Barcharts with delusions of grandeur
The cricket graphics system now allows 3-d barcharts projected over the playing field, and casting actual virtual shadows.
Yeah, nah.
The cricket graphics system now allows 3-d barcharts projected over the playing field, and casting actual virtual shadows.
Yeah, nah.

The New York Times has a well-deserved reputation for data journalism, but anyone can have a bad day. There’s a piece by Steven Johnson on the non-extinction of the music industry (which I think makes some good points), but which the Future of Music Coalition doesn’t like at all. And they also have some good points.
In particular, Johnson says
“According to the OES, in 1999 there were nearly 53,000 Americans who considered their primary occupation to be that of a musician, a music director or a composer; in 2014 more than 60,000 people were employed writing, singing, or playing music. That’s a rise of 15 percent.”
He’s right. This is a graph (not that you really need one)
The Future of Music Coalition give the numbers for each year, and they’re interesting. Here’s a graph of the totals:
There isn’t a simple increase; there’s a weird two-humped pattern. Why?
Well, if you look at the two categories, “Music Directors and Composers” and “Musicians and Singers”, making up the total, it’s quite revealing
The larger category, “Musicians and Singers”, has been declining. The smaller category, “Music Directors and Composers” was going up slowly, then had a dramatic three-year, straight-line increase, then decreased a bit.
Going into the Technical Notes for the estimates (eg, 2009), we see
May 2009 estimates are based on responses from six semiannual panels collected over a 3-year period
That means the three-year increase of 5000 jobs/year is probably a one-off increase of 15,000 jobs. Either the number of “Music Directors and Composers” more than doubled in 2009, or more likely there was a change in definitions or sampling approach. The Future of Music Coalition point out that Bureau of Labor Statistics FAQs say this is a problem (though they’ve got the wrong link: it’s here, question F.1)
Challenges in using OES data as a time series include changes in the occupational, industrial, and geographical classification systems
In particular, the 2008 statistics estimate only 390 of these people as being employed in primary and secondary schools; the 2009 estimate is 6000, and the 2011 estimate is 16880. A lot of primary and secondary school teachers got reclassified into this group; it wasn’t a real increase.
When the school teachers are kept out of “Music Directors and Composers”, to get better comparability across years, the change is from 53000 in 1999 to 47000 in 2014. That’s not a 15% increase; it’s an 11% decrease.
Official statistics agencies try not to change their definitions, precisely because of this problem, but they do have to keep up with a changing world. In the other direction, I wrote about a failure to change definitions that led the US Census Bureau to report four times as many pre-schoolers were cared for by fathers vs mothers.
From Stuff: “Korean pears are the best way to prevent hangovers, say scientists.”
This is precisely not what scientists say; in fact, the scientist in question is even quoted (in the last line of the story) as not saying that.
Meanwhile, as a responsible scientist, she reminded that abstaining from excess alcohol consumption is the only certain way to avoid a hangover.
At least Stuff got ‘prevention’ in the headline. Many other sources, such as the Daily Mail, led with claims of a “hangover cure.” The Mail also illustrated the story with a photo of the wrong species: the research was on the Asian species Pyrus pyrifolia, rather than the European pear Pyrus communis. CSIRO hopes that European pears are effective, since that’s what Australia has vast quantities of, but they weren’t tested.
What Stuff doesn’t seem to have noticed is that this isn’t a new CSIRO discovery. The blog post certainly doesn’t go out of its way to make that obvious, but right at the bottom, after the cat picture, the puns, and the Q&A with the researcher, you can read
Manny also warns this is only a preliminary scoping study, with the results yet to be finalised. Ultimately, her team hope to deliver a comprehensive review of the scientific literature on pears, pear components and relevant health measures.
That is, the experimental study on Korean pears isn’t new research done at CSIRO. It’s research done in Korea, and published a couple of years ago. There’s nothing wrong with this, though it would have been nice to give credit, and it would have made the choice of Korean pears less mysterious.
The Korean researchers recruited a group of young Korean men, and gave alcohol (in the form of shoju), preceded by either Korean pear juice or placebo pear juice (pear-flavoured sweetened water). Blood chemistry studies, as well as research in mice by the same group, suggest that the pear juice speeds up the metabolism of alcohol and acetaldehyde. This didn’t prevent hangovers, but it did seem to lead to a small reduction in hangover severity.
The study was really too small to be very convincing. Perhaps more importantly, the alcohol dose was nearly eleven standard drinks (540ml of 20% alcohol) over a short period of time, so you’d hope it was relevant to a fairly small group of people. Even in Australia.
I’ve posted a few times about the maps, word clouds, and so on that show the most distinctive words by gender or state — sometimes they are even mislabelled as the “most common” words. As I explained, these are often very rare words; it’s just that they are slightly less rare in one group than in the others.
An old post from the XKCD blog gives a really good example. Randall Munroe set up a survey to show people colours and ask for the colour name. He got five million responses, from over 200,000 sessions, and came up with nearly 1000 reasonably well-characterised colours. You can download the complete data, if you care.
The survey asked participants about their chromosomal sex, because two of the colour receptor genes are on the X-chromosome and this is linked to colour blindness (and possibly to tetrachromatic vision). It turned out that the basic colour names were very similar between male and female respondents, though women were slightly more likely to use modifiers (“lime green” vs “green”).
However, Munroe also looked at the responses that differed most in frequency between men and women. These were all uncommon responses, but all from multiple people, and after extensive spam filtering.
You can probably guess which group is which:
(Presumably this is a gender effect, not an X-linked language defect.)
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 | |
|---|---|---|---|
| Tasman | 12.79 | 12.86 | -0.10 |
| Canterbury | 11.10 | 10.90 | 0.20 |
| Counties Manukau | 6.75 | 7.86 | -1.10 |
| Taranaki | 5.78 | 7.70 | -1.90 |
| Auckland | 4.50 | 5.14 | -0.60 |
| Hawke’s Bay | 1.41 | -0.57 | 2.00 |
| Manawatu | -0.41 | -1.52 | 1.10 |
| Wellington | -2.70 | -4.62 | 1.90 |
| Otago | -5.04 | -4.84 | -0.20 |
| Southland | -5.37 | -6.01 | 0.60 |
| Northland | -5.61 | -3.64 | -2.00 |
| Waikato | -6.88 | -6.96 | 0.10 |
| Bay of Plenty | -9.39 | -9.77 | 0.40 |
| North Harbour | -10.92 | -10.54 | -0.40 |
So far there have been 7 matches played, 5 of which were correctly predicted, a success rate of 71.4%.
Here are the predictions for last week’s games.
| Game | Date | Score | Prediction | Correct | |
|---|---|---|---|---|---|
| 1 | Southland vs. Auckland | Aug 13 | 23 – 23 | -7.20 | FALSE |
| 2 | Waikato vs. Tasman | Aug 14 | 20 – 35 | -15.80 | TRUE |
| 3 | Bay of Plenty vs. North Harbour | Aug 14 | 20 – 11 | 4.80 | TRUE |
| 4 | Taranaki vs. Wellington | Aug 15 | 14 – 19 | 16.30 | FALSE |
| 5 | Otago vs. Canterbury | Aug 15 | 24 – 38 | -11.70 | TRUE |
| 6 | Counties Manukau vs. Manawatu | Aug 16 | 36 – 35 | 13.40 | TRUE |
| 7 | Hawke’s Bay vs. Northland | Aug 16 | 39 – 10 | 7.10 | TRUE |
Here are the predictions for Round 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.
| Game | Date | Winner | Prediction | |
|---|---|---|---|---|
| 1 | North Harbour vs. Wellington | Aug 20 | Wellington | -4.20 |
| 2 | Tasman vs. Bay of Plenty | Aug 21 | Tasman | 26.20 |
| 3 | Manawatu vs. Waikato | Aug 22 | Manawatu | 10.50 |
| 4 | Northland vs. Southland | Aug 22 | Northland | 3.80 |
| 5 | Otago vs. Hawke’s Bay | Aug 22 | Hawke’s Bay | -2.40 |
| 6 | Auckland vs. Taranaki | Aug 23 | Auckland | 2.70 |
| 7 | Canterbury vs. Counties Manukau | Aug 23 | Canterbury | 8.40 |
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 | |
|---|---|---|---|
| Roosters | 10.08 | 9.09 | 1.00 |
| Broncos | 7.44 | 4.03 | 3.40 |
| Rabbitohs | 5.66 | 13.06 | -7.40 |
| Storm | 5.48 | 4.36 | 1.10 |
| Cowboys | 5.34 | 9.52 | -4.20 |
| Sea Eagles | 3.34 | 2.68 | 0.70 |
| Bulldogs | 2.58 | 0.21 | 2.40 |
| Dragons | 0.57 | -1.74 | 2.30 |
| Raiders | -0.71 | -7.09 | 6.40 |
| Sharks | -1.84 | -10.76 | 8.90 |
| Panthers | -2.87 | 3.69 | -6.60 |
| Warriors | -4.28 | 3.07 | -7.40 |
| Eels | -5.42 | -7.19 | 1.80 |
| Knights | -6.71 | -0.28 | -6.40 |
| Wests Tigers | -6.79 | -13.13 | 6.30 |
| Titans | -10.53 | -8.20 | -2.30 |
So far there have been 168 matches played, 96 of which were correctly predicted, a success rate of 57.1%.
Here are the predictions for last week’s games.
| Game | Date | Score | Prediction | Correct | |
|---|---|---|---|---|---|
| 1 | Cowboys vs. Rabbitohs | Aug 13 | 18 – 31 | 5.20 | FALSE |
| 2 | Broncos vs. Dragons | Aug 14 | 32 – 6 | 7.30 | TRUE |
| 3 | Wests Tigers vs. Knights | Aug 15 | 18 – 24 | 4.40 | FALSE |
| 4 | Panthers vs. Warriors | Aug 15 | 24 – 10 | 4.00 | TRUE |
| 5 | Roosters vs. Eels | Aug 15 | 28 – 18 | 19.90 | TRUE |
| 6 | Raiders vs. Sea Eagles | Aug 16 | 36 – 26 | -2.90 | FALSE |
| 7 | Bulldogs vs. Titans | Aug 16 | 36 – 14 | 15.10 | TRUE |
| 8 | Sharks vs. Storm | Aug 17 | 2 – 30 | -0.60 | TRUE |
Here are the predictions for Round 24. 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 | Dragons vs. Panthers | Aug 20 | Dragons | 6.40 |
| 2 | Rabbitohs vs. Bulldogs | Aug 21 | Rabbitohs | 6.10 |
| 3 | Sharks vs. Wests Tigers | Aug 22 | Sharks | 7.90 |
| 4 | Warriors vs. Cowboys | Aug 22 | Cowboys | -5.60 |
| 5 | Roosters vs. Broncos | Aug 22 | Roosters | 5.60 |
| 6 | Titans vs. Raiders | Aug 23 | Raiders | -6.80 |
| 7 | Sea Eagles vs. Eels | Aug 23 | Sea Eagles | 11.80 |
| 8 | Storm vs. Knights | Aug 24 | Storm | 15.20 |
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 | |
|---|---|---|---|
| Western Province | 5.04 | 4.93 | 0.10 |
| Lions | 3.84 | 3.04 | 0.80 |
| Sharks | 2.79 | 3.43 | -0.60 |
| Blue Bulls | 1.27 | 0.17 | 1.10 |
| Cheetahs | -2.27 | -1.75 | -0.50 |
| Pumas | -6.27 | -6.47 | 0.20 |
| Griquas | -8.49 | -7.81 | -0.70 |
| Kings | -9.80 | -9.44 | -0.40 |
So far there have been 8 matches played, 6 of which were correctly predicted, a success rate of 75%.
Here are the predictions for last week’s games.
| Game | Date | Score | Prediction | Correct | |
|---|---|---|---|---|---|
| 1 | Lions vs. Pumas | Aug 14 | 44 – 27 | 13.20 | TRUE |
| 2 | Blue Bulls vs. Griquas | Aug 14 | 36 – 12 | 12.60 | TRUE |
| 3 | Western Province vs. Cheetahs | Aug 15 | 9 – 3 | 11.30 | TRUE |
| 4 | Sharks vs. Kings | Aug 15 | 33 – 25 | 16.60 | TRUE |
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 | Kings vs. Pumas | Aug 21 | Pumas | -0.00 |
| 2 | Griquas vs. Cheetahs | Aug 21 | Cheetahs | -2.70 |
| 3 | Sharks vs. Lions | Aug 22 | Sharks | 2.40 |
| 4 | Blue Bulls vs. Western Province | Aug 22 | Western Province | -0.30 |
What are you doing on October 20? Statisticians all over the world will be showcasing the value of their work under the theme ‘Better data, better lives’. Quite. Here is the logo for this year, downloadable from the UNStats site here.
The World Statistics Day was proclaimed by the United Nations General Assembly in 2010 – so, fairly recently – to recognise the importance of statistics in shaping our societies. National and regional statistical days already existed in more than 100 countries, but the General Assembly’s adoption of this international day as 20 October brought extra momentum. That first World Statistics Day in October 2010 was marked in more than 130 countries and areas.
According to UNStats, this year marks an important cornerstone for official statistics, with the conclusion of the Millennium Development Goals (see how countries have fared here), the post-2015 development agenda, the data revolution (see what the Data Revolution Group set up by UN Secretary-General Ban Ki-Moon has to say here), the preparations for the 2020 World Population and Housing Census Programme and the likes.
Statschat hasn’t heard a lot about what might be happening in New Zealand and elsewhere – it might yet be a bit too early for announcements – but if you are running an event or know of one, please let us know. In the meantime, one cute initiative of UNStats is to translate the English logo into many of the languages of the world. We couldn’t miss the opportunity to have UNStats do ours in the first language of this country, te reo Māori. Te tino kē hoki o te moko nā! (Nice logo!)
From XKCD
“How would you even study that?” is an excellent question to ask when you see a surprising statistic in the media. Often the answer is “they didn’t,” but sometimes you get to find out about some really clever research technique.
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