Posts from July 2018 (20)

Team Ratings for the Qualifiers

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 120 matches played, 84 of which were correctly predicted, a success rate of 70%.
Here are the predictions for last week’s games.

Predictions for the Qualifiers

Here are the predictions for the Qualifiers. 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 19

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 136 matches played, 81 of which were correctly predicted, a success rate of 59.6%.
Here are the predictions for last week’s games.

Predictions for Round 19

Here are the predictions for Round 19. 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 19

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 113 matches played, 78 of which were correctly predicted, a success rate of 69%.
Here are the predictions for last week’s games.

Predictions for Round 19

Here are the predictions for Round 19. 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 18

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 128 matches played, 77 of which were correctly predicted, a success rate of 60.2%.
Here are the predictions for last week’s games.

Predictions for Round 18

Here are the predictions for Round 18. 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 Claus Wilke on Twitter, using color to indicate uncertainty, based on data from before the 2016 US election.

The red:blue scale indicates who is ahead, and the grey:coloured scale indicates confidence.  There was lots of discussion about whether this is graying out the differences too much or not enough, and so on, but it’s an interesting idea.

Chris Knox at the Herald has a very nice visualisation of salary distributions and gender differences by age, industry, region, and sector. 

These are tidier than you’d expect from a relatively small survey, because they are predictions from a model, rather than raw survey data.

The good thing about using a model like this is that you can get somewhat realistic pictures from a much smaller survey than you’d otherwise need. The model is expanding the real data for each individual into a smooth distribution on the graph.

The bad thing is there’s a bit of distortion: for example, a graph of a large enough set of raw data would show spikes where multiple people have the same round-number income, and probably a sharper cutoff at the bottom end rather than a smooth tail down to zero.

The graph shows very little difference between private-sector and public-sector workers.  That surprised me, because public sector employees on average have substantially higher wage/salary income — as Keith Ng separately writes in the Herald. The difference doesn’t, of course, represent higher pay for comparable jobs; it’s because the public sector is increasingly biased towards educated professionals.  Also, government bodies (like other large organisations) will often contract out their lowest-paying jobs rather than using their own employees. Keith showed, using StatsNZ data, that public-sector employees tend to earn slightly less than private-sector employees within the same occupation type.

But if it takes comparisons within an occupation to correct the misleading public-private comparison in StatsNZ data, why doesn’t it take comparisons within an occupation in the visualisation?  After some Twitter conversation we worked out that it’s because the visualisation is of salaries, and the other comparison is of salaries and wages. Restricting to salaried employees, while cruder than doing comparisons within occupation types, is enough to remove the bulk of the bias.

From the Listener this week, and now on noted.co.nz

This week, new data emerged from the ASB Bankshowing that foreign buyers are a much more significant part of the overheated housing market than had previously been established; that is, between 11% and 20% rather than the piffling 3.3% nationally – and 7.3% in the Auckland market, ground zero for our property frenzy – previously reported by Statistics New Zealand.

As I wrote last week

1. It’s not new data – it comes from exactly the same StatsNZ report (if either the ASB report or the StatsNZ report had been linked, this would have been easier for the reader to find out)
2. Not foreign buyers. The 11% includes 8% of New Zealand residents who aren’t citizens at the time they buy the house.  The 11-21% range includes 0-10% of buyers who are foreign commercial entities. Because ASB didn’t have any new data, they don’t know what proportion of the commercial entities are local, but they were guessing it was at the low end. So, ASB’s figure for foreign buyers is 3-13%, with a guess that it’s towards the low end.
3. In fact, even the 3% counts people on work visas buying a house or apartment to live in as foreign buyers. You could maybe argue that people on work visas should be driving up rental costs instead, but it’s not that obvious a case.

Team Ratings for Round 18

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 106 matches played, 72 of which were correctly predicted, a success rate of 67.9%.
Here are the predictions for last week’s games.

Predictions for Round 18

Here are the predictions for Round 18. 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 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 124 matches played, 73 of which were correctly predicted, a success rate of 58.9%.
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.

• False positives: people who think they are allergic to penicillin (but aren’t, or aren’t anymore) are at higher risk of getting nasty antibiotic-resistant infections.
• RadioNZ interview with the new Chief Science Advisor, Juliet Gerrard. Also, see the official chief sciencely instagram
• As I point out each year, the Q&A list for the NZ Garden Bird survey has some well-written, simple principles of research design
• There’s a story Americans vote Taco Bell as the ‘Best Mexican Restaurant of 2018’.  Of course it’s not true — and it’s a good example of where voting isn’t going to work.  Most of the good Mexican restaurants in the US will be unknown outside their local area, and any with nationwide recognition will have to be large chains.  The true story is that Taco Bell came top in ‘Brand Equity’ in the Harris poll, which basically means lots of people have heard of it and would be willing to eat there.
• Why People Make Bad Charts (and What to Do When it Happens) from Flowing Data