Posts from October 2018 (17)

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 62 matches played, 43 of which were correctly predicted, a success rate of 69.4%.
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.

• “Data for Sale” at Stuff, on data ethics
• “How a math genius hacked OkCupid to find true love” at Wired
• Chris Knox interviews Cathy O’Neil, who is in New Zealand for a Stats NZ ‘Data Summit’.  StatsChat readers will already be familiar with Dr O’Neil aka mathbabe.org
•  ‘People disagree about what fairness looks like. That’s true in general, and also true when you try to write down a mathematical equation and say, “This is the definition of fairness.”’ An interview with Dr Kristian Lum  of the Human Rights Data Analysis Group
• A group at Johns Hopkins Dept of Biostatistics have been working to reduce the scarcity value of data science. They have a new program: “excited to announce the first part of our new system, a new set of massive online open courses called Chromebook Data Science. These MOOCs are for anyone from high schoolers on up to get into data science. If you can read and follow instructions you can learn data science from these courses!” (There’s obviously a potential conflict of interest here with Auckland’s data science programs, but I think there’s a separate market for in-person training where you can ask questions)
• “Which neighborhoods in America offer children the best chance at a better life than their parents? The Opportunity Atlas uses anonymous data following 20 million Americans from childhood to their mid-thirties to answer this question.” There’s an obvious difficulty with any dataset like this — if you’re looking at people in their mid-thirties, they were children quite a while ago and things may have changed.  Still interesting to explore.

It’s time for StatsChat’s favourite bogus poll: Forest & Bird’s Bird of the Year.

In contrast to most bogus online polls, Bird of the Year doesn’t pretend to be anything more than a publicity stunt, and no-one seriously believes the huge year-to-year variation in the results has any real meaning in popular opinion

Bird of the Year still has more quality control than most bogus polls. They require a unique email address per vote, and this year have monitoring by Dragonfly Data Science.

Dragonfly noticed an apparent attempt to hack the vote last night, with a large number of votes from a single Australian IP address for the cormorants or shags, kawau in te reo.

Yes, Bird of the Year is a joke. But any other online clicky poll is at least as much of a joke.

(PS: for the sake of people whose tolerance for this sort of thing is lower than yours, if you tweet about Bird of the Year, use the hashtag)

From Pew Research, via Twitter

That list of European countries, presumably intended to give the most meaningful comparison to the USA, is a bit unusual.

It includes Malta and Cyprus and Lichtenstein, but doesn’t include Ireland or the UK.

There’s an ‘interactive’ at Stuff about the drug rebates that Pharmac negotiates. The most obvious issue with it is the graphics, for example

and

The first of these is a really dramatic illustration of a well-known way graphs can mislead: using just one dimension of a two-dimensional or three-dimensional thing to represent a number. The 2016/7 capsule looks much more than twice as big as the puny little 2014/15 one, because it’s twice as high and twice as wide (and by implication from shading, twice as deep).  The first graph also commits the accounting sin of displaying a trend from total, nominal expenditures rather than real (ie, inflation-adjusted) per-capita expenditures.

The second one is not as bad, but the descending line to the left of the data points is a bit dodgy, as is the fact that the x-axis is different from the first graph even though the information should all be available.  Also, given that rebates are precisely not a component of Pharmac’s drug spend, the percentage is a bit ambiguous.  The graph shows total rebates divided by what would have been Pharmac’s “drug spend” in the improbable scenario that the same drugs had been bought without rebates. That is, in the most recent year, Pharmac spent $849 million on drugs. If rebates were $400m as shown in the first graph, the percentage in the second graph is something like ($400 million)/($400 million+$849 million)=32%.

More striking when you listen to the whole thing, though,  is how negative it is about New Zealand getting these non-public discounts on expensive drugs.  In particular, the primary issue raised is whether we’re getting better or worse discounts than other countries (which, indeed, we don’t know), rather than whether we’re getting good value for what we pay — which we basically do know, because that’s exactly what Pharmac assesses.  

Now, since the drug companies do want to keep their prices secret there must be some financial advantage to them in doing so, thus there is probably some financial disadvantage to someone other than them.   It’s possible that we’re in that group; that other comparable countries are getting better prices than we are. It’s also possible that we’re getting better prices than them.  Given Pharmac’s relatively small budget and their demonstrated and unusual willingness not to subsidise overpriced new drugs, I know which way I’d guess.

There are two refreshing aspects to the interactive, though.  First, it’s good to see explicit consideration of the fact that drug prices are primarily not a rich-country problem.   Second, it’s good to see something in the NZ mass media in favour of the principle that Pharmac can and should walk away from bad offers. That’s a definite change from most coverage of new miracle drugs and Pharmac.

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 54 matches played, 36 of which were correctly predicted, a success rate of 66.7%.
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 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.

Note that Cheetahs2 refers the Cheetahs team when there is a Pro14 match. The assumption is that the team playing in the Pro14 is the top team and the Currie Cup team is essentially a second team.

Performance So Far

So far there have been 18 matches played, 15 of which were correctly predicted, a success rate of 83.3%.
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.