Stats Chat

Team Ratings for Round 8

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

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 56 matches played, 29 of which were correctly predicted, a success rate of 51.8%.

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.

Team Ratings for Round 11

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

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 61 matches played, 39 of which were correctly predicted, a success rate of 63.9%.

Here are the predictions for last week’s games.

Predictions for Round 11

Here are the predictions for Round 11. 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.

IF UR TXTING & DRIVIN UR 23 TIMES MORE LIKELY 2 B INVOLVED IN A…. #CR@SH%*$!!! Drive phone free. http://t.co/etInpHqv9c

— New Zealand Police (@nzpolice) April 23, 2014

I couldn’t have put it less clearly myself, but if you follow the link, you do get to one of those tall, skinny totem-pole infographics, and the relevant chunk of it says

What it doesn’t do is tell you why they believe this. Neither does anything else on the web page, or, as far as I can tell, the whole set of pages on distracted driving.

A bit of Googling turns up this New York Times story from 2009

The new study, which entailed outfitting the cabs of long-haul trucks with video cameras over 18 months, found that when the drivers texted, their collision risk was 23 times greater than when not texting

That sounds fairly convincing, though the story also mentions that a study of college students using driving simulators found only an 8-fold increase, and notes that texting might well be more dangerous when driving a truck than a car.

The New York Times doesn’t link, but with the name of the principal researcher we can find the research report and Table 17, on page 44 does indeed include the number 23. There’s a pretty huge margin of error: the 95% confidence interval goes down to 9.7. More importantly,  though, the table header says “Likelihood of a Safety-Critical Event”. 

A “Safety-Critical Event” could be a crash, but it could also be a near-crash, or a situation where someone else needed to alter their behaviour to avoid a crash, or an unintentional lane change. Of the 4452 “safety-critical events”, 21 were crashes.  There were 31 safety-critical events observed during texting.

So, the figure of 23 is not actually for crashes, but it is at least for something relevant, measured carefully.  Texting, as would be pretty obvious, isn’t a good thing to do when you’re driving. And even if you’re totally rad,hip, and cool like the police tweetwallah, it’s ok to link.  Pretend you’re part of the Wikipedia generation or something.

There’s a good chance you’ve seen this graph and formed an opinion about it already

There certainly hasn’t been any shortage of comment about it, blaming the Florida Department of Law Enforcement for trying to reverse the apparent impact of the law change.

Andy Kirk, at Visualizing Data, has a couple of posts about the chart that you should read. It turns out that the designer of the chart wasn’t the Florida government, it was the C. Chan whose name appears at the bottom left. She designed the chart to try to show the big increase after 2005. She just likes having the y-axis increase downwards for bad things, inspired by this example

It’s interesting to look at why it’s obvious that the red area is the data in this graph, but not obvious in the first graph.  Part of it is the title reference to blood, and the fact that the bars can be seen as dripping. Another important clue is that the labels and additional graphs are on the white part of the graph, making it look like background; in the Florida graph the label is on the red section. Finally, the Iraq graph is not tied down at the bottom; the Florida graph has the x-axis at the bottom. The Florida graph is like those faces/goblet ambiguous pictures; for many people it doesn’t have strong enough visual cues for foreground and background to overcome the basic expectation that up is up.   A previous example of thoughtful design conflicting with prior expectations about the zero-line for the y-axis was ‘attack of the 14ft cat‘ from last year

I played around with these ideas, and came up with this revision, using a shadow and moving the x-axis to the top (I tried moving the label to the white section, but it didn’t seem to help)

I think it’s a bit easier to see that the red is the foreground here, but it still isn’t really compelling that the red is the data.

From the New York Times, a set of slope graphs comparing countries over a 30-year period at five points in the distribution.

The headline is a bit exaggerated — the US seems to be retaining its lead at the 80th percentile — but the graph is good.

From Jim Savage (on Twitter), income of top 1% or top 5% as multiple of income for bottom 90%

Looking into the top few percent is tricky because the definition of ‘income’ becomes flexible.  As Matt Nolan pointed out a few years ago, there was a big increase in the 1%’s share of US income in 1986, which is due at least in part to the Tax Reform Act making tax evasion less advantageous and so increasing reported taxable income.

From The Wireless, because that’s where I happened to notice it, not because they did anything wrong

But the average wedding costs about $30,000 – equivalent to a down payment on a house, another comparable goal for a couple in their twenties.

That is the stylised number: you can find it in Stuff and the Herald and lots of other places. But what does it mean? Could it really be true that a typical couple spends about half their annual income on a marriage?

A One News story last year said

Nicky Luis, owner of Lavish Events in Auckland, said while there were no official statistics on the average cost in New Zealand, perceptions within the industry put the figure at $30,000.

That is, people working in the lavish-weddings industry perceive there to be lots of lavish weddings and think it’s normal to spend a lot of money getting married.

Even when the number is supposedly based on surveys there are problems, as Will Oremus wrote last year at Slate

The first problem with the figure is what statisticians call selection bias. One of the most extensive surveys, and perhaps the most widely cited, is the “Real Weddings Study” conducted each year by TheKnot.com and WeddingChannel.com. (It’s the sole source for the Reuters and CNN Money stories, among others.) They survey some 20,000 brides per annum, an impressive figure. But all of them are drawn from the sites’ own online membership, surely a more gung-ho group than the brides who don’t sign up for wedding websites, let alone those who lack regular Internet access.

To make matters worse, the summary quoted from the surveys is the mean, but the way the figure is used, a median would be more appropriate. Oremus extracts the information that  the median is about 2/3 of the mean in those surveys, so we’re getting a 50% increase on top of the selection bias.

When you’re thinking about weddings you’ve been to, there is a different sort of bias. Expensive weddings tend to have more guests, so the average wedding you get invited to is larger than the average wedding you might have got invited to but didn’t.

On the recent paper claiming brain changes from low-level cannabis use, you’ve already seen the StatsChat post (I hope)

There’s also

• Lior Pachter’s demolition of the paper itself
• Maggie Koerth-Baker points out that there was no difference in actual psychological measurements between the users and controls — the brain scan differences didn’t show up in behaviour.
• Maia Szalavitz makes similar points
• Mark Kleiman writes about the politics, and also notes that the lead author seems to be innocent (here she is, sounding sensible). The over-the-top quotes come from the last author (‘senior’ author) on the paper.
• Jacob Sullum assigns a large chunk of the blame to university publicity departments
• You can read the actual paper here
• It’s not quite Radio Yerevan, but there’s an Armenian news site that picked up the story from the Telegraph.

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 April 25 2014.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 19 – 25 2014 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.

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The Herald story “Uni cheats: hundreds punished” is a pretty good example of using actual data to combat the ‘false balance’ problem in journalism.  The story notes the huge variation in official proceedings for cheating between NZ universities — nearly half the cases are at Waikato — and rightly highlights it as “suggesting differences in what institutions consider cheating, and how they target and record it.”

As the story doesn’t point out, with 540 cases out of more than 400000 tertiary students it’s pretty clear cheating is underreported everywhere. That’s hardly surprising, given the costs and benefits to staff for following it up. If it wasn’t for the irrational rage cheating arouses in academics, it would be perfectly safe.

From the Herald, in a good article about the Australian report on homeopathy

Auckland homeopath Suzanne Hansen said the treatments could not be measured in the same way medical treatments were.

“When you research it against a medical paradigm it will fail because you treat in a completely different way.”

This is probably true, but it’s a major concession that should be noted for the record.

The`medical paradigm’ of randomised controlled trials doesn’t need treatment to be the same for each person, it doesn’t need the benefits to be the same for each person, it doesn’t need the biological mechanism to be known or even plausible. All they need is that you can identify some group of people and a way of measuring success so that getting your treatment is better on average for your chosen group of people with your chosen way of defining ‘better’.  This isn’t just about homeopathy —  the whole field of personalised genomic medicine is based on individualising treatment, and this doesn’t introduce any difficulties for the medical paradigm.

If an intervention can’t beat fake pills that do nothing, on its choice of patient group and outcome measurement, it will fail when you `research it against a medical paradigm.’  If you’re fine with that, you should be fine with not using any advertising terms that suggest the intervention has non-placebo benefits.