January 18, 2022

Currie Cup Predictions for Round 2

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.

Current Rating Rating at Season Start Difference
Bulls 7.95 7.25 0.70
Sharks 4.13 4.13 -0.00
Western Province 1.93 1.42 0.50
Cheetahs -2.30 -2.70 0.40
Lions -2.39 -1.88 -0.50
Pumas -4.01 -3.31 -0.70
Griquas -5.31 -4.92 -0.40

 

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.

Game Date Score Prediction Correct
1 Pumas vs. Bulls Jan 15 19 – 33 -6.10 TRUE
2 Cheetahs vs. Griquas Jan 15 30 – 20 6.70 TRUE
3 Western Province vs. Lions Jan 16 48 – 36 7.80 TRUE

 

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.

Game Date Winner Prediction
1 Lions vs. Pumas Jan 20 Lions 6.10
2 Sharks vs. Griquas Jan 20 Sharks 13.90
3 Western Province vs. Bulls Jan 20 Bulls -1.50

 

Briefly

  • 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

I’m a lumberjack and I’m ok

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.

 

January 14, 2022

How long to Omicron?

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.

January 13, 2022

Currie Cup Predictions for Round 1

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.

Current Rating Rating at Season Start Difference
Bulls 7.25 7.25 -0.00
Sharks 4.13 4.13 -0.00
Western Province 1.42 1.42 -0.00
Lions -1.88 -1.88 0.00
Cheetahs -2.70 -2.70 -0.00
Pumas -3.31 -3.31 -0.00
Griquas -4.92 -4.92 -0.00

 

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.

Game Date Winner Prediction
1 Pumas vs. Bulls Jan 15 Bulls -6.10
2 Cheetahs vs. Griquas Jan 15 Cheetahs 6.70
3 Western Province vs. Lions Jan 16 Western Province 7.80

 

January 12, 2022

Briefly

  • 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
January 11, 2022

United Rugby Championship Predictions for Week 11

Team Ratings for Week 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.

Current Rating Rating at Season Start Difference
Leinster 15.38 14.79 0.60
Munster 10.22 10.69 -0.50
Ulster 7.47 7.41 0.10
Edinburgh 3.77 2.90 0.90
Connacht 3.60 1.72 1.90
Glasgow 3.55 3.69 -0.10
Bulls 1.98 3.65 -1.70
Sharks 0.97 -0.07 1.00
Stormers 0.29 0.00 0.30
Ospreys 0.07 0.94 -0.90
Cardiff Rugby -1.61 -0.11 -1.50
Scarlets -1.72 -0.77 -1.00
Lions -1.80 -3.91 2.10
Benetton -3.39 -4.50 1.10
Dragons -6.12 -6.92 0.80
Zebre -16.61 -13.47 -3.10

 

Performance So Far

So far there have been 54 matches played, 40 of which were correctly predicted, a success rate of 74.1%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Edinburgh vs. Cardiff Rugby Jan 09 34 – 10 10.40 TRUE
2 Glasgow vs. Ospreys Jan 09 38 – 19 8.80 TRUE
3 Munster vs. Ulster Jan 09 18 – 13 8.30 TRUE

 

Predictions for Week 11

Here are the predictions for Week 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.

Game Date Winner Prediction
1 Lions vs. Sharks Jan 23 Lions 2.20
2 Bulls vs. Stormers Jan 23 Bulls 6.70

 

Top 14 Predictions for Round 16

Team Ratings for Round 16

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.

Current Rating Rating at Season Start Difference
Stade Toulousain 8.35 6.83 1.50
La Rochelle 8.04 6.78 1.30
Bordeaux-Begles 6.84 5.42 1.40
Lyon Rugby 5.14 4.15 1.00
Clermont Auvergne 4.90 5.09 -0.20
Racing-Metro 92 4.24 6.13 -1.90
Montpellier 3.55 -0.01 3.60
Castres Olympique 0.95 0.94 0.00
Stade Francais Paris 0.15 1.20 -1.10
RC Toulonnais -0.19 1.82 -2.00
Section Paloise -2.64 -2.25 -0.40
Brive -3.45 -3.19 -0.30
USA Perpignan -3.97 -2.78 -1.20
Biarritz -4.54 -2.78 -1.80

 

Performance So Far

So far there have been 98 matches played, 75 of which were correctly predicted, a success rate of 76.5%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Biarritz vs. USA Perpignan Jan 09 23 – 25 6.80 FALSE
2 Brive vs. Bordeaux-Begles Jan 09 19 – 22 -3.90 TRUE
3 Castres Olympique vs. Stade Francais Paris Jan 09 15 – 9 7.40 TRUE
4 Lyon Rugby vs. Section Paloise Jan 09 35 – 10 13.50 TRUE
5 Racing-Metro 92 vs. Clermont Auvergne Jan 09 33 – 28 5.90 TRUE

 

Predictions for Round 16

Here are the predictions for Round 16. 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.

Game Date Winner Prediction
1 Bordeaux-Begles vs. Castres Olympique Jan 30 Bordeaux-Begles 12.40
2 Brive vs. Biarritz Jan 30 Brive 7.60
3 La Rochelle vs. Montpellier Jan 30 La Rochelle 11.00
4 Stade Toulousain vs. Racing-Metro 92 Jan 30 Stade Toulousain 10.60
5 USA Perpignan vs. Lyon Rugby Jan 30 Lyon Rugby -2.60
6 Section Paloise vs. Clermont Auvergne Jan 31 Clermont Auvergne -1.00
7 Stade Francais Paris vs. RC Toulonnais Jan 31 Stade Francais Paris 6.80

 

Rugby Premiership Predictions for Round 14

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.

Current Rating Rating at Season Start Difference
Exeter Chiefs 4.76 7.35 -2.60
Saracens 3.91 -5.00 8.90
Leicester Tigers 2.01 -6.14 8.10
Harlequins 1.65 -1.08 2.70
Wasps 1.49 5.66 -4.20
Sale Sharks 1.33 4.96 -3.60
Gloucester 0.87 -1.02 1.90
Northampton Saints 0.01 -2.48 2.50
Bristol -1.85 1.28 -3.10
London Irish -4.05 -8.05 4.00
Bath -5.38 2.14 -7.50
Newcastle Falcons -6.83 -3.52 -3.30
Worcester Warriors -9.54 -5.71 -3.80

 

Performance So Far

So far there have been 76 matches played, 38 of which were correctly predicted, a success rate of 50%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Bristol vs. Sale Sharks Jan 08 32 – 15 -0.50 FALSE
2 Harlequins vs. Exeter Chiefs Jan 09 14 – 12 1.30 TRUE
3 Newcastle Falcons vs. Northampton Saints Jan 09 8 – 44 1.10 FALSE
4 Saracens vs. Gloucester Jan 09 24 – 25 8.60 FALSE
5 Bath vs. Worcester Warriors Jan 10 22 – 19 9.40 TRUE
6 Wasps vs. Leicester Tigers Jan 10 16 – 13 4.20 TRUE

 

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.

Game Date Winner Prediction
1 Bath vs. Harlequins Jan 29 Harlequins -2.50
2 London Irish vs. Exeter Chiefs Jan 30 Exeter Chiefs -4.30
3 Newcastle Falcons vs. Gloucester Jan 30 Gloucester -3.20
4 Worcester Warriors vs. Northampton Saints Jan 30 Northampton Saints -5.10
5 Sale Sharks vs. Leicester Tigers Jan 31 Sale Sharks 3.80
6 Wasps vs. Saracens Jan 31 Wasps 2.10

 

Why screening is hard

The governor of Florida, Ron DeSantis was widely quoted last week as saying

 “Before COVID did anyone go out and seek testing to determine if they were sick? It’s usually you feel like you’re sick and you get tested to determine what you maybe have come down with.” 

As most people will know, the answer is “yes, they did”.  You can’t get near a doctor without having your blood pressure measured, because high blood pressure is common, not obvious without testing, and treatable.  There are tests performed on infants (for genetic disorders such as PKU and cystic fibrosis), on children (vision and hearing screening), and on the middle-aged and old (cholesterol, glycated hemoglobin, cancer screening). There are tests mainly done for high-risk groups (TB, HIV); there are tests done before you get certain medications (liver or kidney function).   Until 1986, Florida required a test to see if you had syphilis in order to get a marriage license.  Governor DeSantis has since said that of course he knew all that, and it was obvious he had specifically meant daily or weekly testing for a viral respiratory infection was unprecedented, not all the normal and routine ways people seek out testing to determine if they are sick. And who knows? Maybe he did mean that.

In some ways there’s surprisingly little population screening.  Screening has traditionally been popular, because people like knowing things and having explanations.  People often argue for more or say they have discovered a way to do more. There are three big barriers in going from a test you take when you feel like you’re sick and one you give to people in advance.

The first barrier is that you have to be able to do something with the result. There’s no point in taking healthy people and finding some illness unless you can do something about it.  In the endemic-Covid testing setting,  you can isolate and not go infect your workmates or your grandparents or the blokes at the pub or whoever.   In the 2004 SARS outbreak we didn’t have rapid antigen tests, but temperature screening was used to find people who might not realise they had SARS or weren’t looking to find out for some other reason.

The second barrier is the base rate problem. The New York Times had a very good story recently about prenatal genetic testing. This looks for very rare genetic disorders, usually as add-ons when testing for Down’s Syndrome. Because those disorders are very rare, most fetuses don’t have them — even most of those who test positive.  The Times reported on a set of tests where a positive test had an 80% or higher chance of being wrong.  In one sense these tests are very accurate — a negative result is very likely to be correct– but just assuming no-one has these conditions is almost as likely to be correct and has no false positives.  The base-rate problem goes together with the problem of what to do; follow-up tests are expensive.

The base rate problem is also an issue for Covid testing here in New Zealand; in contrast to many parts of the world, we currently have very low community prevalence, so a rapid antigen test positive in someone without symptoms or known exposure would very likely be a false positive.  In Victoria, the opposite is true: the base rate is high enough that the only rapid antigen tests where PCR follow-up is recommended are those in asymptomatic people with no known exposure.

The third barrier is Rose’s Prevention Paradox: for a lot of diseases, most of the cases don’t happen in high-risk people. There are people at very high risk of heart attack (what Rose was interested in), but they account for a relatively small fraction of all heart attacks.  There are people at very high risk of premature birth, but they account for a relatively small fraction of all premature births.  Someone with a blood alcohol of 0.2 has a very high risk of getting in a crash, but most car crashes aren’t like that.

Unlike the base-rate problem, Rose’s Prevention Paradox isn’t universal.  Testing for the common CFTR mutations will pick up most cases of cystic fibrosis; the Ishihara plates pick up most cases of deficient colour vision; most lung cancer is in smokers; most liver cancer in Western countries is in heavy drinkers or people with Hepatitis B or C.

The Covid example of the prevention paradox is the recent and controversial CDC announcement that most (vaccinated) people who get seriously ill from Covid have co-morbidities.  On top of the issue of whether that’s actually a cause for rejoicing, there’s the problem that the majority people who don’t get seriously ill from Covid also have co-morbidities. That is, it’s hard to pick out high risk people.  The CDC defined ‘high risk’ very broadly, so that too  many people are ‘high risk’; if they define it too narrowly, they would miss a lot of the serious Covid cases.

Very early in the pandemic, the estimate was that a Covid case gave you roughly a year’s worth of mortality risk — if everyone got Covid over a period of one year, death rates for that year would be double what they normally are, across a wide range of subgroups.  Omicron is worse than the original strain, but the vaccine helps a lot: vaccinated people are much less likely to die or become hospitalised.  Not getting Covid helps even more; masks, ventilation, distancing, testing, etc.