Posts filed under General (3148)

Team Ratings for Round 21

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 152 matches played, 109 of which were correctly predicted, a success rate of 71.7%.
Here are the predictions for last week’s games.

Predictions for Round 21

Here are the predictions for Round 21. 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 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.

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 18 matches played, 9 of which were correctly predicted, a success rate of 50%.
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.

You might have seen, on social media (or asocial or antisocial media) claims that the Delta variant of Covid can be spread by vaccinated people just as easily as unvaccinated people.  It’s not true, but if you strip out the two big reasons it’s not true, what’s left is still worrying. Here’s two relatively careful stories: WaPo, NYTimes.

We know that vaccination dramatically reduces the chance that you’ll spread Covid to someone else by dramatically reducing the chance you’ll be infected if you’re exposed.  Vaccination of people you come into contact with also reduces the chance you’ll be exposed, because they are less likely to be infected.

If vaccination reduces your chance of infection with Delta by 80%, it’s going to reduce your chance of transmitting Delta by around 80%. Reducing the uncertainty on that number is important in public health planning: the chance of transmitting Delta affects how much community protection we get from a given vaccination rate, and so affects what other precautions (MIQ, lockdown, masks, etc) need to be taken to get to an acceptable level of risk.  For example, the modelling of community protection by researchers at Te Punaha Matatini had a baseline assumption that ‘breakthrough’ infections were half as likely to transmit the disease as infections in unvaccinated people (though they also used a lower estimate of vaccine effectiveness in preventing infection than I think we’d use now, so it cancels out to some extent).

Estimating ‘secondary transmission’ is hard. Ideally, you’d trace all the contacts of each infected person and determine how many people they actually transmitted the virus to.  In practice, that won’t work.  In countries like Australia and New Zealand we don’t have enough free-range infections (or vaccination) to get reliable quantitative estimates information. Somewhere like the US or Britain, where you can get a sample of hundreds of cases, you can’t easily track down who infected whom.  There’s some information from comparing high and low vaccination regions in a country such as Israel, and from cluster-randomised trials that vaccinate whole communities at once, but not enough.

Logically, breakthrough infections might be about the same as unvaccinated infections (an infection is an infection), or less transmissible (your immune system reduces the viral load) or even more transmissible (only the people who are especially susceptible get infected).  Reason unaided won’t get us any further; we need data.

One approach is to estimate the transmission from the amount of virus people are shedding.  This roughly works — viral load explains the extra transmissibility of Delta.  If we find that ‘breakthrough’ infections shed a lot less virus, they’re probably less transmissible; if they shed about the same, they’re probably about the same.  According to the CDC, they’re about the same.  This doesn’t mean the vaccine has no effect on viral load — it could easily be that the people who get breakthrough infections would have had higher than average viral load without the vaccine, and the vaccine has reduced it to only average. It doesn’t mean that vaccination isn’t preventing infections — vaccination absolutely is.  It does mean the relationship between number of cases walking  around in the population and risk of new infections is about the same.  Knowing this will allow better estimates of population risk and better choices of precautions.

A graph about the current salary negotiations for nurses, tweeted by Andrew Little, the Minister of Health:

There are many situations when it is entirely proper to draw a graph with a y-axis starting somewhere other than zero.  There are essentially no situations where a bar chart should have the axis starting somewhere other than zero (the very occasional exception is when ‘zero’ basically is a number other than zero).  There’s a reason for this: in a bar chart, the area (length) of the bar conveys the information, and cutting the feet out from under the bar changes the information.

That’s all very well and good, you say, but is there empirical evidence that real people are misled by truncated bar charts? I’m glad you asked! Yes, there was a research paper published last year, titled “Truncating Bar Graphs Persistently Misleads Viewers”, which found …well, what it says on the label.  A truncated graph was misleading; it was still misleading for graphically-sophisticated nerds; and it was still misleading when accompanied by a warning. Truncated bar charts are bad. Don’t use them.

Sticking the missing $30,000 into the bottom of the Minister’s graph gives this:

• Queueing theory is a branch of applied probability and so is StatsChat relevant. Tava Olsen, a professor in the UoA business school, was interviewed on RadioNZ about the MIQ booking system and wrote for The Spinoff.   (disclaimer: I recommended her to RadioNZ)
• Matt Nippert writes in the Herald about Pharmac and — unusually for a story about Pharmac — looks at the tradeoffs involved in what they choose to fund.
• Via Axios: JAMA, the medical journal, requested revisions to the research paper with data supporting approval of aducanumab for Alzheimer’s disease. That’s pretty standard.  Apparently the company said “Nope” and will look for a different journal.  This isn’t unheard of — sometimes, reviewers are just wrong and you try another journal — but it is another unusual occurrence.
• Mediawatch reported that economic forecasts are often wrong.  That’s not really surprising: economics says that (a) recessions are unpredictable and (b) if economists benefit from their forecasts being mentioned in the news they will tend to produce newsworthy forecasts. I suggested that the forecasts should come with uncertainty intervals, so we have some ability to tell if they’re bad at forecasting or it’s just that the economy is uncertain.

Team Ratings for Round 20

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 144 matches played, 104 of which were correctly predicted, a success rate of 72.2%.
Here are the predictions for last week’s games.

Predictions for Round 20

Here are the predictions for Round 20. 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 8

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 15 matches played, 9 of which were correctly predicted, a success rate of 60%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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 7

I got caught out with the Round 6 games being played midweek so missed out on predicting one game prior to it being played. As always my predictions are not altered even though made after the game has been played. The Currie Cup is proving difficult to predict however.

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 12 matches played, 6 of which were correctly predicted, a success rate of 50%.
Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 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 19

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 136 matches played, 97 of which were correctly predicted, a success rate of 71.3%.
Here are the predictions for last week’s games.

Predictions for Round 19

Here are the predictions for Round 19. 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.