Stats Chat

Team Ratings for Week 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 69 matches played, 53 of which were correctly predicted, a success rate of 76.8%.
Here are the predictions for last week’s games.

Predictions for Week 10

Here are the predictions for Week 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.

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 84 matches played, 62 of which were correctly predicted, a success rate of 73.8%.
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.

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 57 matches played, 37 of which were correctly predicted, a success rate of 64.9%.
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.

Team Ratings for Week 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 61 matches played, 46 of which were correctly predicted, a success rate of 75.4%.
Here are the predictions for last week’s games.

Predictions for Week 9

Here are the predictions for Week 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 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, 56 of which were correctly predicted, a success rate of 72.7%.
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 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 53 matches played, 34 of which were correctly predicted, a success rate of 64.2%.
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.

From the Guardian: Computer says there is a 80.58% probability painting is a real Renoir. The story goes on to say Dr Carina Popovici, Art Recognition’s CEO, believes that this ability to put a number on the degree of uncertainty is important.

It’s definitely valuable to put a number on the degree of uncertainty. What’s much less clear is that it’s valuable to put a number on the uncertainty to four-digit precision.   Let’s think about what it would take to be that precise.

If the 80.58% number was estimated from a proportion of observed data in some sense, quoting it to four digits would only make sense if the uncertainty was less than about 0.05%.  A standard error of 0.05% would need a sample size of more than five hundred million.

Another way you can get an estimate with high precision is including subjective expert opinion, which would be entirely appropriate in a context like this. There’s no limit to how precise this can be for the person whose opinion it is — you believe exactly what you believe — but there are very strong limits on how precise it can realistically be as a guide to others.  If the computer isn’t the one buying the Renoir, other people probably shouldn’t care about its opinion to more than one or two digits accuracy.

Sometimes when you come up with an estimate you want to quote it to higher precision than is directly useful — lots of statistical software, including some I write, quotes four or more digits in the default output. This allows rounding to happen closer to the point of use, such as before it’s in a headline in the mainstream media.

From Hannah Martin at Stuff: Only 35% of Covid cases being reported, ministry says, after earlier saying it was 75%

The ministry’s latest Trends and Insights report, released on Monday, said “approximately three quarters of infections are being reported as cases”, based on wastewater testing.

However, it has since said that “based on updated wastewater methodology”, about 35% of infections were reported as cases as of the week to November 2.

This is a straightforward loss during communication:  the 75% was an estimate of how much the reporting had changed since the first Omicron peak, but it got into the Trends and Insights report as an absolute rate.  Dion O’Neale is quoted further down the story explaining this.

For future reference, it’s worth looking at what we can and can’t estimate well from various sources of information we might have.

The wastewater data has the advantage of including everyone in a set of cities and towns, adding up to the majority of the country; everybody poops. It has the disadvantage of not directly measuring cases or infections.  The wastewater data tells us how many Covid viral fragments are in the wastewater.  How that relates to infections depends on how many viral fragments each person sheds and how many of these make it intact to the collection point.  This isn’t known — it’s probably different for different people, and might depend on vaccination and previous infections and which variant you have and age and who knows what else. However, the population average probably changes slowly over time, so if the number of viral fragments is going up this week, the number of active infections is probably going up, and  if the number of viral fragments is going down, the number of active infections is probably going down.

Using the wastewater data, we can see that the ratio of reported cases to wastewater viral fragments has been going down slowly since the first Omicron peak.  We’ve got a lot of other reasons to think testing and reporting is going down, so that’s a good explanation. It’s especially a good explanation because most of the other reasons for a change (eg, less viral shedding in second infections) would make the ratio go up instead.  So, with the ratio of reported cases to viral fragments going down by 25% it makes sense to estimate that the ratio of reported cases to infections has gone down 25%.

Now all we need is to know what the reporting rate was at the peak. Which we don’t know. It couldn’t have been much higher than 60%, because some infections won’t have been symptomatic and some tests will have been false negatives.  If it was 60%, it’s down to roughly 40% now.  If it was lower than that at the peak, it’s lower than 40% now.  You could likely get somewhat better guesses by combining the epidemic models and the wastewater data, but it’s always going to be difficult.

You might think that hospitalisation and death data are less subject to under-reporting. This is true, but the proportion of infections leading to hospitalisation is (happily) going down due to vaccination and prior infection, and the proportion leading to death is (happily) going down even more due to better treatment.  On top of those changes, hospitalisation and death lag infection by quite a long time. The hospitalisation rate and death rate are directly important for policy, but they aren’t good indicators of current infections.

So, we’re a bit stuck. We can detect increases or decreases in infections fairly reliably with wastewater data, but absolute numbers are hard.  This is even more true for other diseases — in the future, there will hopefully be wastewater monitoring for influenza and maybe RSV, where we expect the case reporting rate to be massively lower than it is for Covid.

To get good absolute numbers we need a measurement of the actual infection rate in a random sample of people. That’s planned — originally for July 2022, but the timetable keeps slipping. A prevalence survey is a valuable complement to the wastewater data; it gives absolute numbers that can be used to calibrate the more precise and geographically detailed relative numbers from the wastewater.  Until we have a prevalence survey, the ESR dashboard is a good way to get a feeling for whether Covid infections are going up or down, and how fast.

Some years ago, at the behest of Kiwi Nerd Twitter, I looked at whether the Roy Morgan poll results varied more than those from other organisations, and concluded that they didn’t. It was just that Roy Morgan published polls more often. They had a larger number of surprising results because they had a larger number of results.  Kiwi Nerd Twitter has come back, asking for a repeat.

I’m going to do analyses of two ways of measuring weirdness, for the major and semi-major parties. All the data comes from Wikipedia’s “Opinion polling for the next NZ Election“, so it runs from the last election to now.  First, I’ll look at National.

The first analysis is to look at departures from the general trend.  The general trend for National (from a spline smoother, fitted in R’s mgcv package, in a model that also has organisation effects) looks like this:

Support was low; it went up.

I subtracted off the trend, and scaled the departures by the margin of error (not the maximum margin of error). Here they are, split up by polling organisation

The other analysis I did was to look at poll-to-poll changes, without any modelling of trend. The units for these are just percentage points.

Next, the same things for Green Party support: departures from their overall trend

And poll-to-poll differences

For ACT:

And finally for Labour

So, it’s complicated. The differences are mostly not huge, but for the Greens and Labour there does seem to be more variability in the Roy Morgan results. For National there isn’t, and probably not for ACT.  The Curia polls are also more variable for Green but not for Labour.  I think this makes Roy Morgan less weird than people usually say, but there does seem to be something there.

As an additional note, the trend models also confirm that the variance of poll results is about twice what you’d expect from a simple sampling model. This means the margin of error will be about 1.4 times what the pollers traditionally claim: about 4.5% near 50% and about 1% near the MMP threshold of 5%

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 49 matches played, 31 of which were correctly predicted, a success rate of 63.3%.
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.