Stats Chat

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 112 matches played, 62 of which were correctly predicted, a success rate of 55.4%.
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 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 15 matches played, 12 of which were correctly predicted, a success rate of 80%.
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.

This came from Twitter (arrows added). I don’t have a problem with the basic message, that when people are packed into a smaller area it takes less energy for them to get around, but there are things about the graph that look a bit idiosyncratic, and others that just look wrong

The location of the points comes from an LSE publication that’s cited in the footnote, which got it from a 2015 book, using 1995 data (data not published).  The label on the vertical axis has been changed — in both the sources it was “private passenger transport energy use per capita”, so excluding public transport — and the city-size markers have been added.

One thing to note is that you could almost equally well say that transport energy use depends on what continent you’re in: the points in the same colour don’t show much of a trend.

Two points that first really stood out for me were San Francisco (lower population density than LA) and Wellington (higher population than Frankfurt, Washington, Athens, Oslo; same general class as Manila and Amsterdam).   In this sort of comparison it makes a big difference how you define your cities: is Los Angeles the local government area or the metropolis or something in between? In this case it’s particularly important because the population data were added in by someone else to an existing graph.

In some cases we can tell. Melbourne must be the whole metropolitan area (the thing a normal person would call ‘Melbourne’), not the small municipality in the centre.  The book gives the density for Los Angeles on a nearby page as the “Los Angeles–Long Beach Urbanized Area”, which is (roughly speaking) all the densely populated bits of Los Angeles County. Conversely, San Francisco looks to be the whole San Francisco-Oakland Urbanized Area, which has rather lower density than what you’d think of as San Francisco. The circle looks wrong, though: the city of San Francisco is small, but the San Francisco area has a higher population than Brisbane or Perth.

The same happens in other countries. Manila, by its population, should just be the city of Manila, but that had a population density of 661/ha in 1995 so the density value is for something larger than Manila but smaller than the whole National Capital region (which had a density of 149/ha and a population of 9.5 million).  If it’s in the right place on the graph, its bubble should be bigger. The time since 1995 also matters: Beijing is over 20 million people now, but was under 10 million at the time the graph represents. We’ve seen that the San Francisco point is likely correct, but the size is probably wrong.  The same seems to be true for Wellington: the broadest definition of Wellington will give you a smaller population than the narrowest definition of Washington or Frankfurt.

As I said at the beginning, I don’t think the basic trend is at all implausible. But when you have data points that are as sensitive to user choice as these, and when the size data and density data were constructed independently and don’t have clearly documented sources, it would be good to be confident someone has checked on whether Manila really has the same population as Wellington and San Francisco is really less dense than LA.

When we look at the impact of particular government strategies in Covid, it’s important to compare them to the right thing.  The right comparison isn’t, for example, pandemic with lockdowns vs no pandemic — ‘no pandemic’ was never one of the government’s options. The right comparison is pandemic with lockdowns vs pandemic with some other strategy.

Along these lines, Stuff has a really unusual example of a heading that massively understates what’s the in the story. The headline says Covid-19: Pandemic measures saved 2750 lives, caused life expectancy to rise, based on a blog post by Michael Baker and his Otago colleagues. As you find if you read on, the actual number is more like 17,000 or 23,000 (or even higher).

The 2750 is the difference between the number of deaths we’ve seen during the pandemic period and the number we’d expect with no pandemic measures and also no pandemic.  The fair comparison for the impact of pandemic measures isn’t this, it’s the comparison to what we’d expect with a pandemic and the sort of pandemic measures used in other countries.   According to Prof Baker, we are at minus 2750 excess deaths per 5 million people, the US is at about 20000 excess deaths per 5 million people and the UK at about 13700 excess deaths per 5 million people.  The difference: 13700- -2750 or 20000- -2750 is the impact of having our pandemic measures instead of theirs.

There’s room to argue about the details of these numbers.  The UK is more densely populated than NZ and was run by Boris Johnson, so you might argue that the UK deaths were always going to be worse . Alternatively, the UK and US have more capacity in their medical systems than NZ, so you might argue that NZ deaths with a similar outbreak would have been worse. What’s important, though, is to compare our choices with other choices New Zealand could have made. No pandemic wasn’t one of those options.

• Like a lot of news outlets, the Herald and  Newshub reported the case of a US teen needing amputations after eating some dodgy leftover lo mein.  Fortunately or unfortunately, it’s not true — well, the “after” is true, but the implied “because of” isn’t. The victim had meningoccal disease, which isn’t foodborne, and the connection came only from YouTube.  As the Boston Globe and Ars Technica report “The article never mentioned the leftovers again—because the food wasn’t linked to his illness. The lo mein was simply a red herring that the doctors dismissed, according to the article’s editor and director of the clinical microbiology laboratory at Massachusetts General Hospital, Eric Rosenberg.”
• As Siouxsie Wiles says in Stuff, we could really use a Covid prevalence survey now that case counts aren’t a reliable way to assess infection numbers and allow hospitals to predict what they’re going to see in a week or so.  As a stopgap, we could use various existing data sources to cobble together an estimate, but a proper random survey like the one the UK has been running would be better.  The UK is stopping theirs; the president of the Royal Statistical Society writes about why this is bad
• Interesting US political research: I’ve mentioned quite a few times that opinion polls have a problem with the difference between what people believe and what they say.  This research looked at people who say they believe the 2020 US election was really won by Donald Trump, and concludes that most of them actually do believe it.
• On the other hand, YouGov finds substantial differences between the proportion of people who support vaccine mandates for schoolkids and the proportion who think “parents should be required to” have their children vaccinated.

Team Ratings for Week 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 10 matches played, 7 of which were correctly predicted, a success rate of 70%.
Here are the predictions for last week’s games.

Predictions for Week 3

Here are the predictions for Week 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.

Team Ratings for Week 17

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 80 matches played, 56 of which were correctly predicted, a success rate of 70%.
Here are the predictions for last week’s games.

Predictions for Week 17

Here are the predictions for Week 17. 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 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 129 matches played, 94 of which were correctly predicted, a success rate of 72.9%.
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 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 106 matches played, 56 of which were correctly predicted, a success rate of 52.8%.
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.

Stuff (from the Sydney Morning Herald) reports that a baby was born at exact 2:22 and 22 seconds on February 22nd. An Australian maths professor is quoted as saying

“It’s about 1 in 30 million is the chance of being born at that precise second,”

Maths nerds will recognise that as the number of seconds in a year (roughly π times ten million), and music nerds will remember 525,600 as the number of  minutes in a year and multiply by 60.  So, 1 in 30 million is the chance of picking a particular second if you pick one randomly from a year. It seems a bit strange to give that as the answer for the baby’s chance of being born at that precise time.

If you had picked this specific baby, Bodhi, in advance, his chance of being born at a particular second depends on how much variation there is in birth times.  They’re roughly a Normal distribution with a standard deviation of 16 days, and it turns out this gives a chance of about 2.5% of being born five days early and so about one in 3.6 million of being born in a particular second on that fifth day early.

But we didn’t pick this specific baby in advance and look at when he was born. We picked the time and looked for the baby. There are nearly 300,000 births per year in Australia; about one per 100 seconds.  There would be about a 1 in 100 chance that some baby in Australia is born at one particular second.

So far we’ve been saying there’s one particular second. But the baby was born at 14:22:22 and presumably 02:22:22 would have done just as well. Or maybe 22:02:22 or 22:22:22.  It’s not really one special second in the day.  And on top of that, a baby isn’t really born at a single second — there’s at least a small amount of flexibility in how you define the time, and you know someone is going to take advantage of the flexibility.  What would be really surprising would be a birth recorded as 2:22:19pm on 22/2/2022.