yes

Q: And how many cases let through MIQ for each 46000 people?

A: About four

Q:

A:

Q: ಠ_ಠ

A: My point, and I do have one, is that more people need to say what their criteria are. If you think four lockdowns is unacceptable, what’s your target that you can argue a realistic system would achieve? If you think 10 cases in 115000 MIQ visitors is a good result, what would it take to be a bad result?

Q: So what’s your threshold?

A: I don’t know. That’s why I haven’t been making comments about the overall performance being good or bad.

• About 140% of Kiwis aged 20-24 are on Facebook (NZ$ Herald) — more precisely, that’s the ratio of Facebook’s claimed “reach” to StatsNZ’s population estimates.  This is an age group where the census does relatively badly, but nowhere near that badly.
• The false-positive rate of the PCR tests for Covid has become topical again, so it’s worth revisiting how we know it is very low. In addition to knowing it should be low based on how the test works, we have simple, direct evidence that the false positive rate is low for the test as used in NZ. We have so far performed 1,720,909 tests in New Zealand and 1,717,865 of them, or 99.8%, have been negative. The 0.2% left over include all the true positives (including people with serious Covid cases), all the inconclusive results, and any false positives.
• Business$ Week reports on the countries of origin of Covid cases in MIQ.  As you’d expect, the top four are four of the top five in the world for Covid cases: US, India, UK, Russia. Brazil, the other country in the top five, doesn’t show up, but we don’t get as many immigrants or visitors from South America.  Also, as you’d expect, China is very low on the list, because we didn’t start the MIQ system until after they’d controlled their big outbreak. Image (from tweet by @victoriayoung03)
  

According to the Guardian (and various other sources), the Pfizer Covid vaccine “may be” less effective in people with obesity, based on an Italian study of antibody levels in 248 vaccinated healthcare workers, 26 of whom were classified as obese and  56 more as overweight.

In the actual randomised clinical trial of the Pfizer vaccine, there were 13,000* people classified as obese and a further 13,000 classified as overweight.  The trial considered obesity as a factor that might affect the vaccine success.  In people with obesity, the estimated efficacy based on actual Covid cases was 95.4%. In those without, it was 94.8%. This is not mentioned at all in the Guardian story.

It’s quite common for clinical trials to end up with a very non-representative subset of the population where you want to use the treatment.  That’s much less true of the US Covid vaccine trials (apart from being US-biased). They made serious efforts to recruit a wide range of people, and did pretty well.  Any risk factor that’s common in the United States is likely to have been common in the trial population, so it’s already represented in the results — even if the results weren’t specifically reported based on that factor, as they are for obesity.

There’s some good research with careful estimation efforts being made to check that efficacy in practical use matches up to the trial, but just measuring antibodies in a small convenience sample isn’t worth a headline.

* There are a bunch of different analysis populations in the trial, so the number will be different in different places. They’re all big.

Team Ratings for Round 18

I apparently missed another rescheduled game. The prediction is given below for completeness.

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 125 matches played, 82 of which were correctly predicted, a success rate of 65.6%.
Here are the predictions for last week’s games.

Predictions for Round 18

Here are the predictions for Round 18. 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 Week 3

My automatic updating of scores failed, sorry. Correct results now I hope.

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 Week 3

Here are the predictions for Week 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.

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 59 matches played, 33 of which were correctly predicted, a success rate of 55.9%.
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 14

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, 52 of which were correctly predicted, a success rate of 67.5%.
Here are the predictions for last week’s games.

Predictions for Round 14

Here are the predictions for Round 14. 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.

• Good explanation by Jarrod Gilbert at newsroom of why gang member statistics tend to be inflated: it’s harder to count people leaving.
• Eric Crampton (correctly) argues that the Climate Commission should just publish its models so people can see what they’re sensitive to. Peter Ellis (who would know) thinks this should be perfectly feasible. I reviewed an air pollution epidemiology initiative that did this sort of thing with analysis code and pollution and health information from the 90 largest US cities, nearly two decades ago. Back then, it was kind of notable; now, not so much.
• The New York Times had a front-page visualisation of 500,000 Covid deaths, as dots over time. People in NZ aren’t the best audience for deciding how well something like this works, but there was an interesting Twitter thread criticising it.
• Truncating bar charts — failing to show them all the way down to zero — is bad in theory and I often complain about it here.  New research suggests it’s actually bad in practice, too.  It conveys inaccurate information even to trained people and even to people who are warned in advance.
• For a bit of nerd fun: Iceberger — draw an iceberg and see which way it will end up floating (motivated by the Campaign For Sideways Icebergs)
• Data collection is hard:  “I’m interviewing 5 year olds and their best friend is a pretzel, so it’s hard to map their social networks.” — Jason Chow at an education conference in the US, quoted on Twitter
• Excess mortality in 2020 in the US (from Kieran Healy)
  

Team Ratings for Postponed Games

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 122 matches played, 81 of which were correctly predicted, a success rate of 66.4%.
Here are the predictions for last week’s games.

Predictions for Postponed Games

Here are the predictions for Postponed Games. 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.