Posts from October 2016 (37)

October 18, 2016

Currie Cup Predictions for the Currie Cup Final

Team Ratings for the Currie Cup 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
Lions 9.42 9.69 -0.30
Cheetahs 7.26 -3.42 10.70
Blue Bulls 4.82 1.80 3.00
Western Province 2.97 6.46 -3.50
Sharks 2.67 -0.60 3.30
Pumas -12.52 -8.62 -3.90
Griquas -12.69 -12.45 -0.20
Cavaliers -13.07 -10.00 -3.10
Kings -20.29 -14.29 -6.00

 

Performance So Far

So far there have been 37 matches played, 27 of which were correctly predicted, a success rate of 73%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Blue Bulls vs. Western Province Oct 15 36 – 30 5.20 TRUE
2 Cheetahs vs. Lions Oct 15 55 – 17 -2.50 FALSE

 

Predictions for the Currie Cup Final

Here are the predictions for the Currie Cup 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 Cheetahs vs. Blue Bulls Oct 22 Cheetahs 5.90

 

October 17, 2016

Briefly

  • Beautiful weather maps from Ventusky, via Jenny Bryan
  • From BusinessInsider: 90% of executive board members think the ideal proportion of women on boards is higher than the current 20%, but the majority think it should still be 40% or less.
  • The Ministry for Social Development is collecting more data on people who use government-support community services. On one hand, they’re less likely to misuse it than a lot of internet companies; on the other hand, it might well deter people from seeking help. And while the Ministry is getting written consent, the people obtaining it won’t get paid by the Ministry if consent isn’t given.
  • If you only read one summary of the state of the US elections, the 538 update is a relatively painless and informative one.
  • People might be worrying too much about hackers (techy)

Moreover, we find that cyber incidents cost firms only a 0.4% of their annual revenues, much lower than retail shrinkage (1.3%), online fraud (0.9%), and overall rates of corruption, financial misstatements, and billing fraud (5%).

 

“Kind of” being an important qualifier here.

Vote takahē for Bird of the Year

It’s time again for the only bogus poll that StatsChat endorses: the New Zealand Bird of the Year.

Why is Bird of the Year ok?

  • No-one pretends the result means anything real about popularity
  • The point of the poll is just publicity for the issue of bird conservation
  • Even so, it’s more work to cheat than for most bogus polls

takahe

Why takahē?

  • Endangered
  • Beautiful (if dumb)
  • Very endangered
  • Unusual even by NZ bird standards: most of their relatives (the rail family) are shy little waterbirds.

sora

(A sora, a more-typical takahē relative, by/with ecologist Auriel ‘@RallidaeRule’ Fournier)

Stat of the Week Competition: October 15 – 21 2016

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 October 21 2016.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of October 15 – 21 2016 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…)

Stat of the Week Competition Discussion: October 15 – 21 2016

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!

October 13, 2016

Weighting surveys

From the New York Times: “How One 19-Year-Old Illinois Man Is Distorting National Polling Averages”

There is a 19-year-old black man in Illinois who has no idea of the role he is playing in this election.

He is sure he is going to vote for Donald J. Trump.

I think the story exaggerates the impact of this guy’s opinions on polling averages, but it’s a great illustration of one of the subtleties of polling.

Even in New Zealand, you often see people claiming, for example, that opinion polls will underestimate the Green Party vote because Green voters are younger and more urban, and so are less likely to have landline phones. As we see from the actual elections, that isn’t true. Pollers know about these simple forms of bias, and use weighting to fix them — if they poll half as many young voters as they should, each of their votes counts twice. Weighting isn’t as good as actually having a representative sample, but it’s ok — and unlike actually having a representative sample, it’s achievable.

One of the tricky parts of weighting is which groups to weight. If you make the groups too broadly-defined, you don’t remove enough bias; if you make them too narrowly-defined, you end up with a few people getting really extreme weights, making the sampling error much larger than it should be. That’s what happened here: the survey had one person in one of its groups, and that person turned out to be unusual. But it gets worse.

The impact of the weighting was amplified because this is a panel survey, polling the same people repeatedly. Panel surveys are useful because they allow much more accurate estimation of changes in opinions, but an unlucky sample will persist over many surveys.

Worse still, one of the weighting factors used was how people say they voted in 2012. That sounds sensible, but it breaks one of the key assumptions about weighting variables: you need to know the population totals.  We know the totals for how the population really voted in 2012, but reported vote isn’t the same thing at all — people are surprisingly unreliable at reporting how they voted in the past.

The actual impact on polling aggregators such as 538 is probably pretty small, since they model and try to remove ‘house effects’ (differences between surveys). However, the poll does give aid and comfort to people who don’t want to believe the consensus results, and that is not helpful.

October 11, 2016

Briefly

  • A curriculum to help kids think critically about health claims has been developed — and is being evaluated in a randomised trial in Uganda (from Vox)
  • Someone else (the website Grub Street) has fallen for the cheese addiction hoax. I wrote here about how the story makes no sense.  There’s a post by SciCurious that includes an interview with one of the people behind the actual research, talking about how the story just isn’t supported by her work. We still don’t seem to know who is pushing the hoax version.
  • I was on RadioNZ’s Our Changing World, talking to Allison Ballance about means and medians
  • Using mathematics (or statistics) to help with art repair:  Ingrid Daubechies talks about her work.
  • From MBIE, an interactive map of NZ tourist numbers

This has been an urban legend in the UK — it’s true in Melbourne, though mostly because the Mt Waverley reservoir is a small storage buffer rather than main storage

Mitre 10 Cup Predictions for Round 9

Team Ratings for Round 9

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 16.04 12.85 3.20
Taranaki 9.13 8.25 0.90
Tasman 7.10 8.71 -1.60
Auckland 6.19 11.34 -5.10
Counties Manukau 4.38 2.45 1.90
Wellington -0.03 4.32 -4.40
Otago -0.08 0.54 -0.60
Waikato -1.19 -4.31 3.10
Manawatu -3.24 -6.71 3.50
Bay of Plenty -4.95 -5.54 0.60
North Harbour -5.24 -8.15 2.90
Hawke’s Bay -5.25 1.85 -7.10
Northland -12.25 -19.37 7.10
Southland -14.12 -9.71 -4.40

 

Performance So Far

So far there have been 62 matches played, 47 of which were correctly predicted, a success rate of 75.8%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Manawatu vs. Wellington Oct 05 50 – 28 -5.90 FALSE
2 Auckland vs. Tasman Oct 06 31 – 49 7.70 FALSE
3 Canterbury vs. North Harbour Oct 07 47 – 18 24.50 TRUE
4 Southland vs. Northland Oct 08 39 – 31 0.80 TRUE
5 Otago vs. Counties Manukau Oct 08 14 – 16 -0.10 TRUE
6 Waikato vs. Hawke’s Bay Oct 08 46 – 22 4.60 TRUE
7 Wellington vs. Taranaki Oct 09 31 – 54 1.30 FALSE
8 Bay of Plenty vs. Manawatu Oct 09 38 – 33 4.20 TRUE

 

Predictions for Round 9

Here are the predictions for Round 9. 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. Tasman Oct 12 Tasman -8.30
2 Taranaki vs. Auckland Oct 13 Taranaki 6.90
3 Manawatu vs. Otago Oct 14 Manawatu 0.80
4 Counties Manukau vs. Canterbury Oct 15 Canterbury -7.70
5 Hawke’s Bay vs. Bay of Plenty Oct 15 Hawke’s Bay 3.70
6 Wellington vs. Waikato Oct 15 Wellington 5.20
7 Tasman vs. Southland Oct 16 Tasman 25.20
8 Northland vs. North Harbour Oct 16 North Harbour -3.00

 

October 10, 2016

Stat of the Week Competition: October 8 – 14 2016

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 October 14 2016.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of October 8 – 14 2016 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…)

Stat of the Week Competition Discussion: October 8 – 14 2016

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!