August 1, 2017

Super 18 Predictions for the Super Rugby Final

Team Ratings for the Super Rugby 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
Hurricanes 16.71 13.22 3.50
Crusaders 14.85 8.75 6.10
Lions 14.49 7.64 6.90
Highlanders 10.62 9.17 1.50
Chiefs 9.62 9.75 -0.10
Brumbies 1.81 3.83 -2.00
Stormers 1.38 1.51 -0.10
Sharks 0.72 0.42 0.30
Blues -0.22 -1.07 0.90
Waratahs -3.81 5.81 -9.60
Bulls -4.96 0.29 -5.20
Jaguares -5.03 -4.36 -0.70
Force -6.97 -9.45 2.50
Cheetahs -9.63 -7.36 -2.30
Reds -9.92 -10.28 0.40
Kings -12.08 -19.02 6.90
Rebels -15.29 -8.17 -7.10
Sunwolves -19.38 -17.76 -1.60

 

Performance So Far

So far there have been 141 matches played, 106 of which were correctly predicted, a success rate of 75.2%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Crusaders vs. Chiefs Jul 29 27 – 13 8.00 TRUE
2 Lions vs. Hurricanes Jul 29 44 – 29 -0.00 FALSE

 

Predictions for the Super Rugby Final

Here are the predictions for the Super Rugby 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 Lions vs. Crusaders Aug 05 Lions 3.60

 

NRL Predictions for Round 22

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.

Current Rating Rating at Season Start Difference
Storm 9.57 8.49 1.10
Cowboys 5.79 6.90 -1.10
Sharks 4.27 5.84 -1.60
Broncos 3.76 4.36 -0.60
Raiders 2.92 9.94 -7.00
Panthers 2.58 6.08 -3.50
Roosters 2.27 -1.17 3.40
Eels 0.68 -0.81 1.50
Sea Eagles -1.33 -2.98 1.60
Dragons -1.45 -7.74 6.30
Rabbitohs -2.85 -1.82 -1.00
Warriors -3.32 -6.02 2.70
Titans -3.91 -0.98 -2.90
Wests Tigers -4.30 -3.89 -0.40
Bulldogs -5.42 -1.34 -4.10
Knights -11.30 -16.94 5.60

 

Performance So Far

So far there have been 152 matches played, 92 of which were correctly predicted, a success rate of 60.5%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Panthers vs. Bulldogs Jul 27 16 – 8 12.30 TRUE
2 Warriors vs. Sharks Jul 28 12 – 26 -1.60 TRUE
3 Eels vs. Broncos Jul 28 28 – 14 -2.10 FALSE
4 Knights vs. Dragons Jul 29 21 – 14 -8.80 FALSE
5 Rabbitohs vs. Raiders Jul 29 18 – 32 -0.10 TRUE
6 Roosters vs. Cowboys Jul 29 22 – 16 -1.20 FALSE
7 Storm vs. Sea Eagles Jul 30 40 – 6 10.80 TRUE
8 Titans vs. Wests Tigers Jul 30 4 – 26 8.50 FALSE

 

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.

Game Date Winner Prediction
1 Bulldogs vs. Eels Aug 03 Eels -2.60
2 Dragons vs. Rabbitohs Aug 04 Dragons 4.90
3 Cowboys vs. Storm Aug 04 Storm -0.30
4 Knights vs. Warriors Aug 05 Warriors -4.00
5 Titans vs. Broncos Aug 05 Broncos -4.20
6 Sharks vs. Raiders Aug 05 Sharks 4.90
7 Sea Eagles vs. Roosters Aug 06 Roosters -0.10
8 Panthers vs. Wests Tigers Aug 06 Panthers 10.40

 

Currie Cup Predictions for Round 3

Team Ratings for Round 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.

Current Rating Rating at Season Start Difference
Lions 6.15 7.41 -1.30
Cheetahs 5.69 4.33 1.40
Western Province 2.77 3.30 -0.50
Blue Bulls 2.05 2.32 -0.30
Sharks 1.80 2.15 -0.30
Pumas -10.45 -10.63 0.20
Griquas -10.75 -11.62 0.90

 

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.

Game Date Score Prediction Correct
1 Lions vs. Griquas Jul 28 48 – 43 22.60 TRUE
2 Sharks vs. Pumas Jul 29 29 – 0 15.80 TRUE
3 Cheetahs vs. Western Province Jul 30 30 – 17 6.40 TRUE

 

Predictions for Round 3

Here are the predictions for Round 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.

Game Date Winner Prediction
1 Sharks vs. Griquas Aug 04 Sharks 17.10
2 Blue Bulls vs. Lions Aug 05 Blue Bulls 0.40
3 Western Province vs. Pumas Aug 05 Western Province 17.70

 

Holiday travel trends

The Herald has a story and video graphic, and a nice interactive graphic on international travel by Kiwis since 1979.  The story is basically good (and even quotes a price corrected for inflation).

Here’s one frame of the video graphic
escape

First, a lot of the world isn’t coloured. There are New Zealanders who have visited say, Germany or Turkey or Egypt, even though these countries never make it into the 1-24,999 colour category. It looks as if the video picks a set of 16 countries and follows just those forward in time: we’re not told how these were picked.

Second, there’s the usual map problem of big things looking big (exacerbated by the Mercator projection). In 1999, more people went to Fiji than the US; more to Samoa than France. A map isn’t good at making these differences visually obvious, though the animation helps. And, tangentially, if you’re going to use almost a third of the map real estate on the region north of 60°, you should notice that Alaska is part of the USA.

The other, more important, issue that’s common to the whole presentation (and which I understand is being updated at the moment) is what the country data actually mean. It seems that it really is holiday data, excluding both business and visiting friends/relatives (comparing the video to this from Figure.NZ), but it’s by “country of main destination”.  If you go to more than one country, only one is counted.  That’s why the interactive shows zero Kiwis travelling to the Vatican City, and it may help explain numbers like 300 for Belgium.

Official statistics usually measure something fairly precise, but it’s not always the thing that you want them to measure.

July 31, 2017

Stat of the Week Competition: July 29 – August 4 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 August 4 2017.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of July 29 – August 4 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…)

July 30, 2017

Coffee news?

In 2015, the Herald said

Drinking the caffeine equivalent of more than four espressos a day is harmful to health, especially for minors and pregnant women, the European Union food safety agency has said.

“It is the first time that the risks from caffeine from all dietary sources have been assessed at EU level,” the EFSA said, recommending that an adult’s daily caffeine intake remain below 400mg a day.

(I quoted it at the time: the link seems to be dead now).

Now we have, under the headline Good news for coffee lovers: Caffeine is harmless, says research

A review of 44 trials dispelled the widespread myth that caffeine, found in tea, coffee and fizzy drinks, is bad for the body.

It found that sticking to the recommended daily amount of 400mg – the equivalent four cups of coffee or eight cups of tea – has no lasting damage on the body.

The recommendation that 400mg/day is generally safe was described as ‘caffeine is dangerous’ in 2015 and ‘caffeine is harmless’ now.

Other not-news about this is the not-new research. Obviously the Daily Mail (the only link) isn’t a research source. The research was published in Complete Nutrition, a professional magazine for UK dieticians. As their website says

Each issue of CN is packed with articles which are practical, educational and topical, and all are written by independent, well-respected authors from across the profession.

That’s a valuable mission for a journal, but it would be surprising if an expert opinion article in a journal like that contained new research worth international headlines.

What are election polls trying to estimate? And is Stuff different?

Stuff has a new election ‘poll of polls’.

The Stuff poll of polls is an average of the most recent of each of the public political polls in New Zealand. Currently, there are only three: Roy Morgan, Colmar Brunton and Reid Research. 

When these companies release a new poll it replaces their previous one in the average.

The Stuff poll of polls differs from others by giving weight to each poll based on how recent it is.

All polls less than 36 days old get equal weight. Any poll 36-70 days old carries a weight of 0.67, 70-105 days old a weight 0.33 and polls greater than 105 days old carry no weight in the average.

In thinking about whether this is a good idea, we’d need to first think about what the poll is trying to estimate and about the reasons it doesn’t get that target quantity exactly right.

Officially, polls are trying to estimate what would happen “if an election were held tomorrow”, and there’s no interest in prediction for dates further forward in time than that. If that were strictly true, no-one would care about polls, since the results would refer only to the past two weeks when the surveys were done.

A poll taken over a two-week period is potentially relevant because there’s an underlying truth that, most of the time, changes more slowly than this.  It will occasionally change faster — eg, Donald Trump’s support in the US polls seems to have increased after James Comey’s claims about Clinton’s emails in the US, and Labour’s support in the UK polls increased after the election was called — but it will mostly change slower. In my view, that’s the thing people are trying to estimate, and they’re trying to estimate it because it has some medium-term predictive value.

In addition to changes in the underlying truth, there is the idealised sampling variability that pollsters quote as the ‘margin of error’. There’s also larger sampling variability that comes because polling isn’t mathematically perfect. And there are ‘house effects’, where polls from different companies have consistent differences in the medium to long term, and none of them perfectly match voting intentions as expressed at actual elections.

Most of the time, in New Zealand — when we’re not about to have an election — the only recent poll is a Roy Morgan poll, because  Roy Morgan polls more much often than anyone else.  That means the Stuff poll of polls will be dominated by the most recent Roy Morgan poll.  This would be a good idea if you thought that changes in underlying voting intention were large compared to sampling variability and house effects. If you thought sampling variability was larger, you’d want multiple polls from a single company (perhaps downweighted by time).  If you thought house effects were non-negligible, you wouldn’t want to downweight other companies’ older polls as aggressively.

Near an election, there are lots more polls, so the most recent poll from each company is likely to be recent enough to get reasonably high weight. The Stuff poll is then distinctive in that it complete drops all but the most recent poll from each company.

Recency weighting, however, isn’t at all unique to the Stuff poll of polls. For example, the pundit.co.nz poll of polls downweights older polls, but doesn’t drop the weight to zero once another poll comes out. Peter Ellis’s two summaries both downweight older polls in a more complicated and less arbitrary way; the same was true of Peter Green’s poll aggregation when he was doing it.  Curia’s average downweights even more aggressively than Stuff’s, but does not otherwise discard older polls by the same company. RadioNZ averages the only the four most recent available results (regardless of company) — they don’t do any other weighting for recency, but that’s plenty.

However, another thing recent elections have shown us is that uncertainty estimates are important: that’s what Nate Silver and almost no-one else got right in the US. The big limitation of simple, transparent poll of poll aggregators is that they say nothing useful about uncertainty.

July 29, 2017

Anything goes

According to a story in the Herald, based on what looks like it might be a bogus poll (press release), you need $5.3 million in Australia now to be considered rich.  If we assumed the number did actually measure something, how surprising would it be?

Before “Who wants to be a millionaire?” was a quiz show franchise, it was a Cole Porter song, from the  1956 movie “High Society”, so that seems a reasonable comparison period. The Australian CPI has gone up by a factor of 15.6 since 1956 (and while Australia didn’t have dollars until 1966, US and Australian dollars were roughly comparable then).

On top of pure currency conversion, though, Australia is richer now than in 1956.  Australia’s GDP in current purchasing-power adjusted dollars is nearly 8 times what it was in 1956. The population has gone from 9.4 million to 24.1 million, so real GDP per capita is up by a factor of about 3.5.

So, a 1956 million would be 15.6 current millions just from inflation, and over $50 million as a share of Australia’s economy: a millionaire in those days was not just rich, but Big Rich — as the song says: “flashy flunkies everywhere… a gigantic yacht… liveried chauffeur.”

We’re not given any real reason to believe the $5.3 million figure — there’s no reason you should rely on it more than your own guess. And ‘millionaire’ isn’t a useful comparison without a lot of additional qualification.

July 27, 2017

Will we ever use this in real life?

From deep in the archives at Language Log

The Pirahã language and culture seem to lack not only the words but also the concepts for numbers, using instead less precise terms like “small size”, “large size” and “collection”. And the Pirahã people themselves seem to be suprisingly uninterested in learning about numbers, and even actively resistant to doing so, despite the fact that in their frequent dealings with traders they have a practical need to evaluate and compare numerical expressions. A similar situation seems to obtain among some other groups in Amazonia, and a lack of indigenous words for numbers has been reported elsewhere in the world.

Many people find this hard to believe. These are simple and natural concepts, of great practical importance: how could rational people resist learning to understand and use them? I don’t know the answer. But I do know that we can investigate a strictly comparable case, equally puzzling to me, right here in the U.S. of A.

From context, you can probably guess where he’s heading

July 25, 2017

Tell them to buy an ad

From the editing blog “Heads Up”

… you don’t need a course in statistics to ask what a writer means by “incident count,” “city” and “occurrence percentage,” not to mention why and how the means are weighted, or even why users of an insurance comparison website would be a good representation of a city where a huge proportion of drivers are uninsured. 

And

This isn’t “fake news” in the 2016 sense; it’s the old-school kind that has always gotten past enough gatekeepers to do its work. The traditional response is “tell them to buy an ad.”