Stats Chat

Team Ratings for Week 3

My automatic updating of scores failed, sorry. Correct results now I hope.

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 6 matches played, 5 of which were correctly predicted, a success rate of 83.3%.
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.

Team Ratings for Round 12

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 59 matches played, 33 of which were correctly predicted, a success rate of 55.9%.
Here are the predictions for last week’s games.

Predictions for Round 12

Here are the predictions for Round 12. 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 14

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 77 matches played, 52 of which were correctly predicted, a success rate of 67.5%.
Here are the predictions for last week’s games.

Predictions for Round 14

Here are the predictions for Round 14. 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.

• Good explanation by Jarrod Gilbert at newsroom of why gang member statistics tend to be inflated: it’s harder to count people leaving.
• Eric Crampton (correctly) argues that the Climate Commission should just publish its models so people can see what they’re sensitive to. Peter Ellis (who would know) thinks this should be perfectly feasible. I reviewed an air pollution epidemiology initiative that did this sort of thing with analysis code and pollution and health information from the 90 largest US cities, nearly two decades ago. Back then, it was kind of notable; now, not so much.
• The New York Times had a front-page visualisation of 500,000 Covid deaths, as dots over time. People in NZ aren’t the best audience for deciding how well something like this works, but there was an interesting Twitter thread criticising it.
• Truncating bar charts — failing to show them all the way down to zero — is bad in theory and I often complain about it here.  New research suggests it’s actually bad in practice, too.  It conveys inaccurate information even to trained people and even to people who are warned in advance.
• For a bit of nerd fun: Iceberger — draw an iceberg and see which way it will end up floating (motivated by the Campaign For Sideways Icebergs)
• Data collection is hard:  “I’m interviewing 5 year olds and their best friend is a pretzel, so it’s hard to map their social networks.” — Jason Chow at an education conference in the US, quoted on Twitter
• Excess mortality in 2020 in the US (from Kieran Healy)
  

Team Ratings for Postponed Games

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 122 matches played, 81 of which were correctly predicted, a success rate of 66.4%.
Here are the predictions for last week’s games.

Predictions for Postponed Games

Here are the predictions for Postponed Games. 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 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 2 matches played, 2 of which were correctly predicted, a success rate of 100%.
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 Week 1

I was a bit unsure as to how to deal with Super Rugby this year but will treat it as one competition not separate Australian and New Zealand competitions followed by a joint competition. I will give predictions each week rather than by the designated rounds. Predictions are based on all games played last year in either the initial Super Rugby competition and then the separate country competitions. I am not sure how that will work for the combined competition due to start in May.

This forecast is obviously after the fact but I decided to publish it for completeness.

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.

Predictions for Week 1

Here are the predictions for Week 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.

Team Ratings for Round 11

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 53 matches played, 30 of which were correctly predicted, a success rate of 56.6%.
Here are the predictions for last week’s games.

Predictions for Round 11

Here are the predictions for Round 11. 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 13

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 71 matches played, 48 of which were correctly predicted, a success rate of 67.6%.
Here are the predictions for last week’s games.

Predictions for Round 13

Here are the predictions for Round 13. 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 combination of descriptions about NZ as a small, empty, remote island and news about Tesla buying lots of Bitcoin made me compare this graph of blockchain transactions per day (from Wikipedia, by user HocusPocus00)

with an infographic from paymentsnz for 2019.

There are about a third of a million Bitcoin transactions per day, and nearly 1.25 million Ethereum transactions per day.  How does that compare with the NZ economy?

We get through about a million direct electronic transfers per day  — when you send money from your bank account to someone else’s, like your plumber or a guy on TradeMe or splitting an accomodation bill with your mates.  There’s about 360,000 direct debit transfers — when Vodafone sucks your phone and broadband bill out of your bank account.  We have about 2.8 million EFTPOS debit transactions per day, and about 2.3 million credit card and PayWave transactions per day at point of sale (that was 2019; I’m guessing Paywave is up and EFTPOS is down in 2020).

Now, a single Bitcoin transaction can be more complicated than a single debit card purchase, and it might actually package a whole lot of transactions together (and, no, this isn’t quite like how a single supermarket debit card transaction might include toothpaste and cheese). It’s still a little surprising how few blockchain transactions there are.