Stats Chat

Team Ratings for Round 17

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 121 matches played, 71 of which were correctly predicted, a success rate of 58.7%.
Here are the predictions for last week’s games.

Predictions for Round 17

Here are the predictions for Round 17. 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 the Super Rugby Final

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 90 matches played, 70 of which were correctly predicted, a success rate of 77.8%.
Here are the predictions for last week’s games.

Predictions for the Super Rugby Final

Here are the predictions for the Super Rugby Final. 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 the Currie Cup Final

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 58 matches played, 33 of which were correctly predicted, a success rate of 56.9%.
Here are the predictions for last week’s games.

Predictions for the Currie Cup Final

Here are the predictions for the Currie Cup Final. 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.

When I wrote about ChatGPT-detecting last time, I said that overall accuracy wasn’t enough; you’d need to know about the accuracy in relevant groups of people:

In addition to knowing the overall false positive rate, we’d want to know the false positive rate for important groups of students.  Does using a translator app on text you wrote in another language make you more likely to get flagged? Using a grammar checker? Speaking Kiwi? Are people who use semicolons safe?

Some evidence is accumulating.   The automated detectors can tell opinion pieces published in Science from ChatGPT imitations  (of limited practical use, since Science has an actual list of things it has published).

More importantly, there’s a new preprint that claims the detectors do extremely poorly on material written by non-native English speakers. Specifically, on essays from the TOEFL English exam, the false positive rate averaged over several detectors was over 50%.  The preprint also claims that ChatGPT could be used to edit the TOEFL essays (“Enhance the word choices to sound more like that of a native speaker”) or its own creations (“Elevate the provided text by employing advanced technical language”) to reduce detection.

False positives for non-native speakers are an urgent problem with using the detectors in education. Non-native speakers may already fall under more suspicion, so false positives for them are even more of a problem. However, it’s quite possible that future versions of the detectors can reduce this specific bias (and it will  be important to verify this).

The ability to get around the detectors by editing is a longer-term problem.  If you have a publicly-available detector and the ability to modify your text, you can make changes until the detector no longer reports a problem.   There’s fundamentally no real way around this, and if the process can be automated it will be even easier.  Having a detector that isn’t available to students would remove the ability to edit — but “this program we won’t let you see says you cheated” shouldn’t be an acceptable solution either.

At Ars Technica there’s an interesting story about a smartphone addon being developed to provide rapid, inexpensive blood pressure measurement.   Home blood pressure machines aren’t all that cheap and they’re a bit tricky to use reliably on your  own. Smartphones, obviously, are more expensive than the blood pressure machines, but people might already own them because they are multi-use devices — you can read StatsChat on them as well.  Blood pressure measurement is important because high blood pressure not only presents a risk of heart and brain and kidney damage but is something we can actually treat, safely and effectively, at very low cost.

The gadget uses a smartphone torch and camera to shine light into your finger, and also measures how hard you’re pressing on it. Taking a range of readings at different pressures over a few minutes lets it estimate your blood pressure.  If you heard about problems with oxygen perfusion measurements during the early pandemic, you might worry that this isn’t going to work well in people with dark skin.  The researchers tried to look at this, but it’s not clear whether they had any particularly dark-skinned people in the study (they divided their participants up into Asian, Hispanic, and White, and this was in San Diego).  The other question is accuracy. The story says

 The current version of BPClip produces measurements that may differ from cuff readings by as much as 8 mmHg.

The linked research paper, on the other hand, says 8 mmHg is the mean absolute error — the average difference between the new gadget and a traditional reading. You’d expect about 40% of readings to differ by more than that, and maybe 10% to differ by twice as much.  In fact, the research paper has this picture where the vertical axis is the difference between the new gadget and traditional BP measurements (and the horizontal axis is the average of the two)

It’s clear that the gadget produces measurements that may differ from cuff readings by as much as 20 mmHg — and that’s after dropping  out 5 of the 29 study participants because they couldn’t get good measurements.  When researchers do carefully report their accuracy it’s a pity to get it wrong in translation

Team Ratings for Round 16

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 116 matches played, 67 of which were correctly predicted, a success rate of 57.8%.
Here are the predictions for last week’s games.

Predictions for Round 16

Here are the predictions for Round 16. 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 the Final

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 186 matches played, 134 of which were correctly predicted, a success rate of 72%.
Here are the predictions for last week’s games.

Predictions for the Final

Here are the predictions for the Final. 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 the Semi-finals

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 88 matches played, 68 of which were correctly predicted, a success rate of 77.3%.
Here are the predictions for last week’s games.

Predictions for the Semi-finals

Here are the predictions for the Semi-finals. 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 the Semi Finals

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 the Semi Finals

Here are the predictions for the Semi Finals. 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.

Chris Luxon, via Q+A:

“72% of all landlords are Mum & Dad investors… they are not evil property speculators”

This is attracting a lot of comment, because it can be interpreted as two quite different claims: either that the landlord of a randomly chosen rental has a 72% chance of being a “Mum & Dad investor” or that a randomly chosen person or corporation who is a landlord has a 72% chance of being a “Mum & Dad investor”.  Mr Luxon presumably means the latter, which is the more natural statistical interpretation, though (and this is a frequent Statschat theme) not necessarily the most relevant number to the policy question at hand.

Strictly speaking, we don’t have data on the fraction of  landlords who are Mum & Dad investors or evil property speculators. No-one makes landlords report whether they have children, whether they own property for speculation or investment, or even whether they are evil.  Some landlords might be evil and have children and not be speculators!  However, if we follow Kirsty Johnston a few years ago in the Herald in dividing up landlords by the numbers of properties they own, it turns out that (a) most of the actual people who own homes don’t own a lot and (b) the occasional people who each own a lot of homes collectively own a lot of homes.

Proportion of properties owned by people who own

One property – 27.1 per cent
Two properties – 13.2 per cent
Three properties – 6.5 per cent
Four to six properties – 10.7 per cent
Seven to 20 properties – 9.7 per cent
More than 20 properties – 9.4 per cent

Of the people who own more than one property, 52% own only two and 70% own two or three.

The word ‘people’ is important here: homes are also owned by companies.  Companies are definitionally not Mum & Dad investors, but they also aren’t necessarily evil property speculators.  The largest in that Herald story was Housing New Zealand, as it then was, who had 60,000 rental properties, and the category would include some other non-profit providers.  At least some of the people who don’t like landlords feel differently about community housing providers.

And, finally homes are owned by trusts, which is a much more difficulty category to survey, since there isn’t a centralised registry of beneficiaries of trusts. The Herald didn’t even hazard a guess.

So, there’s plenty of room for Chris Luxon’s claim to be largely true. Most landlords are probably small-scale, but the small proportion who aren’t will still add up to a lot of rentals.  However, he almost certainly doesn’t have reliable population data about either the parental status or moral alignment of landlords. Nor is it clear that the typical portfolio size of landlords should be decisive in deciding how to tax and regulate them.  Mum & Dad takeaways still need to follow food safety rules.