September 26, 2017

Currie Cup Predictions for Round 12

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.

Current Rating Rating at Season Start Difference
Sharks 5.31 2.15 3.20
Western Province 3.84 3.30 0.50
Lions 3.02 7.41 -4.40
Cheetahs 2.73 4.33 -1.60
Blue Bulls 0.16 2.32 -2.20
Pumas -6.51 -10.63 4.10
Griquas -11.29 -11.62 0.30

 

Performance So Far

So far there have been 33 matches played, 23 of which were correctly predicted, a success rate of 69.7%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Sharks vs. Blue Bulls Sep 23 18 – 5 9.00 TRUE
2 Western Province vs. Griquas Sep 23 55 – 27 18.90 TRUE
3 Cheetahs vs. Pumas Sep 23 33 – 32 14.70 TRUE

 

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.

The Cheetahs have been playing in the Pro 14 competition and fielding a second or third team in the Currie Cup. With little experience so far it is difficult to estimate the difference in expected points this might make. After 3 such games a quick estimate is that they might score 23 points less than if they were fielding their best team, but losses so far will have dropped their rating a couple of points already. I would guess that a difference of 21 points might be appropriate, so instead of the points difference being -9.50 below, it might be 11.5 and a win to Griquas.

Game Date Winner Prediction
1 Sharks vs. Lions Sep 29 Sharks 6.80
2 Griquas vs. Cheetahs Sep 30 Cheetahs -9.50
3 Blue Bulls vs. Western Province Oct 01 Blue Bulls 0.80

 

Mitre 10 Cup Predictions for Round 7

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.

Current Rating Rating at Season Start Difference
Canterbury 19.36 14.78 4.60
Wellington 10.36 -1.62 12.00
Taranaki 7.63 7.04 0.60
North Harbour 5.30 -1.27 6.60
Tasman 4.84 9.54 -4.70
Otago 3.81 -0.34 4.20
Counties Manukau -0.79 5.70 -6.50
Manawatu -1.32 -3.59 2.30
Bay of Plenty -1.63 -3.98 2.30
Auckland -2.13 6.11 -8.20
Waikato -4.08 -0.26 -3.80
Northland -5.07 -12.37 7.30
Hawke’s Bay -14.93 -5.85 -9.10
Southland -23.97 -16.50 -7.50

 

Performance So Far

So far there have been 46 matches played, 31 of which were correctly predicted, a success rate of 67.4%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Bay of Plenty vs. Southland Sep 20 57 – 0 20.00 TRUE
2 Otago vs. Auckland Sep 21 34 – 26 10.40 TRUE
3 Manawatu vs. Northland Sep 22 39 – 25 6.40 TRUE
4 North Harbour vs. Canterbury Sep 23 28 – 41 -9.40 TRUE
5 Waikato vs. Wellington Sep 23 10 – 34 -7.50 TRUE
6 Hawke’s Bay vs. Taranaki Sep 23 17 – 48 -15.80 TRUE
7 Bay of Plenty vs. Counties Manukau Sep 24 31 – 31 0.50 FALSE
8 Tasman vs. Southland Sep 24 50 – 17 29.40 TRUE

 

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.

Game Date Winner Prediction
1 Northland vs. Otago Sep 27 Otago -4.90
2 Taranaki vs. Tasman Sep 28 Taranaki 6.80
3 North Harbour vs. Hawke’s Bay Sep 29 North Harbour 24.20
4 Southland vs. Manawatu Sep 30 Manawatu -18.60
5 Auckland vs. Bay of Plenty Sep 30 Auckland 3.50
6 Canterbury vs. Waikato Sep 30 Canterbury 27.40
7 Wellington vs. Otago Oct 01 Wellington 10.50
8 Counties Manukau vs. Northland Oct 01 Counties Manukau 8.30

 

NRL Predictions for the Grand Final

Team Ratings for the Grand Final

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.

Current Rating Rating at Season Start Difference
Storm 15.88 8.49 7.40
Broncos 5.08 4.36 0.70
Cowboys 4.71 6.90 -2.20
Raiders 4.07 9.94 -5.90
Panthers 2.80 6.08 -3.30
Sharks 2.55 5.84 -3.30
Eels 1.60 -0.81 2.40
Roosters 0.21 -1.17 1.40
Dragons -0.94 -7.74 6.80
Sea Eagles -1.11 -2.98 1.90
Bulldogs -3.55 -1.34 -2.20
Wests Tigers -3.72 -3.89 0.20
Rabbitohs -3.84 -1.82 -2.00
Warriors -7.23 -6.02 -1.20
Titans -9.03 -0.98 -8.10
Knights -9.54 -16.94 7.40

 

Performance So Far

So far there have been 200 matches played, 120 of which were correctly predicted, a success rate of 60%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Storm vs. Broncos Sep 22 30 – 0 11.40 TRUE
2 Roosters vs. Cowboys Sep 23 16 – 29 1.30 FALSE

 

Predictions for the Grand Final

Here are the predictions for the Grand Final. 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.

Game Date Winner Prediction
1 Storm vs. Cowboys Oct 01 Storm 11.20

 

September 25, 2017

Stat of the Week Competition: September 23 – 29 2017

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

  • Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday September 29 2017.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of September 23 – 29 2017 inclusive.
  • Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)

September 24, 2017

The polls

So, how did the polls do this time? First, the main result was predicted correctly: either side needs a coalition with NZ First.

In more detail, here are the results from Peter Ellis’s forecasts from the page that lets you pick coalitions.

Each graph has three arrows. The red arrow shows the 2014 results. The blue/black arrow pointing down shows the current provisional count and the implied number of seats, and the horizontal arrow points to Graeme Edgeler’s estimate of what the special votes will do (not because he claims any higher knowledge, but because his estimates are on a web page and explain how he did it).

First, for National+ACT+UnitedFuture

national

Second, for Labour+Greens

labgrn

The result is well within  the uncertainty range of the predictions for Labour+Greens, and not bad for  National. This isn’t just because NZ politics is easy to predict: the previous election’s results are much further away. In particular, Labour really did gain a lot more votes than could reasonably have been expected a few months ago.

 

Update: Yes, there’s a lot of uncertainty. And, yes, that does  mean quoting opinion poll results to the nearest 0.1% is silly.

September 20, 2017

Democracy is coming

Unless someone says something really annoyingly wrong about polling in the next few days, I’m going to stop commenting until Saturday night.

Some final thoughts:

  • The election looks closer than NZ opinion polling is able to discriminate. Anyone who thinks they know what the result will be is wrong.
  • The most reliable prediction based on polling data is that the next government will at least need confidence and supply from NZ First. Even that isn’t certain.
  • It’s only because of opinion polling that we know the election is close. It would be really surprising if Labour didn’t do a lot better than the 25% they managed in the 2014 election — but we wouldn’t know that without the opinion polls.

 

 

Takes two to tango

Right from the start of StatsChat we’ve looked at stories about how men or women have more sexual partners. There’s another one in the Herald as a Stat of the Week nomination.

To start off, there’s the basic adding-up constraint: among exclusively heterosexual people, or restricted to opposite-sex partners, the two averages are necessarily identical over the whole population.

This survey (the original version of the story is here) doesn’t say that it just asked about opposite-sex partners, so the difference could be true.  On average, gay men have more sexual partners and lesbians have fewer sexual partners, so you’d expect a slightly higher average for all men than for all women.  Using binary classifications for trans and non-binary people will also stop the numbers matching exactly.

But there are bigger problems. First, 30% of women and 40% of men admit this is something they lie about. And while the rest claim they’ve never lied about it, well, they would, wouldn’t they?

And the survey doesn’t look all that representative.  The “Methodology” heading is almost entirely unhelpful — it’s supposed to say how you found the people, not just

We surveyed 2,180 respondents on questions relating to sexual history. 1,263 respondents identified as male with 917 respondents identifying as female. Of these respondents, 1,058 were from the United States and another 1,122 were located within Europe. Countries represented by fewer than 10 respondents and states represented by fewer than five respondents were omitted from results.

However, the sample is clearly not representative by gender or location, and the fact that they dropped some states and countries afterwards suggests they weren’t doing anything to get a representative sample.

The Herald has a bogus clicky poll on the subject. Here’s what it looks like on my desktop

sex

On my phone it gets a couple more options visible, but not all of them. It’s probably less reliable than the survey in the story, but not by a whole lot.

This sort of story can be useful in making people more willing to talk about their sexual histories, but the actual numbers don’t mean a lot.

September 19, 2017

Briefly

  • During the Cold War, there were a few occasions where a nuclear war could easily have started if one person hadn’t got in the way. One of those people was Stanislav Petrov. He died this week.
  • I saw a pharmacy in Ponsonby advertising “Ultrasound bone density screening for all ages”. There’s no way screening for osteoporosis makes sense ‘for all ages’, even if it was free (which it isn’t).
  • As I’ve mentioned a few times, the UK has an independent Statistics Authority whose chair is supposed to monitor and rebuke misuses of official statistics. The chair, Sir David Norgrove, criticised Boris Johnson over the £350m “savings” from Brexit he has kept repeating. We don’t have anything similar, sadly.
  • If you’re interested in the history of data journalism, you could do worse than reading Alberto Cairo’s PhD thesis. Dr Cairo is a former data journalist, current professor of visual journalism at the University of Miami, and one of next year’s Ihaka Lecture speakers here in Auckland.
  • Janelle Shane has a blog with examples of neural networks generalising from a wide range of inputs (recipes, hamster names, craft beers). Her current post is on D&D spell names, and shows the importance of a large input set for these networks: would you prefer your character to cast “Plonting Cloud” or “Wall of Storm”?
  • Kieran Healy, of Duke University, has an online book Data Visualization for Social Science. Yes, if you think you recognise the name, it’s him.
  • The American Statistical Association and the New York Times are partnering in a new monthly feature, “What’s Going On in This Graph?”

Denominators and BIGNUMs

billennial

It’s pretty obvious that Bon Appétit has just confused averages and totals here.

So, what is the average? There were about 75 million millennials in the US in 2016 (we can probably assume  Bon Appétit doesn’t care about other countries), so we’re looking at $1280/year, or about $25/week. Which actually seems pretty low as an average.  The US as a whole spent $1.46 trillion on food and beverages in 2014, which is about $4500/person/year or about $87/week.

As with so much generation-mongering, asking about the facts is missing the intended purpose of the story, which is to recycle some stereotypes about lazy/wasteful youth.

The story links to another, about a new book “Generation Yum”

Turow characterized the quintessential Millennial experience this way: “You got into a top tier high school, you hustled through college—you’ve done everything society told you—and you’re not rewarded. 

When “get into a top-tier high school” is a quintessential generational experience it’s clear we’re not even trying to go beyond unrepresentative stereotypes.  In which case, hold the numbers.

NRL Predictions for the Preliminary Finals

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.

Current Rating Rating at Season Start Difference
Storm 14.43 8.49 5.90
Broncos 6.53 4.36 2.20
Raiders 4.07 9.94 -5.90
Cowboys 3.58 6.90 -3.30
Panthers 2.80 6.08 -3.30
Sharks 2.55 5.84 -3.30
Eels 1.60 -0.81 2.40
Roosters 1.34 -1.17 2.50
Dragons -0.94 -7.74 6.80
Sea Eagles -1.11 -2.98 1.90
Bulldogs -3.55 -1.34 -2.20
Wests Tigers -3.72 -3.89 0.20
Rabbitohs -3.84 -1.82 -2.00
Warriors -7.23 -6.02 -1.20
Titans -9.03 -0.98 -8.10
Knights -9.54 -16.94 7.40

 

Performance So Far

So far there have been 198 matches played, 119 of which were correctly predicted, a success rate of 60.1%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Broncos vs. Panthers Sep 15 13 – 6 7.30 TRUE
2 Eels vs. Cowboys Sep 16 16 – 24 3.30 FALSE

 

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.

Game Date Winner Prediction
1 Storm vs. Broncos Sep 22 Storm 11.40
2 Roosters vs. Cowboys Sep 23 Roosters 1.30