Posts from March 2017 (37)

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 18 matches played, 12 of which were correctly predicted, a success rate of 66.7%.
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.

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, 3 of which were correctly predicted, a success rate of 37.5%.
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.

From YouGov (who seem to already be regretting it).

This obviously isn’t a pie chart, because the pieces are the same size but the numbers are different. It’s not really a graph at all; it’s an idiosyncratically organised, illustrated table.  It gets worse, though. The pizza picture itself isn’t doing any productive work in this graphic: the only information it conveys is misleading. There’s a clear impression given that particular ingredients go together, when that’s not how the questions were asked. And as the footnote says, there are a lot of popular ingredients that didn’t even make it on to the graphic.

In medical research we distinguish ‘hard’ outcomes that can be reliably and objectively measured (such as death, blood pressure, activated protein C concentrations) from ‘soft’ outcomes that depend on subjective reporting.  We also distinguish ‘patient-centered’ or ‘clinical’ or ‘real’ outcomes that matter directly to patients (such as death, pain, or dependency) from ‘surrogate’  or ‘intermediate’ outcomes that are biologically meaningful but don’t directly matter to patients.  ‘Death’ is one of the few things we can measure that’s on both lists.

Cause of death, however, is a much less ideal thing to measure.  If some form of cancer screening makes it less likely that you die of that particular type of cancer but doesn’t increase how long you live, it’s going to be less popular than if it genuinely postpones death.  What’s more surprising is that cause of death is hard to measure objectively and reliably. But it is.

Suppose someone smokes heavily for many years and as a result develops chronic lung disease, and as a result develops pneumonia, and as a result is in hospital, has a cardiac arrest due to a medical error, and dies. Is the cause of death ‘cardiac arrest’ or ‘medical error’ or ‘pneumonia’ or ‘COPD’ or ‘smoking’?  The best choice is a subjective matter of convention: what’s the most useful way to record the primary cause of death? But even with a convention in place, there’s a lot of work to make sure it is followed.  For example, a series of research papers in Thailand estimated that more than half the deaths from the main causes (eg stroke, HIV/AIDs, road traffic accidents, types of heart disease) were misclassified as less-specific causes, and came up with a way to at least correct the national totals.

In Western countries, things are better on average. However, as Radio NZ described today, in Australia (and probably NZ) deaths of people with intellectual disability tend to be reported as due to their intellectual disability rather than to whatever specific illness or injury they had.  You can see why this happens, but you should also be able to see why it’s not ideal in improving healthcare for these people.  Listen to the Radio NZ story; it’s good. If you want a reference to the open-access research paper, though, you won’t find it at Radio NZ. It’s here

• “Does air pollution kill 40,000 each year people in the UK?”, from David Spiegelhalter

• Phil Cook on “99% sugar free” beer (me, last year, on a similar topic)

• Newshub had a story about the Accommodation Survey not specifically excluding people in hotels who were there as emergency housing.  Nerds across the NZ political spectrum (eg, me, Keith Ng, and Eric Crampton) were unimpressed with this story. Eric actually wrote a blog post, so I’ll refer you there for more details.

• Russell Brown wrote about the overuse of workplace drug tests that aren’t tests for impairment.

• Ars Technica shows you can write about lab-science discoveries relevant to health without leaving out all the cautions and caveats.

• A research paper in PLoS One shows that newspapers write about news.  That is, they write `breakthrough’ stories about new treatments but give a lot less prominence to later studies that are less favorable.  Interestingly, this didn’t apply to ‘lifestyle’ stories, where ‘coffee is Good/Bad this week’ can always find a place.

• The Herald had a story last week about “The $2m+ price tag for a top decile Auckland education.” In contrast to their story two years ago, this doesn’t make any attempt to estimate the premium for the top school zones. That is, if a family with school-age kids doesn’t live in the ‘Double Grammar Zone’ and pay $2 million for a house, they’ll still have to live somewhere and pay something for a house.  In the 2015 story, the cost of a house just outside a top school zone was about 20% lower than just inside. Even that probably overestimates the school premium, but the total cost of a house obviously does.

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 March 10 2017.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of March 4 – 10 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.

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If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!