Stats Chat

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 64 matches played, 36 of which were correctly predicted, a success rate of 56.2%.
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.

1. Ancestry.com has maps of the ancestry structure of North America, based on people who sent DNA samples in for their genotype service (click to embiggen)

To make these maps, they looked for pairs of people whose DNA showed they were distant relatives, then simplified the resulting network into relatively stable clusters. They then drew the clusters on a map and coloured them according to what part of the world those people’s distant ancestors probably came from.  In theory, this should give something like a map of immigration into the US (and to a lesser extent, of remaining Native populations).  The map is a massive oversimplification, but that’s more or less the point: it simplifies the data to highlight particular patterns (and, necessarily, to hide others).  There’s a research paper, too.

2. In a satire on predictive policing, The New Inquiry has an app showing high-risk neighbourhoods for financial crime. There’s also a story at Buzzfeed.

The app uses data from the US Financial Regulatory Authority (FINRA), and models the risk of financial crime using the usual sort of neighbourhood characteristics (eg number of liquor licenses, number of investment advisers).

3. The Sydney Morning Herald had a social/political quiz “What Kind of Aussie Are You?”.

They also have a discussion of how they designed the 7 groups.  Again, the groups aren’t entirely real, but are a set of stories told about complicated, multi-dimensional data.

The challenge in any display of this type is to remove enough information that the stories are visible, but not so much that they aren’t true– and not everyone will agree on whether you’ve succeeded.

In New Zealand, the Government Statistician reports to the Minister of Statistics, currently Mark Mitchell.  For about a decade, the UK has had a different system, where the National Statistician reports to the UK Statistics Authority, which is responsible directly to Parliament. The system is intended to make official statistics more clearly independent of the government of the day.

An additional role of the UK Statistics Authority is as a sort of statistics ombudsman when official statistics are misused.  There’s a new letter from the Chair to the UK political parties

The UK Statistics Authority has the statutory objective to promote and safeguard the production and publication of official statistics that serve the public good.

My predecessors Sir Michael Scholar and Sir Andrew Dilnot have in the past been obliged to write publicly about the misuse of official statistics in other pre-election periods and during the EU referendum campaign. Misuse at any time damages the integrity of statistics, causes confusion and undermines trust.

I write now to ask for your support and leadership to ensure that official statistics are used throughout this General Election period and beyond, in the public interest and in accordance with the principles of the Code of Practice for Official Statistics. In particular, the statistical sources should be clear and accessible to all; any caveats or limitations in the statistics should be respected; and campaigns should not pick out single numbers that differ from the picture painted by the statistics as a whole.

I am sending identical letters to the leaders of the main political parties, with a copy to Sir Jeremy Heywood, Cabinet Secretary.

We don’t have anyone whose job it is to write that sort of letter here, but it would be nice if the political parties (and their partisans) still followed this advice.

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 71 matches played, 55 of which were correctly predicted, a success rate of 77.5%.
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.

• The Herald (from the Daily Mail) recommends drinking beetroot juice, based on a study of brain waves: “This finding could help people who are at-risk of brain deterioration to remain functionally independent, such as those with a family history of dementia“.  The NHS Choices blog commented on a similar study by the same research group in 2010; their comments still apply.

• Testimonials and motivational speakers tell you “I did this and look how it turned out”.  As XKCD illustrates, results may not be typical 

• “Data made available for reanalysis, a journal that promptly responded to the outcomes of that reanalysis, and a finding that could save lives.” (from Stat). Another moral to the story: don’t edit data by copy-and-paste.

• “The company says it has studies that back up its claims, but refused to release them on the grounds that they are commercial-in-confidence.” It appears that Johnson & Johnson would rather pull their ad than let people look at the evidence. (from The Age)

• “it’s not acceptable if you’ve got the information readily available to leave it to the last minute for release, that’s not what the Act says you can do”  The Chief Ombudsman interviewed by Newsroom  about the Official Information Act.

And finally

I ended a recent #SciComm talk with this slide of questions scientists should answer even if they’re not asked: pic.twitter.com/N88LuuSBoM

— Rachel Feltman (@RachelFeltman) April 11, 2017

So, the Herald (from the Daily Mail) has a headline Why women should eat a punnet of strawberries a day. That seems a little extreme, especially as punnets of strawberries are fairly seasonal.

The story leads off with

Eating just 15 strawberries a day protected mice from aggressive breast cancer in a new medical study.

So, first of all, mice, not women.  Also, when you go to the open-access research paper, it didn’t exactly ‘protect’ the mice.  The mice had cells from a breast cancer cell culture implanted under their skins, and the study looked at the change in size of those implanted tumours, not at spread within the mouse or health of the mouse or anything like that.  It’s a useful approach to learning about cancer cell biology, but not all that close to preventing or treating human cancers.

More surprisingly, though, “15 strawberries a day” seems quite a lot for a mouse — several times its body weight. The story changes a bit later:

In total, the strawberries made up 15 percent of the mice’s diet. That is just shy of the recommended daily amount of fruit we should eat each day, and would be equivalent to a punnet of strawberries, reported the Daily Mail.

A figure of 15% seems more plausible than 15 strawberries, though it’s still not quite true, since actually the mice were given concentrated strawberry extract in their food rather than strawberries.  Using the standard (lowish) estimate of 2000 kcal/day, 15% of calories would  be 300 kcal/day  which would take nearly a kilogram of strawberries.

Previous studies have already shown that eating between 10 and 15 strawberries a day can make arteries healthier by reducing blood cholesterol levels.

There isn’t a reference, but the same researcher has studied strawberries and cholesterol (this time even in humans). The ‘between 10 and 15 strawberries a day’ was actually 500g per day.

[via Sam Warburton]

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday April 28 2017.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 22 – 28 2017 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)

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 56 matches played, 32 of which were correctly predicted, a success rate of 57.1%.
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 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 63 matches played, 49 of which were correctly predicted, a success rate of 77.8%.
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.

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday April 21 2017.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 15 – 21 2017 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)