Posts from May 2019 (27)

• From Stuff, an interactive visualisation of homicides in New Zealand, which also lets you do your own comparisons. For example, this one shows that alcohol is most often involved when people are killed by a friend
  
• From the LA Times, a visualisation mapping the settlements of the Tongva, who lived in Los Angeles before it was colonised.
• Previously the questions raised about mining in Foulden Maar haven’t been StatsChat-relevant, but this from Newsroom is. It says animal nutrition specialists are skeptical that the diatomaceous earth is actually of any benefit as an animal food, and there doesn’t appear to be any published research supporting its use. The story also says one of the selling points of the earth is that it contains fulvic acid, which I can recognise as a popular pseudoscience topic (when consumed; it’s a perfectly good soil component).
• Sometimes researchers do try to fight back when their work is exaggerated in the media: here’s an example on Twitter
• A well-sourced Twitter thread from Russell Brown, on how saliva testing to see if drivers are impaired by cannabis is nowhere near ready for use. Note that it’s not even fit for purpose if you’re trying to detect cannabis use, let alone for detecting impairment.
• Epidemic simulations on a network, letting you fiddle with various characteristics of the disease/meme/whatever

Team Ratings for Round 10

The basic method is described on my Department home page.

Round 9 games all being played in Brisbane caused problems for me again. I had changed the order of the teams for the Sea Eagles versus Broncos game to give them home ground advantage which then impacted my routines for webscraping results and adding them to my database. I thought I had corrected the resulting errors but missed one. Since that affected the updated ratings and the predictions for Round 10, I have updated the whole post.

Thanks to Chad James for advising me of the error.

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 72 matches played, 52 of which were correctly predicted, a success rate of 72.2%.
Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 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 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 85 matches played, 57 of which were correctly predicted, a success rate of 67.1%.
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 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 72 matches played, 53 of which were correctly predicted, a success rate of 73.6%.
Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 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.

• Some very good science reporting from Dylan Cleaver (traditionally a sports reporter) in the NZ$ Herald, on what’s known and what’s controversial in sports brain injury. The last of the three-part series is here.
• And explaining colds and flu and the immune system in The Age (Melbourne), co-written by a PhD student and the newspaper’s science reporter.
• Twitter bot @BeehiveBanter gives daily summaries of the most common words used in Parliament
• This went moderately viral on Twitter
  
  There’s obviously something wrong with these numbers — in fact, food is one of the things that’s gotten cheaper in real terms.  Some follow-up shows that the income is inflation-adjusted and the bread prices aren’t.
• From StatsNZ How wealthy are you compared with households like you? Find out with our new app: statisticsnz.shinyapps.io/wealth/
• Good explanation from the Grattan Institute on a misleading Australian housing statistic that keeps recurring

Q: Did you see that drinking wine could prevent colds?

A: <skeptical look>

Q: It’s on Newshub. Feel a winter cold coming? News study shows wine could be antibacterial

A: And how would it help if wine was antibacterial?

Q: It could kill the bacteria that….. oh… viruses?

A: Viruses. And where do you think the cold viruses live?

Q: Um. In your nose?

A: Ever got wine up your nose?

Q: Ok. Point. But later in the story it says “it’s not the alcohol or the acid that kill the bugs but rather the antibacterial properties of both red and white wine that help fight off both tooth plaque and sore throats.” So at least they’ve shown it works for plaque and sore throats

A: Not exactly

Q: In mice?

A: Not even.

Q: It’s bacteria in a dish in the lab, then?

A: Yes.

Q: Do bacteria even have teeth? And where did you find the research?

A: Basically same story was in the Edinburgh News and Maxim and Metro UK, and they all linked. Compounds in the wine kill some of the same bacteria that cause plaque and sore throats.

Q: That could be useful. How long did it take?

A: An hour.

Q: That’s a long time to gargle wine. Do they know what compounds in the wine have the effect? Is it these polyphenols and antioxidants we keep hearing about

A: They say it’s succinic, malic, lactic, tartaric, citric, and acetic acids, and that the polyphenols didn’t show any effect.

Q: But wasn’t it supposed to not be the acids?

A: It’s not just the acidity: the equivalent acidity from hydrochloric acid didn’t kill the bacteria.

Q: So it might not be directly useful, and it’s not a good reason to drink wine, but it’s still interesting new research?

A: The research was published in 2007. I’ve got no idea why it has resurfaced. Maybe that’s why the Newshub headline says “news study”, not “new study”

Q: Seems to reinforce the official StatsChat policy on wine, though.

A: If you’re drinking it primarily for the health benefits, you’re doing it wrong.

Team Ratings for Round 9

The basic method is described on my Department home page.

This is an unusual round in that all games are being played at Suncorp Stadium which is the home ground of the Brisbane Broncos. Hence they have been assigned home ground advantage. All other games are treated as being played at a neutral ground. The exception is the Warriors versus the Dragons where I have penalised the Warriors because they are playing overseas.

Thanks to Ben Frank for alerting me to this unusual situation.

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 64 matches played, 47 of which were correctly predicted, a success rate of 73.4%.
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.

From USA Today, via Twitter

These figures don’t make a lot of sense.

Firstly, that’s a really high proportion of median income.  You can easily find median household income in the US as just over $60,000.  If a typical household had two members who each spent $18,000 on “nonessentials”, that’s more than half their total income, which is hard to believe.  [We’re just doing obvious credibility checks here, but from more detailed data (table H-15)  the median per-person household income is $34,000]

It’s still possible that the figures are true, but really are means and have nothing to do with the “typical American”.  But consider the numbers for subscription boxes and rideshares. If these are means for adults in the US, we can multiply by 250 million to get the corresponding totals of just under and just over $290 billion per year. A quick Google search finds that Uber’s gross income from bookings was about $14 billion last year (which I think includes Uber Eats as well as rideshare). Uber is the largest rideshare company, and there’s no way it’s only 5% of the total market.

It’s harder to find data for e-commerce subscription boxes, but McKinsey says total revenue for the companies in that category that are on a list of the top 500 internet retailers was $2.6 billion in 2016, and whatever people say about long-tail e-commerce, I don’t believe those top companies have less than 1% of the market.

If you click through to look for the data source, there’s nothing there that gives any information. Nothing about the questions asked; nothing about how the people were selected; nothing about how representative the sample ended up being.

Chris Knox (of the NZ Herald) pointed me to a twitter thread where Kate Rabinowitz (of the Washington Post) compared the numbers to the Bureau of Labor Statistics’ Consumer Expenditure (CE) Survey.  Clearly some reporters understand the issue.  But it shouldn’t be possible for a life insurance company to report a dubious survey as a way to get advertising in a major newspaper. Make them buy an ad instead.

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 78 matches played, 50 of which were correctly predicted, a success rate of 64.1%.
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 22

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 126 matches played, 86 of which were correctly predicted, a success rate of 68.3%.
Here are the predictions for last week’s games.

Predictions for Round 22

Here are the predictions for Round 22. 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.