Posts filed under General (1020)

September 27, 2016

NRL Predictions for the Grand Final

Team Ratings for the Grand 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
Raiders 10.05 -0.55 10.60
Storm 9.36 4.41 4.90
Cowboys 7.77 10.29 -2.50
Panthers 6.14 -3.06 9.20
Sharks 5.65 -1.06 6.70
Broncos 5.20 9.81 -4.60
Roosters -0.08 11.20 -11.30
Eels -0.82 -4.62 3.80
Bulldogs -1.03 1.50 -2.50
Titans -1.31 -8.39 7.10
Rabbitohs -1.55 -1.20 -0.30
Sea Eagles -2.83 0.36 -3.20
Wests Tigers -4.05 -4.06 0.00
Warriors -6.26 -7.47 1.20
Dragons -7.44 -0.10 -7.30
Knights -17.13 -5.41 -11.70

 

Performance So Far

So far there have been 200 matches played, 129 of which were correctly predicted, a success rate of 64.5%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Sharks vs. Cowboys Sep 23 32 – 20 -0.90 FALSE
2 Storm vs. Raiders Sep 24 14 – 12 2.40 TRUE

 

Predictions for the Grand Final

Here are the predictions for the Grand 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.

Note that because that is how it appears in the official fixture list, I have listed the Storm as the first team here which is normally the home team. I have treated the venue as neutral. If the Sharks are considered to have home ground advantage, the expected margin drops to 0.70, still a win to the Storm, but close to even money.

Game Date Winner Prediction
1 Storm vs. Sharks Oct 02 Storm 3.70

 

Mitre 10 Cup Predictions for Round 7

Team Ratings for Round 7

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 16.20 12.85 3.30
Taranaki 7.24 8.25 -1.00
Auckland 5.81 11.34 -5.50
Tasman 4.73 8.71 -4.00
Counties Manukau 4.26 2.45 1.80
Otago 2.79 0.54 2.30
Wellington 2.70 4.32 -1.60
Waikato -0.75 -4.31 3.60
Manawatu -4.24 -6.71 2.50
Bay of Plenty -4.63 -5.54 0.90
Hawke’s Bay -4.71 1.85 -6.60
North Harbour -5.44 -8.15 2.70
Southland -13.04 -9.71 -3.30
Northland -14.42 -19.37 5.00

 

Performance So Far

So far there have been 46 matches played, 36 of which were correctly predicted, a success rate of 78.3%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Southland vs. Bay of Plenty Sep 21 20 – 16 -4.90 FALSE
2 Northland vs. Wellington Sep 22 21 – 29 -14.20 TRUE
3 Counties Manukau vs. Waikato Sep 23 35 – 26 9.00 TRUE
4 Canterbury vs. Otago Sep 24 45 – 34 18.80 TRUE
5 Taranaki vs. Manawatu Sep 24 30 – 19 16.50 TRUE
6 Hawke’s Bay vs. Tasman Sep 24 29 – 36 -5.10 TRUE
7 North Harbour vs. Southland Sep 25 35 – 14 10.30 TRUE
8 Bay of Plenty vs. Auckland Sep 25 38 – 44 -5.70 TRUE

 

Predictions for Round 7

Here are the predictions for Round 7. 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 Waikato vs. Canterbury Sep 28 Canterbury -13.00
2 Tasman vs. Counties Manukau Sep 29 Tasman 4.50
3 Wellington vs. Southland Sep 30 Wellington 19.70
4 North Harbour vs. Bay of Plenty Oct 01 North Harbour 3.20
5 Manawatu vs. Hawke’s Bay Oct 01 Manawatu 4.50
6 Auckland vs. Otago Oct 01 Auckland 7.00
7 Taranaki vs. Canterbury Oct 02 Canterbury -5.00
8 Northland vs. Waikato Oct 02 Waikato -9.70

 

Currie 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
Lions 11.37 9.69 1.70
Blue Bulls 4.74 1.80 2.90
Cheetahs 4.34 -3.42 7.80
Western Province 4.14 6.46 -2.30
Sharks 2.65 -0.60 3.30
Griquas -11.70 -12.45 0.80
Pumas -12.99 -8.62 -4.40
Cavaliers -14.16 -10.00 -4.20
Kings -19.82 -14.29 -5.50
TBC

 

Performance So Far

So far there have been 31 matches played, 22 of which were correctly predicted, a success rate of 71%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Cavaliers vs. Blue Bulls Sep 23 26 – 48 -13.80 TRUE
2 Griquas vs. Western Province Sep 23 31 – 52 -11.10 TRUE
3 Pumas vs. Cheetahs Sep 23 10 – 52 -10.70 TRUE
4 Kings vs. Lions Sep 24 7 – 71 -23.90 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 Lions vs. Sharks Sep 30 Lions 12.20
2 Western Province vs. Cavaliers Sep 30 Western Province 21.80
4 Pumas vs. Kings Sep 30 Pumas 10.30
3 Cheetahs vs. Griquas Oct 01 Cheetahs 19.50

 

September 25, 2016

Briefly

  • A post from Minding Data looking at the proportion of syndicated stories in the Herald.  I’m not sure about the definition — some stories are edited here, and it’s not clear what it takes to not have an attribution to another paper.
  • On measuring the right numbers, from Matt Levine at Bloomberg View “The infamous number is that 5,300 Wells Fargo employees were fired for setting up fake customer accounts to meet sales quotas, but it is important — and difficult — to try to put that number in context. For instance: How many employees were fired for not meeting sales quotas because they didn’t set up fake accounts? “
  • Data Visualisation: how maps have shown elevation, from National Geographic — including why maps of European mountains are lit from the northwest,  rather from somewhere the sun might be. (via Evelyn Lamb)
  • I was Unimpressed when the authors of an unconvincing paper on GMO dangers had a ‘close-hold embargo’ — allowing journalists an advance look only if they promised not to get any expert input to their stories. It’s not any better when the FDA does it.
September 20, 2016

NRL Predictions for the Preliminary Finals

Team Ratings for the Preliminary Finals

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
Raiders 10.02 -0.55 10.60
Storm 9.39 4.41 5.00
Cowboys 8.68 10.29 -1.60
Panthers 6.14 -3.06 9.20
Broncos 5.20 9.81 -4.60
Sharks 4.73 -1.06 5.80
Roosters -0.08 11.20 -11.30
Eels -0.82 -4.62 3.80
Bulldogs -1.03 1.50 -2.50
Titans -1.31 -8.39 7.10
Rabbitohs -1.55 -1.20 -0.30
Sea Eagles -2.83 0.36 -3.20
Wests Tigers -4.05 -4.06 0.00
Warriors -6.26 -7.47 1.20
Dragons -7.44 -0.10 -7.30
Knights -17.13 -5.41 -11.70

 

Performance So Far

So far there have been 198 matches played, 128 of which were correctly predicted, a success rate of 64.6%. Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Cowboys vs. Broncos Sep 16 26 – 20 6.60 TRUE
2 Raiders vs. Panthers Sep 17 22 – 12 6.30 TRUE

 

Predictions for the Preliminary Finals

Here are the predictions for the Preliminary Finals. 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 Sharks vs. Cowboys Sep 23 Cowboys -0.90
2 Storm vs. Raiders Sep 24 Storm 2.40

 

Mitre 10 Cup Predictions for Round 6

Team Ratings for Round 6

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 16.91 12.85 4.10
Taranaki 7.73 8.25 -0.50
Auckland 5.86 11.34 -5.50
Tasman 4.56 8.71 -4.10
Counties Manukau 4.26 2.45 1.80
Wellington 3.26 4.32 -1.10
Otago 2.08 0.54 1.50
Waikato -0.75 -4.31 3.60
Bay of Plenty -3.87 -5.54 1.70
Hawke’s Bay -4.54 1.85 -6.40
Manawatu -4.73 -6.71 2.00
North Harbour -6.48 -8.15 1.70
Southland -12.81 -9.71 -3.10
Northland -14.98 -19.37 4.40

 

Performance So Far

So far there have been 38 matches played, 29 of which were correctly predicted, a success rate of 76.3%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Counties Manukau vs. Taranaki Sep 14 29 – 30 -1.00 TRUE
2 Southland vs. Hawke’s Bay Sep 15 29 – 43 -2.10 TRUE
3 Tasman vs. Northland Sep 16 33 – 23 26.50 TRUE
4 Wellington vs. Bay of Plenty Sep 16 24 – 10 10.50 TRUE
5 Otago vs. North Harbour Sep 17 24 – 13 12.90 TRUE
6 Manawatu vs. Canterbury Sep 17 19 – 13 -22.80 FALSE
7 Auckland vs. Counties Manukau Sep 18 26 – 30 7.70 FALSE
8 Waikato vs. Taranaki Sep 18 20 – 20 -5.50 FALSE

 

Predictions for Round 6

Here are the predictions for Round 6. 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 Southland vs. Bay of Plenty Sep 21 Bay of Plenty -4.90
2 Northland vs. Wellington Sep 22 Wellington -14.20
3 Counties Manukau vs. Waikato Sep 23 Counties Manukau 9.00
4 Canterbury vs. Otago Sep 24 Canterbury 18.80
5 Taranaki vs. Manawatu Sep 24 Taranaki 16.50
6 Hawke’s Bay vs. Tasman Sep 24 Tasman -5.10
7 North Harbour vs. Southland Sep 25 North Harbour 10.30
8 Bay of Plenty vs. Auckland Sep 25 Auckland -5.70

 

Currie 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
Lions 9.45 9.69 -0.20
Blue Bulls 3.91 1.80 2.10
Western Province 3.51 6.46 -2.90
Cheetahs 2.77 -3.42 6.20
Sharks 2.65 -0.60 3.30
Griquas -11.08 -12.45 1.40
Pumas -11.42 -8.62 -2.80
Cavaliers -13.34 -10.00 -3.30
Kings -17.90 -14.29 -3.60

 

Performance So Far

So far there have been 27 matches played, 18 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. Kings Sep 15 53 – 0 20.80 TRUE
2 Blue Bulls vs. Griquas Sep 16 57 – 20 16.20 TRUE
3 Lions vs. Cheetahs Sep 17 29 – 37 12.40 FALSE
4 Western Province vs. Pumas Sep 17 31 – 23 19.90 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 Cavaliers vs. Blue Bulls Sep 23 Blue Bulls -13.80
2 Griquas vs. Western Province Sep 23 Western Province -11.10
3 Pumas vs. Cheetahs Sep 23 Cheetahs -10.70
4 Kings vs. Lions Sep 24 Lions -23.90

 

September 18, 2016

Yo mamma so smart

Q: Did you see intelligence is inherited just from mothers?

A: Yeah, nah.

Q: No, seriously. It’s in Stuff. “Recent scientific research suggests that rather than intelligence being genetically inherited from both their parents, it comes from their mother.”

A: I don’t think so.

Q: You’re objecting to their definition of intelligence, aren’t you?

A: Not this time. For today, I’m happy to stipulate to whatever their definition is.

Q: But they have Science! The “intelligence genes originate from the X chromosome” and “Some of these affected genes work only if they come from the mother. If that same gene is inherited from the father, it is deactivated.”

A: That sounds like two different explanations grafted together.

Q: Huh?

A: Some genes are imprinted so the paternal and maternal copies work differently, but that’s got nothing to do with the X chromosome.

Q: Why not?

A: Because any given cell has only one functioning X chromosome: for men, it comes from your mother, for women, it’s a random choice between the ones from each parent.

Q: Ok. But are all the intelligence genes on the X chromosome?

A: No. In fact, modern studies using hundreds of thousands of genetic variants suggest that genes contributing to intelligence are everywhere on the genome.

Q: But what about the ‘recent research’?

A: What recent research? I don’t see any links

Q: Maybe they’re in the blog post that the story mentions but doesn’t link to. Can you find it?

A: Yes.

Q: And the references?

A: Mostly in mice.

Q: But there’s one about a study in Glasgow, Scotland. In nearly 13,000 people.

A: There is, though it’s actually an analysis of the US National Longitudinal Study of Youth.  Which, strangely enough, did not recruit from Glasgow, Scotland. And less than half of the 12,686 participants ended up in the analysis.

Q: Whatever. It’s still recent research?

A: Ish. 2006.

Q: And it found mother’s intelligence was the most important predictor of child’s intelligence, though?

A: Yes, of the ones they looked at.

Q: So, more important than father’s intelligence?

A: That wasn’t one of the ones they looked at.

Q: “Wasn’t one of the ones they looked at”

A: Nope.

Q: Ok. So is there any reason for saying intelligence genes are on the X chromosome or is it all bollocks?

A: Both.

Q: ಠ_ಠ

A: Especially before modern genomics, it was much easier to find out about the effects of genes on the X chromosome, since breaking them will often cause fairly dramatic disorders in male children.

Q: So it’s not that more intelligence-related genes are on the X chromosome, just that we know more about them?

A: That could easily be the case. And just because a gene affects intelligence when it’s broken doesn’t necessarily mean small variations it in affect normal intelligence.

Q: But wouldn’t be it great if we could show those pretentious ‘genius’ sperm-donor organisations were all useless wankers?

A: On the other hand, we don’t need more reasons to blame mothers for their kids’ health and wellbeing.

September 17, 2016

Local polls

Since we have another episode of democracy coming on, there are starting to be more stories about polls for me to talk about.

First, the term “bogus”.  Two people, at least one of whom should have known better, have described poll results they don’t like as “bogus” recently. Andrew Little used the term about a One News/Colmar Brunton poll, and Nick Leggett said “If you want the definition of a bogus poll this is it” about results from Community Engagement Ltd.

As one of the primary NZ users of the term ‘bogus poll’ I want it to mean something. Bogus polls are polls that aren’t doing anything to get the right answer. For example, in the same Dominion Post story, Jo Coughlan mentioned

“…two independent Fairfax online Stuff polls of 16,000 and 3200 respondents showing me a clear winner on 35 per cent and 50 per cent respectively.”

Those are bogus polls.

So, what about the two Wellington polls cited as support for the candidates who sponsored them? Curia gives more detail than the Dominion Post.  The results differ by more than the internal margin of error, which will be partly because the target populations are different (‘likely voter’ vs ‘eligible’), and partly because the usual difficulties of sampling are made worse by trying to restrict to Wellington.

It wouldn’t be unreasonable to downweight the poll from Community Engagement Ltd just because seem to be a new company, but the polls agree the vote will go to preferences. That’s when things get tricky.

Local elections in NZ use Single Transferable Vote, so second and later preferences can matter a lot.  It’s hard to do good polling in STV elections even in places like Australia where there’s high turnout and almost everything really depends on the ‘two-party preferred’ vote — whether you rank Labor above or below the L/NP coalition.  It’s really hard when you have more than two plausible candidates, and a lot of ‘undecided’ voters, and a really low expected turnout.

With first-past-the-post voting the sort of posturing the candidates are doing would be important — you need to convince your potential supporters that they won’t be wasting their vote.  With STV, votes for minor candidates aren’t wasted and you should typically just vote your actual preferences, and if you don’t understand how this works (or if think you do and are wrong) you should go read Graeme Edgeler on how to vote STV.

September 15, 2016

Briefly

  • From Cardiogram: using the Apple Watch to diagnose abnormal heart rhythms
  • From MIT Technology Review, an analysis of emotional patterns in fiction. “We find a set of six core trajectories which form the building blocks of complex narratives” They don’t really cover the possibility that they find six just because that’s as many as they can align neatly with their current approach..
  • From Hilda Bastian: The quality of a research study is rarely uniformly good across all the things it studies (though it could be uniformly bogus)
  • On diagnosing depression from Instagram photos “But they’ve buried the real story. The depression rate among adults in the United States is 6.7%. The depression rate among the crowdsourced workers who shared their photos is 41.2%” (Medium)