Posts filed under General (3156)

Team Ratings for Round 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 Round 1

Here are the predictions for Round 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.

Team Ratings for Round 4

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 21 matches played, 13 of which were correctly predicted, a success rate of 61.9%.

Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. 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.

Q:  What do you think about this new research on tea preventing diabetes?

A: That’s not what it says

Q: Sure it is. Big black letters, right at the top: “Three cups of tea a day can cut your risk of diabetes… even if you add milk”

A: I mean that’s not what the research says

Q: The bit about milk?

A: Well, they didn’t study milk at all, but that’s not the main problem

Q: They didn’t study cups?

A: No. Or diabetes. Or, in one of the studies, tea.

Q: Hmm. Ok, so this “glucose-lowering effect” they write about, is that a lab study?

A: Yes.

Q: Mice?

A:  One of the studies used rats, the other didn’t

Q: Cells, then?

A: No, just enzymes in a test tube, and a highly processed chemical extract of tea.

Q: Ok, forget about that one. But the rat study, that measured actual glucose lowering and actual tea?

A:  Almost. They gave the rats a high-sugar drink, and if they were given the tea first, their blood glucose didn’t go up as much.

Q: Which of the two studies was this one?

A: The one where the story just says the results were similar and doesn’t give the researchers’ names, only their institution.

Q: Wouldn’t you think the story would say more about this one, since it actually involves blood glucose and, like, living things?

A: In a perfect world, yes.

Q: The story says they don’t think milk would make a difference. What about sugar?

A: No mention of it.

Q: That’s strange. Quite a lot of British people have sugar in their tea. Wouldn’t it be helpful to say something?

A: You’d think.

Q: How much tea did the rats get?

A: The lowest effective dose they report is 62.5 mg/kg of freeze-dried tea powder

Q: What’s that in cups?

A: The research paper says “corresponds to nine cups of black tea”.

Q: Per day?

A: No, all at once.

Q: So we need to get bigger cups?

A: Or fewer reprinted British ‘health’ stories.

It’s nearly CensusAtSchool time again!  CAS is a biennial educational project in te reo Māori and English that turns school students into data detectives, using real-world, anonymised data about them, their peers, and their world.

This is how it works: In the classroom, using any sort of internet-enabled digital device, and under the supervision of teachers, students fill in a confidential questionnaire in English or te reo Māori.

Some questions involve practical skills, such as weighing their schoolbags and measuring their arm span. Some questions ask about their day-to-day lives: How do they get to school? Where did they eat their dinners the night before? Do they think bullying is a problem in their school? And, given that this is a major sporting year: Which two teams will contest the Rugby World Cup final?

The database is then made available for students and their teachers to undertake statistical investigations, which is an important part of the statistics strand of the curriculum.

Teachers, this year’s Census starts on March 16, and can be completed any time this year. It’s free and you can register at www.censusatschool.org.nz. For everyone else, CAS always attracts great mainstream media interest – we’ll post the best stories here as they crop up.

CensusAtSchool is an international educational project that began in the UK in 2000, based on a 1990 trial project by Dr Sharleen Forbes, then of Statistics New Zealand. It is now run in the UK, Ireland, Australia, Canada, South Africa, Japan and the US, as well as  New Zealand.

• NZ papers have sensible coverage of the new peanuts/kids research (Herald, Stuff). NHS Behind The Headlines has a summary and takes some UK papers to task.
• “Rich Data, Poor Data”: Nate Silver writes about why sports statistics works. Unfair summary: it’s an artificial problem in a controlled environment that people care about more than they should.
• “the vast majority of health sites, from the for-profit WebMD.com to the government-run CDC.gov, are loaded with tracking elements that are sending records of your health inquiries to the likes of web giants like Google, Facebook, and Pinterest, and data brokers like Experian and Acxiom.” Story at vice.com, video summary from the researcher:
  
• “A memo to the American people from US Chief Data Scientist Dr DJ Patil”.  More informative than you might expect given the source.
• “the biggest problem facing the world of public opinion research isn’t that online opt-in polls, but rather the temptation to troll twitter to “see what people are thinking” and other thoughts from Cathy O’Neil, based on the new report on Big Data from the American Association for Public Opinion Research.
• Openweathermap.org: another part of the increasing supply of open data
  

We’ve written before about Wiki New Zealand, which aims to ‘democractise data’. WNZ has revamped its website to make things clearer and cleaner, and you can browse here.

As I’m a postgraduate scarfie this year, the table on domestic students in tertiary education interested me – it shows that women (grey) are enrolled in greater numbers than men at every single level. Click the graph to embiggen.

Founder Lillian Grace talks about the genesis of Wiki New Zealand here, and for those who love the techy  side, here’s a video about the backend.

Team Ratings for Round 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 7 matches played, 2 of which were correctly predicted, a success rate of 28.6%.

Here are the predictions for last week’s games.

Predictions for Round 2

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.

• The mathematics of the US Powerball lottery: even under the unreasonable assumption that you care about the million+first dollar as much as the first, it’s not worth it.
• How to choose data visualisations (from Scientific American blogs, via Alberto Cairo)
• The Dark Side of Open Data (via Harkanwal Singh)
• Animations of prime factorisations. Also via Harkanwal Singh. What makes these interesting is the lack of non-trivial patterns: to an striking extent, primes behave randomly (technical stuff by Terry Tao)
• A survey of mathematical podcasts, from mathbabe.org

Vox.com has a post on calendar patterns.  Yuri Victor noticed that, this year, February is a nice rectangular shape on a calendar (as happens whenever it starts on Sunday in  a non-leap year), and wondered how often this happened.  This is the sort of question where you can easily find out the answer, so he did:

I decided to see if this occurs often so I wrote some code and found out it happens more than I thought.In the past 100 years, there have been 11 Februaries that make a rectangle.

He also noticed that February 13th would be Friday when this happened and wondered how often we got a Friday 13th:

Friday the 13ths also happen more than I thought. In the past 100 years there have been 171 Friday the 13ths, which means there is one to two a year.

This is a Good Thing. We want journalists wondering about patterns and looking up data to check them. We don’t want them being required to call an expert in calendars to give a quote. It’s also a Good Thing that he tells us his expectations were wrong

It would be even better, though, if he’d tried to work out a quantitative guess and tell us. The simplest guess would be that, in the long run, February 1 is a Sunday as often as any other day, and that the 13th of a month is a Friday as often as any other day.  These are natural guesses because there’s no special reason the year or a particular month should start on a particular day of the week. 

In  100 years there are 1200 months, and 1200/7 is 171.4, so it looks as though Friday 13th happens in almost exactly 1/7 of months.  In the past 100 years there are 75 Februaries with 28 days, and 75/7 is 10.7, so 28-day Februaries begin on Sunday almost exactly 1/7 of the time.

You wouldn’t always expect the simplest possible explanation to hold. For example, the date of Passover is set based on the solar and lunar calendars, in a 19-year cycle. Since 7 doesn’t divide 19, you’d expect either that the days of the week didn’t divide up equally or that they took a long time (requiring lots of leap years) to do so.

Team Ratings for Round 1

Sorry about the delay in posting these predictions. I have never been really happy with my selection of parameters, so decided to do a massive grid search yesterday which ended up taking about 16 hours. That means this year I have shiny new parameter values for my predictions. As ever, remember the adage, “past performance is not necessarily indicative of future behaviour”.

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 Round 1

Here are the predictions for Round 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.