Stats Chat

Team Ratings for Round 15

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, 47 of which were correctly predicted, a success rate of 61%.
Here are the predictions for last week’s games.

Predictions for Round 15

Here are the predictions for Round 15. 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 the Final and Postponed Matches

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 94 matches played, 63 of which were correctly predicted, a success rate of 67%.
Here are the predictions for last week’s games.

Predictions for the Final and Postponed Matches

Here are the predictions for the Final and Postponed Matches. 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 3

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, 13 of which were correctly predicted, a success rate of 81.2%.
Here are the predictions for last week’s games.

Predictions for Round 3

Here are the predictions for Round 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.

Remember the bin lid?

Case C tested positive in MIQ at their day-12 test. They had a room next door to D and D’s child, E. D is the person who was initially thought to have been infected by touching a bin lid that C had also touched some 20 hours before.

There’s now a published paper by Jemma Geohegan and numerous co-workers, and Siouxsie Wiles has an explainer at the Spinoff:

Now we know that SARS-CoV-2 is airborne, a much more likely scenario is that case D was infected by exposure to airborne virus shed by case C. 

CCTV footage showed that C and D were never outside their MIQ rooms at the same time. But they both got their day 12 swabs taken on the same day. And these were taken from their doorways. C was tested first. Then there was a 50-second window between them closing their door and D opening theirs to be swabbed. It looks like having the hotel room doors open for as long as it took to be swabbed was enough to move airborne virus from C and their room into the enclosed and unventilated corridor and then on to D and into their room. From the genome sequencing, it looks like D then infected their child, E, and another household member, F. 

This might seem strange: we know there was a contaminated bin lid, and the opportunity for airborne^* transmission was pretty marginal, so why are we concluding it was airborne^*?

Back last year, scientists and public health people worked on the assumption that the Covid virus could spread via contaminated surfaces, because lots of microbes do and it’s a relatively easy fix.  Some of us went as far as washing individual tomatoes when we got them back from the supermarket.  It became clear fairly quickly that airborne^*  transmission was important; there were some smoking-gun examples that couldn’t be explained any other way.  It took a lot longer to decide that contaminated surfaces were pretty much never important, because most cases that could have been surface transmission could also have been airborne^* transmission. Still, there were no smoking-gun cases of transmission by contaminated surfaces, and you’d expect a few unless it was very rare.

As the numbers of active infections rose, it became harder to track down causes for any individual case.  Except, in New Zealand, it didn’t.  Since the end of the first outbreak we have had a very small number of cases, small enough that contact tracing and genome sequencing is possible for every person involved, plus the sort of hotel surveillance that’s unusual in the public sector outside prisons.  Here, it is possible to track most individual transmissions. So, the ‘bin lid’ case was interesting because it looked like an unambiguous case of transmission from a contaminated surface.

What we know now is that it wasn’t. That is, it wasn’t an unambiguous case of transmission from a contaminated surface; it could have been airborne^*  transmission.  The ‘bin lid’ case

• definitely isn’t the smoking gun for surface transmission that it first appeared
• and so, based on everything else we know, probably was airborne^*  transmission

It’s not that we have strong evidence for airborne^*  transmission from the facts of this specific case, but we don’t have strong evidence against it, and other evidence definitely points that way.

* for some definition of the word, let’s not be dicks about it.

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 133 matches played, 89 of which were correctly predicted, a success rate of 66.9%.
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 Week 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 14 matches played, 10 of which were correctly predicted, a success rate of 71.4%.
Here are the predictions for last week’s games.

Predictions for Week 5

Here are the predictions for Week 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.

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 71 matches played, 41 of which were correctly predicted, a success rate of 57.7%.
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.

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.

Performance So Far

So far there have been 88 matches played, 59 of which were correctly predicted, a success rate of 67%.
Here are the predictions for last week’s games.

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.

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.

Performance So Far

So far there have been 8 matches played, 8 of which were correctly predicted, a success rate of 100%.
Here are the predictions for last week’s games.

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.

Q: Did you see 90% of Covid deaths are due to obesity?

A: No

Q: It’s on Newshub: “90 percent or 2.2 million of the 2.5 million deaths from the pandemic disease so far were in countries with high levels of obesity.”

A: So, if that’s true, what countries are we talking about?

Q: With high levels of obesity? Australia, New Zealand, some of the Pacific Island nations, USA, UK,…

A: And what don’t those countries have in common?

Q: Lots of Covid deaths, I suppose.  But Australia and NZ and the Pacific Island countries didn’t have many deaths just because they didn’t have many cases. They don’t tell you anything about the risk

A: Neither does the ‘report’, which says, according to Newshub, “the majority of global COVID-19 deaths have been in countries where many people are obese, with coronavirus fatality rates 10 times higher in nations where at least 50 percent of adults are overweight”

Q: That sounds bad, though? Ten times higher is a lot!

A: How much do you think being overweight increases the risk of death?

Q: Well, if 50% overweight means ten times the risk, maybe a factor of twenty?

A: Yeah nah. Less than double, according to that very same report. (PDF, p13)

Q: So where do they get such a strong association?

A: Well, what are the main risk factors for dying of Covid?

Q: Um. Age?

A: And?

Q: Heart disease? Diabetes?

A:

Q:

A:

Q: Having Covid?

A: Exactly.  So we’d need to look at the proportion of infected people dying (the case fatality rate), not the total number of deaths, and we’d ideally want to compare countries with similar age profiles. Instead, Newshub shows us a graph of deaths, basically like this

Q: Why are some of the dots black?

A: They are countries that were missing from the Johns Hopkins Covid data, and so were missing from the original graph, mostly small nations like Samoa and Tonga and Timor-Leste with very few cases

Q: And what’s the dot on the left that isn’t in the graph on Newshub?

A: Afghanistan. I don’t know why it’s not there.

Q: So what about for risk of dying if you get Covid?

A: Here’s what it looks like for case fatality rate

Q: That’s…surprisingly unimpressive. Well, except for that one country…

A: Yemen

Q: Things they don’t need right now, number #1

A:  Too true.

Q: So the massive correlation?

A: Well, overweight is common in the USA, the UK, Brazil, and various other large countries that have handled the pandemic badly. While I suppose you could argue that handling the pandemic well should go with handling other public health issues well, the empirical evidence would not really be in your favour on that one.  You could also listen to a slightly different approach to the data from David Spiegelhalter and Tim Harford on BBC’s “More or Less”