Posts from October 2017 (33)

October 10, 2017

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 16.32 14.78 1.50
Wellington 11.48 -1.62 13.10
Taranaki 10.66 7.04 3.60
Tasman 4.74 9.54 -4.80
North Harbour 3.14 -1.27 4.40
Otago 2.22 -0.34 2.60
Counties Manukau -0.03 5.70 -5.70
Auckland -1.08 6.11 -7.20
Bay of Plenty -1.32 -3.98 2.70
Manawatu -3.19 -3.59 0.40
Northland -3.26 -12.37 9.10
Waikato -3.35 -0.26 -3.10
Hawke’s Bay -14.33 -5.85 -8.50
Southland -24.60 -16.50 -8.10

 

Performance So Far

So far there have been 62 matches played, 43 of which were correctly predicted, a success rate of 69.4%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Tasman vs. North Harbour Oct 04 21 – 14 4.80 TRUE
2 Manawatu vs. Counties Manukau Oct 05 24 – 29 2.10 FALSE
3 Canterbury vs. Taranaki Oct 06 43 – 55 14.40 FALSE
4 Otago vs. Bay of Plenty Oct 07 28 – 36 11.00 FALSE
5 Northland vs. Hawke’s Bay Oct 07 34 – 7 12.40 TRUE
6 Southland vs. Wellington Oct 07 12 – 61 -28.40 TRUE
7 Tasman vs. Auckland Oct 08 31 – 18 8.90 TRUE
8 Waikato vs. North Harbour Oct 08 11 – 13 -2.80 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 Taranaki vs. Manawatu Oct 11 Taranaki 17.90
2 Wellington vs. Northland Oct 12 Wellington 18.70
3 Auckland vs. Canterbury Oct 13 Canterbury -13.40
4 Bay of Plenty vs. Waikato Oct 14 Bay of Plenty 6.00
5 Otago vs. Southland Oct 14 Otago 30.80
6 Counties Manukau vs. Tasman Oct 14 Tasman -0.80
7 North Harbour vs. Taranaki Oct 15 Taranaki -3.50
8 Hawke’s Bay vs. Manawatu Oct 15 Manawatu -7.10

 

Currie Cup Predictions for Round 14

Team Ratings for Round 14

The basic method is described on my Department home page.

After trying to deal with the Cheetahs playing their first team in the Pro14 and a second or third team in the Currie Cup, I have come up with the appropriate solution, which is to have two separate Cheetahs teams. Cheetahs is the team when there is no Pro14 game, so the first choice players should be playing. Cheetahs2 is the team when there is a Pro14 game that week, so the reserve players will be playing in the Currie Cup.

I arbitrarily started the Cheetahs2 at a rating of -30, which is a bit of a rough guess based on results so far, and reran all my predictions so far to produce this weeks predictions. Note that the Cheetahs2 rating has not changed very much over the games so far, nor has the rating for the Cheetahs.

I would welcome comments on the assumptions underlying this approach.

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
Cheetahs 5.08 4.33 0.70
Sharks 4.45 2.15 2.30
Western Province 3.47 3.30 0.20
Lions 2.66 7.41 -4.70
Blue Bulls 0.09 2.32 -2.20
Pumas -8.17 -10.63 2.50
Griquas -10.19 -11.62 1.40
Cheetahs2 -30.14 -30.00 -0.10

 

Performance So Far

So far there have been 39 matches played, 26 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Cheetahs2 vs. Blue Bulls Oct 06 36 – 64 -25.30 TRUE
2 Pumas vs. Griquas Oct 07 35 – 38 7.30 FALSE
3 Lions vs. Western Province Oct 08 29 – 20 3.70 TRUE

 

Predictions for Round 14

Here are the predictions for Round 14. 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 Blue Bulls vs. Pumas Oct 13 Blue Bulls 12.80
2 Lions vs. Cheetahs Oct 14 Lions 2.50
3 Sharks vs. Western Province Oct 14 Sharks 5.90

 

October 9, 2017

Briefly

  • NY Times piece on personal genetic testing. (Disclaimer: I’m doing some consulting for a personal genomics company)
  • Where Americans get their science news and how much they trust various sources, from Pew Research.
  • “The Subjects Planned for the 2020 Census and American Community Survey report released today inadvertently listed sexual orientation and gender identity as a proposed topic in the appendix,” the U.S. Census Bureau said in a statement to NBC News. “This topic is not being proposed to Congress for the 2020 Census or American Community Survey”
  • ” It’s no longer good enough to shrug off (“briefly,” “for a small number of queries”) the problems in the system simply because it has computers in the decision loop.”
  • Road deaths are up since 2013.  Contrary to what the NZTA spokesperson says, it can’t be explained by increases in cars on the road: there has been a change in the trend for deaths per unit distance travelled.
  • Voting is now open for NZ Bird of the Year.  StatsChat doesn’t usually endorse bogus polls, but this one admits it’s just a publicity stunt.

Stat of the Week Competition: October 7 – 13 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 October 13 2017.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of October 7 – 13 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…)

Stat of the Week Competition Discussion: October 7 – 13 2017

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

October 5, 2017

Strength of evidence and type of evidence

The last 127 people to win a Nobel Prize for Physics have been men. Various people have calculated probabilities for this under a model where the true probability each time is 50:50.  Those probabilities are very small: they start with 37 zeros. Now, as people who analyse coincidences will tell you, there’s potential for cherry-picking. Gender of Nobel Laureates in Physics isn’t the only possible comparison.  On the other hand, if the comparison were picked from a thousand million billion trillion competing comparisons, the probability would still be tiny. The hypothesis that the committee chooses people at random for the Nobel Prize in Physics is not statistically defensible.  In fact, even the hypothesis that the committee chooses people with physics PhDs at random isn’t statistically defensible.

The reason there’s still controversy is that ‘choosing people at random’ isn’t anyone’s claim about how the Nobel Prize in Physics works.  Roughly speaking, there are three explanations for it just being given to women:

  1. Physics is too hard for women, who should just stick to biostatistics
  2. Women don’t get many opportunities to lead really ground-breaking physics research, because science is sexist
  3. The Nobel Committee for Physics (or the people eligible to nominate) are less likely to choose women who have contributed just as much

The p-value with a ridiculous number of zeros doesn’t provide any basis for assessing how important the three explanations are.  You need more data — and a different type of data.

So, for example, it’s relevant that Lise Meitner was nominated 29 times for the prize (and 19 times for the Chemistry prize) and didn’t win, but that Otto Hahn did win for their joint work. It’s relevant that Chieng-Shieng Wu was nominated 7 times and that a prize was awarded for the discovery she worked on. It’s relevant that Vera Rubin received lots of other prizes and awards and was routinely mentioned as a possible Nobelist — we don’t yet know how often she was nominated because there’s a 50-year secrecy rule.

Personally (though I’m not a physicist) I think that explanation 1 can be largely discounted and explanation 2 has to stretch a lot to cover the situation, so explanation 3 is looking plausible. But the numbers with 37 zeroes aren’t a relevant summary of the data.

October 4, 2017

Briefly

  • Data leakage: Bluetooth sex toys do not have a good sense of what’s a private activity (probably NSFW)
  • “Science in Society” award winners from the (US) National Association of Science Writers
  • The Nobel Prize for Physics went to gravitational wave astronomy. That’s a more statistical area than usual — extracting minute gravitational-wave signals from the background noise is a statistical challenge as well as an engineering nightmare. Renate Meyer, from the UoA Statistics department, and her co-workers, did some of the early work on this problem, and Matt Edwards (who we’re hoping to get back after a postdoc overseas) is a member of the LIGO Consortium.

Slip, slop, slap

From Stuff, the front-page link:

sun

As Betteridge’s Law of Headlines implies, the answer is “No.” Even the vendor doesn’t make a claim like that.

The story says (with the advertising redacted)

The key ingredient in the capsules is 100mg of … a blend of grapefruit and rosemary extracts. An independent lab trial of [the stuff] in Italy in 2015 found the onset of sunburn was delayed by 30 percent after two months of daily use.

It appears to be still-unpublished study. According to an advertising white paper,  it’s actually better than a lot of nutraceutical research: it was blinded and had 35 people in each group.  If we assume there aren’t any hidden problems, the study says that people who take this stuff daily for a couple of months end up needing about 30% more UV light to get a mild sunburn.

That is, the optimistic view is we’re looking at the equivalent of SPF 1.3 sunscreen.

October 3, 2017

Mitre 10 Cup Predictions for Round 8

Team Ratings for Round 8

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 18.69 14.78 3.90
Wellington 9.62 -1.62 11.20
Taranaki 8.28 7.04 1.20
Tasman 4.19 9.54 -5.40
Otago 3.93 -0.34 4.30
North Harbour 3.39 -1.27 4.70
Counties Manukau -0.67 5.70 -6.40
Auckland -0.73 6.11 -6.80
Manawatu -2.55 -3.59 1.00
Bay of Plenty -3.03 -3.98 1.00
Waikato -3.41 -0.26 -3.10
Northland -4.57 -12.37 7.80
Hawke’s Bay -13.02 -5.85 -7.20
Southland -22.74 -16.50 -6.20

 

Performance So Far

So far there have been 54 matches played, 38 of which were correctly predicted, a success rate of 70.4%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Northland vs. Otago Sep 27 32 – 30 -4.90 FALSE
2 Taranaki vs. Tasman Sep 28 40 – 26 6.80 TRUE
3 North Harbour vs. Hawke’s Bay Sep 29 33 – 30 24.20 TRUE
4 Southland vs. Manawatu Sep 30 20 – 25 -18.60 TRUE
5 Auckland vs. Bay of Plenty Sep 30 38 – 19 3.50 TRUE
6 Canterbury vs. Waikato Sep 30 37 – 17 27.40 TRUE
7 Wellington vs. Otago Oct 01 27 – 24 10.50 TRUE
8 Counties Manukau vs. Northland Oct 01 25 – 16 8.30 TRUE

 

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.

Game Date Winner Prediction
1 Tasman vs. North Harbour Oct 04 Tasman 4.80
2 Manawatu vs. Counties Manukau Oct 05 Manawatu 2.10
3 Canterbury vs. Taranaki Oct 06 Canterbury 14.40
4 Otago vs. Bay of Plenty Oct 07 Otago 11.00
5 Northland vs. Hawke’s Bay Oct 07 Northland 12.40
6 Southland vs. Wellington Oct 07 Wellington -28.40
7 Tasman vs. Auckland Oct 08 Tasman 8.90
8 Waikato vs. North Harbour Oct 08 North Harbour -2.80

 

Currie Cup Predictions for Round 13

Team Ratings for Round 13

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
Sharks 5.89 2.15 3.70
Western Province 3.98 3.30 0.70
Lions 2.44 7.41 -5.00
Cheetahs 1.59 4.33 -2.70
Blue Bulls 0.01 2.32 -2.30
Pumas -6.51 -10.63 4.10
Griquas -10.15 -11.62 1.50

 

Performance So Far

So far there have been 36 matches played, 24 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Sharks vs. Lions Sep 29 24 – 10 6.80 TRUE
2 Griquas vs. Cheetahs Sep 30 59 – 24 -9.50 FALSE
3 Blue Bulls vs. Western Province Oct 01 45 – 46 0.80 FALSE

 

Predictions for Round 13

Here are the predictions for Round 13. 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 Cheetahs have been playing in the Pro 14 competition and fielding a second or third team in the Currie Cup. They have another such game this weekend causing problems with prediction. After 4 such games a quick estimate is that they might score 27 points less than if they were fielding their best team, but losses so far will have dropped their rating a couple of points already. I would guess that a difference of 25 points might be appropriate, so instead of the points difference being 6.1 below, it might be -19 and a win to the Blue Bulls.

Game Date Winner Prediction
1 Cheetahs vs. Blue Bulls Oct 06 Cheetahs 6.10
2 Griquas vs. Pumas Oct 07 Griquas 0.90
3 Lions vs. Western Province Oct 08 Lions 3.00