Stats Chat

Team Ratings for Week 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 23 matches played, 15 of which were correctly predicted, a success rate of 65.2%.
Here are the predictions for last week’s games.

Predictions for Week 4

Here are the predictions for Week 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 Week 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 16 matches played, 11 of which were correctly predicted, a success rate of 68.8%.
Here are the predictions for last week’s games.

Predictions for Week 3

Here are the predictions for Week 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.

From 1News on Twitter — with the text “Do these stats make you mad? Pay attention – for every $100 a man earns in NZ, a woman doing the same job earns $8 less.”

As Graeme Edgeler says, the linked article does not substantiate the claim in the text of the tweet at all. The $8 number isn’t referenced in the article and the 9.2% number doesn’t measure earnings differences for two people doing the same job.  It’s also worth considering a second article about the new income statistics linked from the first article. It gives yet another pair of income numbers for men and women, one where the gap is 25% and is slightly smaller than last year.  On top of that,  the 10% and 9.2% claims in the image seem to be about two different income measures, though it’s not really clear where the 10% is sourced.  I’ll also link the new StatsNZ figures, which 1News doesn’t bother to do.

Let’s start with the gender pay gap.  Stats New Zealand’s indicator for gender pay differences is the percentage difference in median hourly earnings.  They have a nice document from 2015 explaining why they define it this way. It’s not a perfect summary — it’s not going to be; it’s a single number — but it’s what they use.  A gender pay gap of 9.2% means that if median hourly earnings for men were $100 (sadly, no) they would be $90.80 for women.  That’s not $8 less; it’s $9.20 less.  The $8 figure doesn’t seem to come from anything in the article, and it’s an understatement of the gap, which is a bit unusual.

Not only is the figure of $8 apparently wrong, the interpretation is wrong.  The gender pay gap isn’t about comparing income for  the same job. That’s good, because a significant part of gender discrimination in pay comes from women’s jobs being paid less.  Looking at the exact same job would understate the pay gap, and would get you into endless arguments over, eg, whether “captain of the national rugby union team” was or was not the same job for Sam Cane and Ruahei Demant.  The interpretation is also wrong because another reason women earn less money than men is they tend to work fewer hours, and that’s not included in the pay gap.

The second linked article discusses median weekly earnings. It has figures of $1320 for men and $1055 for women, a 25% gap.  The increase in median weekly earnings for the population as a whole was 8.8%, which is the only whole-population percentage increase in either article, so it seems like it should be the basis for “we’re all earning nearly 10% more than last year”.  If you dismiss the ‘all’ as journalistic license, and rephrase as “a typical person” then I suppose 8.8% is nearly 10%, but it’s further from 10% than 9.2% is.

Given how different the gender pay gap is when you compute it in different ways, you might  wonder if international comparisons use StatsNZ’s preferred definition. They don’t.  The OECD restricts to people working full time:  hourly and weekly earnings will have the same gaps, but you’ll miss out the pay gap due to more women working in part time jobs.  By the OECD definition, the New Zealand gender pay gap is 6.7% for employees and 30.8% for self-employed people.

The article linked to the tweet is about a campaign to make large businesses disclose their gender pay gap.  I’m fairly generally in favour of this sort of transparency, but when major news outlets seem to not understand or not care what the number means, publicising it might not be as effective as you’d like.

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, 114 of which were correctly predicted, a success rate of 67.9%.
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 Wednesday Game 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 15 matches played, 11 of which were correctly predicted, a success rate of 73.3%.
Here are the predictions for last week’s games.

Predictions for Wednesday Game for Round 2

Here are the predictions for Wednesday Game 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.

• The Slate Money podcast talks about why no-one can agree on the US inflation rate
• Emma Vitz looks at how much this year’s lower school attendance can be explained by Covid
• Guides from the UK NHS explaining risks and benefits of treatment options in various illness: good examples of conveying risks
• What apps are the cool kids using? (Pew Research)
  

Team Ratings for Week 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 8 matches played, 5 of which were correctly predicted, a success rate of 62.5%.
Here are the predictions for last week’s games.

Predictions for Week 2

Here are the predictions for Week 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.

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, 108 of which were correctly predicted, a success rate of 67.5%.
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.

Team Ratings for Wednesday Game for Round 1

Because of the unusual scheduling of games in the NPC, I will be giving a forecast for the Wednesday game on a Tuesday and forecasts for the weekend games after the Wednesday game.

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, 4 of which were correctly predicted, a success rate of 57.1%.
Here are the predictions for last week’s games.

Predictions for Wednesday Game for Round 1

Here are the predictions for Wednesday Game for Round 1. 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.

• There’s a new version of ESR’s Wastewater Covid dashboard. It has information on which variants are being found, by location and over time
• Hashigo Zake, the Wellington craft beer bar, has a new Twitter bot tweeting out the CO2 concentration inside the bar. I summarised a couple of days of it:
  
• How far can you go by train in 5 hours? A map of Europe
• How likely are people to win the lottery: the Washington Post did a quiz
• Jamie Morton in the Herald has a good discussion of the Stats NZ review of the population denominator used in Covid vaccine stats.  The HSU undercounts somewhat, especially for Māori and Pacific Peoples, but it has the virtue of counting ethnicity the same way that the vaccination data does, and of including people in NZ who are not residents.