Stats Chat

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, 8 of which were correctly predicted, a success rate of 57.1%.
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 7 matches played, 5 of which were correctly predicted, a success rate of 71.4%.
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.

Back in early December, I wrote about a political ad authorised by Simon Bridges, showing the price of fuel.

As I said the, the numbers do not remotely match the graph.  A graph using those numbers would look more like

Dylan Reeve and other people complained to the Advertising Standards Authority, both about the graph itself and about the choice of numbers, which (in his opinion and mine) was cherrypicked in a misleading way.

The ASA decided (in a split decision) that the graphic was not misleading

The majority said the data displayed was correct which saved the hyperbolic graphic from being misleading, given the political medium used and the principles of advocacy advertising.

I believe this is decision is bad in terms of norms for mainstream political advertising, and that it’s likely to be factually incorrect as to the impact of the graphic.

The cherrypicked numbers are misleading, but they are misleading in a way that is, sadly, routine in political advertising.  I’ve written about examples from both parties here since StatsChat started. My starting point for any political advocacy involving numerical comparisons is always that the numbers are likely to be correct as quoted, but chosen to mislead. Given the established norms,  I can understand the ASA not wanting to get involved.

The distorted graph, on the other hand, seems to be new.  I was genuinely surprised at the extent of the distortion — well beyond common tricks of perspective or false baseline.

If writing the numbers on a misleading graph was enough to stop it being misleading, there would be no point having data graphics.  The whole point of data graphics is that they provide a clearer and more forceful impression of the data than just tabulating the numbers.  Misleading graphs are misleading.

Team Ratings for Round 9 Delayed Match

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 69 matches played, 53 of which were correctly predicted, a success rate of 76.8%.
Here are the predictions for last week’s games.

Predictions for Round 9 Delayed Match

Here are the predictions for Round 9 Delayed Match. 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 54 matches played, 37 of which were correctly predicted, a success rate of 68.5%.
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.

• Via Alberto Cairo’s blog, a beautiful example of how to annotate a data graphic of time-use data, at the Financial Times
• If you happen to want data-visualisation themed coffee mugs, Martin Telefont has some
• The Geography of FM Radio (via Nathan Yau on Twitter): spatial patterns of US radio stations. Some types follow population density; others strikingly don’t.
• From Peter Ellis: how much effect does seeding have in a tennis tournament? It doesn’t seem to do a lot for the top player, but it really does increase the chance of the next few getting into the final rounds.
• A computer algorithm can do surprisingly well in answering questions about pictures just by knowing what answers are common for particular questions and without looking at the picture.
• “The data set covers just 300 patients, and only 38 of them gave birth prematurely”, nevertheless, a bunch of computer algorithms thought they could accurately predict preterm birth from electrical activity in the muscles of the uterus, before 37 weeks.  The algorithms used some created data examples, intended to boost the effective sample size, which is a perfectly respectable technique.  Using those created examples in estimating the final accuracy, on the other hand, can be a problem.  [Research preprint]
• HPV vaccination is preventing cancer-causing HPV infections: data from the UK (it’s also not currently increasing rates of other HPV strains) (PDF report)
  

One reputable source of moderately technical information about the new coronavirus is the MRC Centre for Global Infectious Disease Analysis, at Imperial College, London.  They’ve worked on outbreak modelling and control advice for a long time, across a wide range of epidemics (including both previous coronavirus outbreaks: SARS and MERS).

Their latest (third) report says that it’s clear there has been sustained person-to-person transmission — a spreading epidemic — in China, and that basically nothing else is clear.

From the Discussion section

Whether transmission continues at the same rate now critically depends on the effectiveness of the intense control effort now underway in Wuhan and across China. We note the large body of evidence that suggests that the reproduction number for SARS changed considerably when populations became fully aware of the threat. If a similar change to contact patterns is occurring in this outbreak, rates of transmission are likely to be lower now than during the period for which these estimates were made, due to control measures and risk avoidance in the population. Whether the reduction in transmission is sufficient to reduce R to below 1–and thus end the outbreak –remains to be seen. Reports point to mildly symptomatic but infectious cases of 2019-nCoV, which were not a feature of SARS. Prompt detection and isolation of such cases will be extremely challenging, given the larger number of other diseases (e.g. influenza) which can cause such non-specific respiratory symptoms.While more severe cases will always need to be prioritised, control may depend upon successful detection, testing and isolation of suspect cases with the broadest possible range of symptom severity.Our results emphasise the need to track transmission rates over the next few weeks, especially in Wuhan. If a clear downwards trend is observed in the numbers of new cases, that would indicate that control measures and behavioural changes can substantially reduce the transmissibility of 2019-nCoV. Genetic data from Wuhan after the implementation of strong public health measures may also provide valuable insight into the patterns and rate of transmission.

Despite the recent decision of the WHO Emergency Committee to not declare this a Public Health Emergency of International Concern at this time, this epidemic represents a clear and ongoing global health threat. It is uncertain at the current time whether it is possible to contain the continuing epidemic within China. In addition to monitoring how the epidemic evolves, it is critical that the magnitude of the threat is better understood. Currently, we have only a limited understanding of the spectrum of severity of symptoms that infection with this virus causes, and no reliable estimates of the case fatality ratio –the proportion of cases who will die as a result of the disease. Characterising the severity spectrum, and how severity of symptoms relates to infectiousness, will be critical to evaluating the feasibility of control and the likely public health impact of this epidemic.

When they talk about ‘containing’ the epidemic within China, they don’t mean whether or not there are cases outside China — there are already are — they mean whether or not there’s sustained transmission from person to person outside China.

Via maths teacher Twitter, two graphs, from the economics PhD research of Cody Tuttle, showing data from the United States Sentencing Commission on recorded drug amounts in federal drug cases.

The first one shows amounts of crack cocaine 1990-2010

The second shows crack cocaine for the years 2011-2015

For some reason, people suddenly started getting caught with 280g of crack in 2011. Now, 280g is a rounder number than it looks — it’s 20 times 14g, or in other words, 10 ounces.  Even so, you’d wonder why 10oz loads of cocaine suddenly started being popular.

It turns out that there was a change in the law. Up to 2010, a relic of the ‘war on drugs’ period meant there was a mandatory 10-year minimum sentence for more than 50g. From 2011, the threshold for the mandatory minimum was 280g.  Suddenly, the proportion of people convicted of having 280-290g shot up.  Further graphs and analyses show that the increase was much more pronounced for Black and Hispanic defendants than White.

Interestingly, the paper says “However, the data on drug seizures made by local and federal agencies do not show increased bunching at 280g after 2010.” The conclusion reached in the analysis is that a substantial minority of prosecutors, who have some discretion in deciding what quantity of drugs to list in the charges, misused this discretion. 

The analysis is a good example of the sort of auditing you’d like for high-stakes computer algorithms, and it shows how you can bias the outputs of a decision-making system (such as the court) by biasing the data you feed it.

One of the advantages of computerised algorithms is that this sort of auditing is much easier (in principle). It’s because you can’t force the US Federal court system to run on your choice of simulated data that you need to rely on ‘natural experiments’ like this one.

New Zealand and international media are reporting an new analysis of the gender pay gap among NZ academics. At one level this isn’t anything very surprising: there’s a gender pay gap, of the same percentage order of magnitude as in NZ as a whole (larger in Medicine, smaller in Arts).

As I’ve pointed out before, we know this is caused by gender, it’s not just some sort of correlation caused by confounding factors, since there aren’t any. What’s interesting is how it is that women come to be paid less. You could imagine a range of direct mechanisms:

• slower promotion
• lower pay at the same grade
• less likely to be head of department/school
• more likely to be at institutions where pay is lower
• more likely to be in fields where pay is lower

And you could imagine possible factors leading into these

• lower research ability
• lower average age, because of past discrimination
• interested in putting more effort into teaching or into service
• pushed into putting more effort into teaching or into service
• interested in putting more effort into childcare
• pushed into putting more effort into childcare
• discrimination in salary assignments
• discrimination in promotion

and so on.

While many people have more or less informed opinions about these mechanisms, it’s often hard to get good data.  The research (by Associate Professors Ann Brower and Alex James of the University of Canterbury) takes advantage of the 2018 PBRF evaluations of NZ academics.  These evaluations were based on research portfolios selected to show the best research from each person (quality rather than quantity) and were evaluated by panels of NZ and overseas experts in each field.

In this paper, Brower and James got access to PBRF ratings and salary data for NZ academics, and so could look at whether women of similar age with similar PBRF scores had similar pay. As will surely astonish you, they didn’t.  In particular, it appears that women are less likely to be promoted to Associate Professor and Professor, with similar PBRF ratings, that men are.  Differences in age distribution and research performance explain about half the gender pay gap; the other half remains.

The big limitation of any analysis of this sort is the quality of the performance data.  If performance is measured poorly, then even if it really does completely explain the outcomes, it will look as if there’s a unexplained gap.  The point of this paper is that PBRF is quite a good measurement of research performance: assessed by scientists in each field, by panels convened with at least some attention to gender representation, using individual, up-to-date information.  If you believed that PBRF was pretty random and unreliable, you wouldn’t be impressed by these analyses: if PBRF scores don’t describe research performance well, they can’t explain its effect on pay and promotion well.

There could be bias in the other direction, too.  Suppose PBRF were biased in favour of men, and promotions were biased in favour of men in exactly the same way.  Adjusting for PBRF would then completely reproduce the bias in promotion, and make it look as if pay was completely fair.

Now, I’m potentially biased, since I was on a PBRF panel in 2013 (and since I got a good PBRF score), but I think PBRF is a fairly good assessment. I think the true residual pay gap could easily be quite a bit smaller or larger than this analysis estimates, but it’s as good as you’re likely to be able to do, and it certainly does not support the view that the pay gap is zero.

There’s a nice piece on Stuff about earthquake risk in New Zealand (basically, the geology is out to get us, and we should be prepared).

It includes this map, which comes from “SUPPLIED”

I wondered what the risk numbers (0.15, 0.3) actually meant.  Are they some kind of probability of a quake? Over what period of time?

It’s surprisingly hard to find out.  The first step is easy: the numbers are seismic risk factors used in the building code, eg, see this map from Radio NZ in 2017

Searching a bit more, you can readily find that (eg, at building.govt.nz) these are the “Z-values to determine seismic risk”, and that there’s

• a low seismic risk if the area has a Z factor that is less than 0.15; and
• a medium seismic risk if the area has a Z factor that is greater than or equal to 0.15 and less than 0.3; and
• a high seismic risk if the area has a Z factor that is greater than or equal to 0.3.

This isn’t getting us much further forward, but there is a reference to a Standard.  Now, there are (for some reason) serious penalties over and above the copyright law for being too explicit about the contents of a standard, but you can go and read it for yourself, and verify that Z is a hazard factor that you look up in a table, and that it gets combined with other information about soil and so on to give you a number that goes into how strong your building needs to be. But there isn’t any more explicit explanation of what a Z is.

Searching further, I found a research paper which describes the Australian standards as having a Z that looks very much like the NZ one, defined as the “effective peak ground acceleration with a return period of 500 years”. So, it’s not the probability of a quake, it’s the intensity of the largest quake expected over a 500 year period, in units of the acceleration due to gravity.  The US also has a Z with the same definition, though they probably say it ‘zee’.

So, two points: first, it shouldn’t be this much work to find out what the numbers mean on a map published on a major news website. Second, I’m not convinced that ‘seismic risk’ is a good name for this thing, since ‘seismic risk’ sounds more like it should involve probabilities.