Posts from October 2020 (25)

Team Ratings for Round 22

OK, so this is late, with only one game to play. It has been a crazy time trying to keep up with changes to fixtures. The Gloucester versus Northampton game was forfeited by Northampton and the result given to Gloucester 20-0. I think I will follow FlashScore on that game and ignore the allocated result.

I have also missed the start of the Pro14. I haven’t even sorted out the fixtures for that yet, or chosen parameters, but will catch up tomorrow 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 126 matches played, 83 of which were correctly predicted, a success rate of 65.9%.
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 Finals 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 164 matches played, 113 of which were correctly predicted, a success rate of 68.9%.
Here are the predictions for last week’s games.

Predictions for Finals Week 2

Here are the predictions for Finals 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 5

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 28 matches played, 17 of which were correctly predicted, a success rate of 60.7%.
Here are the predictions for last week’s games.

Predictions for Round 5

Here are the predictions for Round 5. 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 long interactive in the Herald prompted by the 2000th Lotto draw, earlier this week.  Among other interesting things, it has a graph of purporting to show the ‘luckiest’ regions

Aucklanders have won more money in Lotto prizes than any other region — roughly three times as much as either Canterbury or Wellington. By an amazing coincidence, Auckland has roughly three times the population of Canterbury or Wellington.  The bar chart is only showing population. Auckland is not punching above its weight.

Wins per capita are over on the side, and are much less variable. Some of this will be that people in different regions play Lotto more or less ofter; some probably was luck. It’s possible that some variation is due to strategy — not variation in whether you win, but in how much.

Perhaps more importantly, the ‘wins per capita’ figure is gross winnings, not net winnings.   Lotto NZ didn’t release details of expenditures, but 2000 draws is a long enough period of time that we can work with averages and get a rough estimate.  As the Herald reports, about 55c in the dollar goes in prizes, so the gross winnings will average about 55% of revenue and the net winnings will average -45% of revenue, or -9/11 times gross winnings.

So: as an estimate over the past 2000 draws, the ‘luckiest’ NZ regions

Some of the smaller regions are probably misrepresented here by good/bad luck — if Lotto NZ released actual data on revenue by region I’d be happy to do a more precise version

Marc Daalder at newsroom has a very good piece about genome sequencing and its role in handling the Auckland outbreak.

One thing I want to highlight is this family tree. It’s the B.1.1.1 clade of the virus, a particular viral subfamily

The samples from the Auckland outbreak are that blue/green cluster at the top. They’re all more closely related to each other than to any other sample that has been sequenced in NZ or anywhere else in the world, so we know they had a recent common ancestor: the virus that started the outbreak.

I’m mentioning this because I saw discussion on Twitter over the past month or so of the ‘B.1.1.1 clade’ and whether there had been other earlier cases from that clade in NZ and whether that meant the government was hiding something.  B.1.1.1 is a pretty broad family, going back to a common ancestor in late February.   If two samples are from different broad clades, they are different incursions to NZ, but if they’re from the same broad clade they aren’t necessarily the same incursion. You need the full sequence to say how closely viruses are related.