Stats Chat

Team Ratings for Round 6

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 10 matches played, 9 of which were correctly predicted, a success rate of 90%.
Here are the predictions for last week’s games.

Predictions for Round 6

Here are the predictions for Round 6. 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 9

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 16 matches played, 11 of which were correctly predicted, a success rate of 68.8%.
Here are the predictions for last week’s games.

Predictions for Round 9

Here are the predictions for Round 9. 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 13

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 96 matches played, 64 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Predictions for Round 13

Here are the predictions for Round 13. 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.

At the moment, NZ policy is to test everyone with suitable symptoms consistent with COVID-19, and potential contacts of cases, but not to go around randomly bothering healthy people for community surveillance.  Looking at some numbers explains why that’s a good strategy, and also gives a way to think about what it takes for other surveillance strategies to be useful.

The first question is how well you do just by testing symptomatic people, when we know some people never develop symptoms, and other people only develop symptoms after passing on the virus.  Professor Nick Wilson and various co-workers^* studied this problem back in May.  They did a lot of computer simulations of what would happen if you introduced one COVID case to ‘an island nation’ where the coronavirus had been eliminated but there was still widespread testing.  Under the assumption that about 40% of cases ended up getting tested, they found that an outbreak had a fifty-fifty chance of being detected when there were only six active cases, but that a reasonable worst case was 50-100 active cases at the time of detection.

You can disagree with the particular assumptions being made (they did this way back in May, the coronavirus equivalent of the Sony Walkman era) but it’s a reasonable ballpark guide.   The idea is that you maybe don’t routinely test absolutely everyone with a cold, but you do test everyone with some (new or worsening) respiratory symptom plus shortness of breath, or fever, or loss of sense of smell, or various other combinations.  We’ve gotten lucky: flu-like illnesses are much less common so far this year than in a usual year, so right now we don’t need to test as many people as they modelled.

So, taking the reasonable worst case, suppose at some point there are 50-100 people out there in NZ with coronavirus and we’ve been unlucky enough that none of them got tested (or a few got tested and the tests were false negatives).  How many random people would we need to test to pick up this outbreak?

Fifty people in NZ is one person in 100,000, so we’d need to test about 100,000 people to have a chance of finding a case.  A simple statistical rule of thumb says that testing about 300,000 people would make us pretty sure to find a case.  Outbreaks grow fast;  if there’s an outbreak with 50 people this week, it had maybe  15 people last week. To get a worthwhile improvement in detecting even the worst outbreaks we’d need to test 100k-300k healthy people each week. That isn’t happening.  Random community testing could be useful if we knew where to look. If we had one suspected case in a town of 10,000 people it might be worth just testing as many people as we could, to try to  get ahead of the contact-tracing process. But if you don’t know where to look there isn’t much point in looking.

Sewage testing is another promising possibility for picking up outbreaks, but these numbers show that it’s not going to be easy.  The testing has to be reliable enough that we’d be prepared to take some fairly major and expensive actions based on finding the virus, but sensitive enough to pick up just 50 or so cases.  It currently isn’t clear whether or not that’s possible, but ESR have the expertise (and some funding) needed to work on the question.  Reliable wastewater testing would be very helpful in the situation we’re now in, where there’s a  suggestion of transmission in NZ but not good evidence — but unreliable wastewater testing would just make things worse.

The take-home message is that we’re probably going to find the next outbreak by testing someone with symptoms. That person might very well have no known contact with international travellers.  If you might be that person, you should call Healthline to ask about getting tested.

^* Statisticians will recognise Matt Parry; any Kiwi who hasn’t been hiding in a cave on Mars with their fingers in their ears should recognise Ayesha Verrall and Michael Baker.

We aren’t getting international tourism at the moment, which is obviously a problem for those working in the international tourism industry¹, and to some extent a problem for everyone because of the hit to the economy.

I saw some speculation on Twitter today about how big international tourism actually is, and about the extent to which NZ tourism expenditure staying in NZ would offset the losses.   Now, there will obviously be gaps, where foreign and domestic tourists don’t do the same things (eg, domestic tourists don’t buy long-distance plane tickets from Air New Zealand), but what about the totals?

Overall, tourism (as defined in the tourism satellite account) brought in $17 billion in the year ending June 2019.  Nearly $4 billion of it was actually international education lasting less than a year, leaving a bit over $13 billion in ‘real’ tourism.  That’s just behind dairy, but roughly equal to meat and wood products together.  The short-term international-education component of ‘tourism’ was a bit a head of fruit exports.

Domestic expenditures on international tourism aren’t completely captured, but the Household Expenditure Survey estimates that all NZ households together spent $2 billion on “overseas accomodation prepaid in NZ” and $4.5 billion on “international air transport” in the year to June 2019.  That’s going to miss food bought overseas and admission tickets to cultural experiences, and some overseas accomodation, but it still looks as though redirecting NZ tourism locally would leave a big hole.  The Household Expenditure Survey does miss out on business travel that isn’t a household expenditure, but it seems more of a stretch that business travel spending will just be redirected to NZ.

¹ I was surprised to find this, in one sense at least, includes me, since international students here for less than 12 months are counted in the ‘tourism satellite account’.

The recent trends in opinion-poll support for the National Party got a lot of attention. That’s because real opinion polls, like those done by Colmar Brunton and Reid Research (and the internal party polling that they tell us about when they think it will help them) are genuine attempts to estimate popular opinion.  You can argue about how good they are — but you can argue about how good they are, there are factual grounds for discussion.

NewsHub ran a bogus online clicky poll with the question Who would you prefer as Prime Minister – Judith Collins or Jacinda Ardern?  Of the people who clicked on the poll, 53% preferred Ms Collins, and 47% preferred Ms Ardern.  Let’s compare that to the two real polls. The 1News/Colmar Brunton poll had

• Jacinda Ardern: 54% 
• Judith Collins: 20%

The 3/Reid poll had

• Jacinda Ardern: 62% 
• Judith Collins: 15% 

Why are these so different from the Newshub clicky poll? The first point is that there’s no reason for them to be similar. Two of them are estimates of popular opinion; the other one is a video game.

On top of that, the question is different.  The real polls are asking who (out of basically anyone) is your preferred PM.  The bogus poll forces the choice down to Ardern vs Collins.  If you supported Simon Bridges or Todd Muller — or Metiria Turei  or Winston Peters — the real polls let you say so, and the bogus poll doesn’t.

Research Association NZ, who are the professional association for opinion researchers in NZ, have a code of practice for political polling (PDF). It’s only binding on their members, but it does have best practice advice for the media, such as using the term “poll” only for serious attempts to estimate public opinion, not for bogus clicky website things.

• The Algorithm Charter has been released. Stories from NewsHub, newsroom, The Guardian, The Register, ZDnet
• Covid-19 in Victoria: bad, and according to modelling by Peter Ellis, still getting worse.  If you’re in NZ, make sure you have a mask and hand sanitiser available and at least have the contact apps on your phone, in case we get another outbreak.
• RadioNZ’s podcast “The Detail” has an episode on polling.  I’m reliably informed that I’m on it.
• In 2020, we’re amid that critical juncture for ’90s music—we can finally start asking today’s teens, “What music do you recognize from the ’90s?”. From pudding.cool

A company called Genderify has what they say is “an AI-powered tool for identifying the gender of your customers”. This is an example of something that is not worth doing (asking is easy and reliable; people will be upset when you get it wrong), but also very difficult.

After seeing some examples on Twitter, I decided to try it on some senior members of the Stats department (whose gender identity I’m reasonably confident of)

“Thomas Lumley” is 63.90% likely to be male and 36.10% likely to be female, and you have to like the four digit precision. But “Dr Thomas Lumley” is 89.40% likely to be male, and “prof thomas lumley” gets up to 94.60%!

“Ilze Ziedins” is 85.20% likely to be male, which will surprise her. “Dr Ilze Ziedins” gets to 96.00%

“James Curran” is 99.60% likely to be male, adding “Dr” or “Prof” gets him up to 99.90%

“Rachel Fewster” is at 72.00% likely to be male, adding her professorial title puts that up to 95.40%

“Renate Meyer” is at 62.30%, her doctorate moves that up to 88.20%, and her promotion to professor makes it 94.00%

Note that none of these are classically gender-neutral or gender-ambiguous names: no Hadley or Hilary or Cameron.  The overall level of accuracy is pretty terrible to start with — but the response to adding qualifications is bizarre.  If that wasn’t in the basic pre-release testing, then what was?

Even better (worse):  it’s not just that adding “Dr” or “Prof” make it think you’re more likely mean a man, adding “Dame” also does.

Update: on Twitter, (Dr, Prof) Casey Fiesler raised the possibility that Genderify are just trolling, which I must say is looking quite plausible.

It’s getting to be that time of the triennium again, so some highlights from past StatsChat posts on electoral polling

• Overview of polling and how it works
• What is the ‘margin of error’ for minor parties: one, two
• Why you can’t predict Epsom from polls
• Why polling is important: your friends aren’t representative
• Survey weighting: why polls don’t massively underestimate the Greens vote.
• The difference between 1 in 20 exceedance of the margin of error and a ‘rogue poll‘

Team Ratings for Round 5

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 8 matches played, 7 of which were correctly predicted, a success rate of 87.5%.
Here are the predictions for last week’s games.

Predictions for Round 5

Here are the predictions for Round 5. 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.