Posts from August 2019 (18)

Team Ratings for Round 24

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 176 matches played, 110 of which were correctly predicted, a success rate of 62.5%.
Here are the predictions for last week’s games.

Predictions for Round 24

Here are the predictions for Round 24. 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 4

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 21 matches played, 14 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. 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 SemiFinals

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 to 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. Possibly there will be no such clashes this year

Performance So Far

So far there have been 21 matches played, 12 of which were correctly predicted, a success rate of 57.1%.
Here are the predictions for last week’s games.

Predictions for the SemiFinals

Here are the predictions for the SemiFinals. 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 23

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 168 matches played, 106 of which were correctly predicted, a success rate of 63.1%.
Here are the predictions for last week’s games.

Predictions for Round 23

Here are the predictions for Round 23. 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 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 14 matches played, 7 of which were correctly predicted, a success rate of 50%.
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 7

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 to 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. Possibly there will be no such clashes this year

Performance So Far

So far there have been 18 matches played, 9 of which were correctly predicted, a success rate of 50%.
Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. 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 the UK Office of National Statistics, some fascinating graphs about the age distribution of deaths by suicide and by drug poisoning.  The graphs make the generational differences really clear: it’s my generation in both cases.
• From the comic Saturday Morning Breakfast Cereal, the issue of mathwashing by AI
• From Radio NZ: discussion of Auckland Transport’s new CCTV camera network.
• New York Times on how people perceive their incomes  as high/medium/low
• “Data Visualisation in the Humanities”, from New Left Review. “What interests us is visualization as a practice, in the conviction that practices—what we learn to do by doing, by professional habit, without being fully aware of what we are doing—often have larger theoretical implications than theoretical statements themselves.“
• Why does Google Maps have a fake New York neighbourhood called “Haberman”?

I’d been worrying about this, so it’s an excuse to do some arithmetic in a news setting.

Hannah Martin at Stuff has a story about the current measles outbreak

Of the 516 cases across the country, 299 had not been vaccinated at all.

A further 154 people who contracted measles this year did not know their vaccination status, ESR data showed.

I thought that implied a surprisingly high number of cases in vaccinated people.  Here’s the ESR report. Let’s compare fully-vaccinated and unvaccinated people, and restrict to those over 4 where ‘fully vaccinated’ means two doses of vaccine, not just ‘on schedule so far’.

There were 30 cases in fully vaccinated people, and about 150 in unvaccinated people.  What sort of ratio would we expect?  Roughly 90% of people have been vaccinated, so if the vaccine had no effect and exposure was uncorrelated with vaccination we’d be expecting about a 10:1 ratio in favour of vaccinated cases. We get a 1:5 ratio the other way.  This very crude comparison suggests about a 50-fold reduction in risk from full vaccination, which is about what’s expected.

It’s a bit more complicated than that. Firstly, the vaccination coverage figures don’t count immigrants.  People who immigrated as kids from somewhere that needs a visa would typically have been vaccinated.  It’s less clear for adults — I had one dose of measles vaccine as a baby  and one when I started my PhD [edit: and another one when I applied for US residency], but no-one asked when I moved here. If immigrants are less likely to be vaccinated, the 10:1 population ratio is smaller and we’d expect more unvaccinated cases.

Second, people who aren’t vaccinated are more likely to be exposed to measles, because of the way vaccination is distributed in the population.  That’s true both for ‘vaccine hesistant’ groups and (as Kirsty Johnston and Chris Knox report) because of poverty and access limitations. If you aren’t vaccinated, it’s more likely that your friends and neighbours aren’t.  Again, this would give us more unvaccinated cases.

Putting these together, the proportion of unvaccinated cases might be a bit lower than we might expect, but not seriously lower.  If there is a discrepancy, it could be due to trusting self-report of vaccination status — adults who know they got all the vaccines that were on offer as kids might well assume they were fully vaccinated against measles if they didn’t have the paperwork to check.

If you’ve only had one dose of the measles vaccine, or if you aren’t sure, you need another one.  Check with your doctor about the current recommendations — and if they’re short of vaccine now, make a note to check again in a few months.  We eradicated the ancestor of measles in 2011, the cattle disease rinderpest, but we’re not close to eradicating measles.

There’s a pretty good piece on Stuff about bias in the justice system that might be attributable to biased algorithms. You should read it.

The story talks about two specific people, one who had a low predicted risk and did re-offend, and one who had a high predicted risk and, well, we don’t know yet.  That’s evidence that the model isn’t perfect; it doesn’t tell us much about how good or bad it is: if you have a well-calibrated model and it says someone has a 0.06 chance of re-offending, then out of every sixteen people like that you’d expect one to re-offend.  Individual cases aren’t very helpful in assessing how good or bad the system is; you need statistics.

As the story makes clear, though, if you want a system that gives Māori and Pākehā the same sentences, simply leaving out the ethnicity variable from your model isn’t going to do that.  Differences by ethnicity are all over the data. A statistical model is going to see who is in prison, and send along more people like that.

Part of the problem (as the story says) is the data: we don’t actually have data on re-offending, only on re-conviction, and the difference between the two involves the justice system and its biases, and there’s a potentially very nasty feedback loop there. But that’s only part of the problem.  The other part is that basing imprisonment on the likelihood of re-offending is going to result in longer terms in prison for people from groups that re-offend more often. And that will include Māori: the over-representation of Māori in the prison population is not just because the justice system is racist, but also because society is racist.

There’s not just a problem with the answer that the model gives; I think there’s a problem with the question, too. The intuition behind predictive sentencing is that if you have two people convicted for the same crime, and they are otherwise similar, and one of them is more likely to commit future crimes, you want to keep that one out of the community for longer.  For me, at least, the intuition relies quite strongly on the ‘otherwise similar’ qualification.  If you came along and said “young people are more likely to commit future crimes than old people, so we should lock them up for longer”, I wouldn’t be at all persuaded. The same for poor vs rich. Or men vs women. Or Māori and Pākehā.  These don’t seem like the sort of relevantly-similar-but-different-risk distinctions that are intuitively a good idea to base imprisonment on.

That is, I think one of the reasons many people don’t like the outputs of predictive sentencing models is that we don’t actually believe in sentencing based on risk of re-offending; at most, we believe in something much more complicated that the models don’t try to do.

I have to admit a distinction here between initial sentencing and parole. Parole decisions, according to the Parole Act must consider both the likelihood of further offending; and the nature and seriousness of any likely subsequent offending. Parole fundamentally does involve the risk of re-offending. Initial sentencing has a lot of purposes, and risk of re-offending is much less tightly linked with it.   According to the story, though, the predictive model is an input to both processes, and similar models are certainly an input to sentencing in the USA.

And finally, it’s important to remember that one of the original reasons people built statistical models to help with sentencing and parole decisions was that it was previously being done by the humans who are the source of the biased data we’re complaining about. Getting rid of statistical models and just relying on the fairness and objectivity of people in the justice system isn’t a panacea either.

Team Ratings for Round 22

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 160 matches played, 100 of which were correctly predicted, a success rate of 62.5%.
Here are the predictions for last week’s games.

Predictions for Round 22

Here are the predictions for Round 22. 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.