Stats Chat

Team Ratings for the Preliminary Finals

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 198 matches played, 128 of which were correctly predicted, a success rate of 64.6%.
Here are the predictions for last week’s games.

Predictions for the Preliminary Finals

Here are the predictions for the Preliminary Finals. 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 8

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 49 matches played, 32 of which were correctly predicted, a success rate of 65.3%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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.

I was hoping to be able to release predictions in batches of 8 matches, but some matches which could be predicted already would then not have a prediction. Instead I have decided to predict all future games, and update as required to ensure that before a team plays, my predictions will take into account all previous games.

Observe below that means for example that I will provide updated predictions before the Georgia vs. Uruguay game to take into account the game played by Uruguay against Fiji.

Team Ratings 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, 8 of which were correctly predicted, a success rate of 100%.
Here are the predictions for the previous games.

Predictions 2

Here are the updated predictions. 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.

NZ is having local elections.  Some of the elections work by single transferable vote.  The working of STV are not precisely statistics, but they’re nerdy enough in a vaguely mathematical way to be on topic.  And I’ve run the counting of STV elections (for the Monash Association of Students), so I have a pretty good grasp of how it works.

Rather than explain it to you, though, I’m going to outsource to Graeme Edgeler’s nice and accurate explanation.

• How to teach machines humanity and ethics (Russell Brown’s ‘Actually Interesting’ AI podcast, sponsored by Microsoft)
• Beautiful interactive graphs of mortality rate in England & Wales, by age and over time.
• Data Trusts as a model for data governance?
• A Twitter thread of deliberately awful graphics
• Washington Post graphics on school diversity in the US
• Nathan Yau at Flowing Data looks at graphing sleep schedule for people of different ages
• The first Census 2018 data release is on Monday

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 196 matches played, 126 of which were correctly predicted, a success rate of 64.3%.
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 7

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 42 matches played, 29 of which were correctly predicted, a success rate of 69%.
Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. 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.

Hadley is Chief Scientist of RStudio, an alumnus and adjunct Professor here in the Department of Statistics, and the recent winner of the Presidents’ Award from the Committee of Presidents of Statistical Societies, one of the top honours in statistics.

That’s two celebrity statisticians interviewed by Kim Hill in consecutive weeks, which must be some sort of record.

Listen here

Team Ratings for Week 1

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.

Publication Schedule

I aim to publish RWC predictions on a schedule which ensures that team ratings have been calculated using all prior results. With 5 teams per pool and 4 pools, only 8 games can be played without the necessity for a team to play twice and that is how the pool games schedule appears to have been arranged. I thus intend to publish new predictions after each group of 8 games. There may be some variations in when I publish because I am currently on a cycling holiday in Europe.

For the NRL and Mitre 10 Cup predictions, I aim to publish on a Tuesday each week as usual.

We haven’t had one of these for a while, but there’s some dodgy-looking extrapolation going on in the Keep New Zealand Beautiful litter audit. The audit itself is a good idea: measure litter in a detailed and reproducible way, so you can compare amounts now to amounts in the future and see whether things are getting greener and cleaner.  And I don’t have any problems with how they conducted the survey.

But.  The report (PDF) says (p18)

10,269,090,000 LITTERED CIGARETTE BUTTS polluting our ecosystem

and the Herald story says

Despite drops in smoking rates, discarded cigarette butts remained a big headache: some 10,269,090,000 were picked up, or 2,142 for every person in the country.

There weren’t 10 billion cigarette butts picked up. That would take a while.  There were 39 cigarette butts picked up per 1000 square metres of land surveyed. With 10,000 477,000 square metres surveyed [update: I was confused by Table 2 in the report, which says 10,000 but is just illustrating the calculation (that’s the Table 2 on p25, not one of the others)], that comes to 390 18600.  The detailed breakdowns in the report are fine, but there are also these extrapolations, where the amount of litter per 1000 square metres is scaled up by the number of 1000 square metre patches it would take to cover the whole country — about 260,000,000.

And, similarly, from the Herald quoting KNZB chief executive Heather Sanderson

“Extrapolated, that means 265,324,848 litres of illegal dumping – enough to fill 2,123 rail carriages, which if you stack them on top of each other, would be as high as 151 Sky Towers.”

is obtained by finding just under 1 litre (or 0.001 cubic metres) per 1000 square metres in the survey, and scaling up to the whole country. (Also, those imaginary rail carriages are being stacked end on end, which is probably not good for them)

Scaling up like this is how survey statistics works, but only if the sites you survey are an equal-probability random sample of the area  The report doesn’t say they were, and it seems pretty unlikely, because it’s quite hard to get to a lot of randomly chosen bits of New Zealand, and these places  — whether they’re up inaccessible mountains or in the middle of a big dairy farm  — will tend to have less litter.

[Update: it’s obvious not an equal-probability sample of NZ; it could be some sort of stratified sample of the types of areas they were focusing on]

[Update, 13 September: Keep NZ Beautiful has modified the report to take out the dodgy extrapolations.  Congratulations.  The Herald hasn’t modified their story, though.]