Stats Chat

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 54 matches played, 40 of which were correctly predicted, a success rate of 74.1%.
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.

• On the relationship between infographics and scientific illustration
• A non-peer-reviewed lab-bench research study being used to market some ‘natural’ product isn’t new on StatsChat. All the problems being pointed out in the news story is, though. 
• If you like running gratuitously long distances, you might like runningstats.nz. It includes comparisons of all the NZ marathon, half-marathon, and 10k events that are based on comparing the same runners on different routes and so aren’t biased by different participation in different events.
• Chris Knox has a nice interactive at oneroof for looking at real estate price changes across NZ.
• Lessons From Posting A Fake Map

From Scoop.co.nz, the latest Colmar Brunton poll results

National 46 percent up three points 
Labour 43 percent down two points
Greens 5 percent down two points
NZ First 4 percent down one point
Maori Party unchanged on 4 one percent
ACT up one point to one percent

Question: which of these changes are greater than the ‘margin of error’ for polls of this size?

The maximum margin of error in these polls  is 3% (the maximum margin of error is for parties polling not too far from 50%). That makes the maximum margin of error for changes between two polls about 4.5% — there are two polls involved, and that multiplies the likely error by the square root of two.  For any given party, about one poll in six should show a change of three or more points if the underlying support is stable.  That’s in a perfect mathematical world — in the real world, the likely sample errors are larger because the polls aren’t an ideal random sample.

If the 3-point increase in National’s support were real, it would be interesting. But a single poll is a very blunt instrument and the grounds for calling this a “surge” are very weak.

The maximum margin of error doesn’t apply to the Greens, though.  When you get to smaller parties, the likely sampling error is smaller in absolute terms, though larger as a proportion of their support.  I’ve posted before on this topic, and you can look up the table there to find that the margin of error at 5% is a bit under 1.5 percentage points, so a change of two points is borderline interesting — depending on whether it was rounded up to two or down to two.

You might also wonder if the same applied to ACT. There we’re completely at the mercy of the rounding — it will be possible to tell more when Colmar Brunton releases their detailed report, which (going from past versions) gives an extra decimal place for parties below 5%

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 48 matches played, 38 of which were correctly predicted, a success rate of 79.2%.
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.

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 63 matches played, 52 of which were correctly predicted, a success rate of 82.5%.
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.

Two press releases from Stats NZ today

2018 Census data release delayed

Why the 2018 Census data release is delayed

Brian Wansink, a prominent food researcher from Cornell, was forced to retire earlier this year. Andrew Gelman has some good perspectives. Wansink’s research was on contextual effects on eating — eg, the impact of plate size — and a bunch of these papers have now been retracted.

This week, Wired has a Thanksgiving-themed article about his research. It includes this quote from an email he sent to colleagues ahead of his retirement

“We may believe that our papers have been unfairly retracted. But what they can’t retract is the impact these have had on people’s lives and the impact they will continue to have.”

That’s a perfect summary of the problems with studies that over-promise and are over-publicised.  Whether the research was done well or not, it’s going to stick.  Subsequent developments — whether modifications, replication failures, or retractions — never get the impact of the original claim.

• “Rather than assume algorithms will produce better outcomes and hope they don’t accelerate discrimination, we should assume they will be discriminatory and inequitable unless designed specifically to redress these issues.” Lucy Bernholz
• ” Introduction of [a predictive risk screening tool] resulted in a statistically significant increase in emergency hospital admissions and use of other [National Health] services without evidence of benefits to patients or the [National Health Service].” In the academic journal BMJ, so a bit more technical
• Why the NY Times map of the US election results is so good: a Twitter thread
• Stacey Kirk in the Sunday Star-Times on the campaign to get Pharmac to pay for one of the most expensive drugs in the world.
• Interesting interactive in the Herald about quality-of-life and work in NZ cities.  It’s very economist in style.  That’s true on the good sense that it appreciates high house prices are a signal that lots of people want to live somewhere and low house prices are a signal that lots of people don’t.  It’s also true in the bad sense that there some places where not many people want to live, but the people who do live there really like it — and this sort of analysis suppresses that variation in preferences.
• Interesting book on data science and data use: “Data Feminism”

The Herald has a story from the Washington Post on an “AI” screening tool for babysitters, that allegedly uses both computer vision and text processing to screen social media for risk factors.   Here are three quotes from it:

1. A company co-founder says

Parents, he said, should see the ratings as a companion that “may or may not reflect the sitter’s actual attributes.”

But the danger of hiring a problematic or violent babysitter, he added, makes the AI a necessary tool for any parent hoping to keep his or her child safe.

The first thing to note about this is that you could make the same claims about astrology or handwriting analysis or a tarot reading. There’s no quantitative information about accuracy given, and it’s hard to see how the company could even know much how accurate its ratings were, or how biased. It’s not even for sure that the risk rating is positively correlated with risk to kids; the company seems careful not to make even a claim this weak.

2.

Parents could, presumably, look at their sitters’ public social media accounts themselves. But the computer-generated reports promise an in-depth inspection of years of online activity, boiled down to a single digit: an intoxicatingly simple solution to an impractical task.

If the algorithms actually predicted risk better and were less biased than typical employers there might be an advantage to this: your social media would be shared with a faceless US company rather than your potential employer, so there might be less actual privacy invasion — after all, some faceless US companies already have your social media. It might be less embarrassing than your boss knowing what your favourite member of the appropriate sex calls you. The computer could also be set up to ignore irrelevant information like whether you talk about your sexual orientation online.   With the setup as it is, that’s not the case, and one of the biggest risks is the completely unfounded appearance of both accuracy and objectivity — “mathwashing” as the jargon puts it.

3.

Where she lives, “100 per cent of the parents are going to want to use this,” she added. “We all want the perfect babysitter.”

One of the significant risks of automating human judgement is that it can go viral. There’s a limit to how well ordinary human prejudices can scale — you’ve got some chance of finding someone who has different biases. The prejudices of one computer checklist, though, can keep someone completely out of an employment sector.

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 42 matches played, 32 of which were correctly predicted, a success rate of 76.2%.
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.