September 16, 2014

Currie Cup Predictions for Round 7

Team Ratings for Round 7

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.

Current Rating Rating at Season Start Difference
Western Province 6.10 3.43 2.70
Lions 2.37 0.07 2.30
Sharks 2.26 5.09 -2.80
Cheetahs -1.39 0.33 -1.70
Blue Bulls -2.18 -0.74 -1.40
Pumas -5.64 -10.00 4.40
Griquas -7.31 -7.49 0.20
Kings -13.52 -10.00 -3.50

 

Performance So Far

So far there have been 24 matches played, 18 of which were correctly predicted, a success rate of 75%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Kings vs. Blue Bulls Sep 12 13 – 28 -5.10 TRUE
2 Griquas vs. Pumas Sep 13 31 – 27 3.20 TRUE
3 Cheetahs vs. Sharks Sep 13 30 – 30 1.60 FALSE
4 Lions vs. Western Province Sep 13 35 – 33 1.10 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 Lions vs. Pumas Sep 19 Lions 13.00
2 Western Province vs. Griquas Sep 20 Western Province 18.40
3 Blue Bulls vs. Sharks Sep 20 Blue Bulls 0.60
4 Kings vs. Cheetahs Sep 20 Cheetahs -7.10

 

ITM Cup Predictions for Round 6

Team Ratings for Round 6

Here are the team ratings prior to Round 6, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

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 19.69 18.09 1.60
Tasman 12.10 5.78 6.30
Wellington 8.20 10.16 -2.00
Counties Manukau 2.19 2.40 -0.20
Hawke’s Bay 2.07 2.75 -0.70
Waikato 0.77 -1.20 2.00
Auckland 0.31 4.92 -4.60
Otago -1.62 -1.45 -0.20
Taranaki -3.68 -3.89 0.20
Bay of Plenty -4.12 -5.47 1.30
Southland -5.25 -5.85 0.60
Northland -9.09 -8.22 -0.90
Manawatu -9.45 -10.32 0.90
North Harbour -14.19 -9.77 -4.40

 

Performance So Far

So far there have been 38 matches played, 24 of which were correctly predicted, a success rate of 63.2%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Otago vs. Northland Sep 10 23 – 40 11.80 FALSE
2 Tasman vs. Taranaki Sep 11 39 – 31 19.80 TRUE
3 North Harbour vs. Manawatu Sep 12 24 – 13 -0.70 FALSE
4 Canterbury vs. Wellington Sep 12 46 – 12 15.50 TRUE
5 Bay of Plenty vs. Auckland Sep 13 12 – 27 -0.40 TRUE
6 Southland vs. Northland Sep 13 36 – 34 7.80 TRUE
7 Waikato vs. Counties Manukau Sep 14 26 – 21 2.60 TRUE
8 Hawke’s Bay vs. Otago Sep 14 41 – 0 7.00 TRUE

 

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. Tasman Sep 17 Tasman -13.30
2 Northland vs. Taranaki Sep 18 Taranaki -1.40
3 Counties Manukau vs. Canterbury Sep 19 Canterbury -13.50
4 Hawke’s Bay vs. Bay of Plenty Sep 20 Hawke’s Bay 10.20
5 Auckland vs. North Harbour Sep 20 Auckland 18.50
6 Manawatu vs. Southland Sep 20 Southland -0.20
7 Otago vs. Waikato Sep 21 Otago 1.60
8 Wellington vs. Tasman Sep 21 Wellington 0.10

 

September 15, 2014

Briefly

  • From the Brainflapping blog at the Guardian, a set of classifications for science stories (Axe Grinding, Soapbox, Wild Extrapolation). My favourite “Article has not been checked by anyone who knows how to communicate”

Stat of the Week Competition: September 13 – 19 2014

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 September 19 2014.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of September 13 – 19 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.

(more…)

September 10, 2014

Cannabis graduation exaggeration

3News

Teenagers who use cannabis daily are seven times more likely to attempt suicide and 60 percent less likely to complete high school than those who don’t, latest research shows.

Me (via Science Media Center)

“The associations in the paper are summarised by estimated odds ratios comparing non-users to those who used cannabis daily. This can easily be misleading to non-specialists in two ways. Firstly, nearly all the statistical evidence comes from the roughly 1000 participants who used cannabis less than daily, not the roughly 50 daily users — the estimates for daily users are an extrapolation.

“Secondly, odds ratios are hard to interpret.  For example, the odds ratio of 0.37 for high-school graduation could easily be misinterpreted as a 0.37 times lower rate of graduation in very heavy cannabis users. In fact, if the overall graduation rate matched the New Zealand rate of 75%, the rate in very heavy cannabis users would be 53%, and the rate in those who used cannabis more than monthly but less than weekly would be 65%.

That is, the estimated rate of completing high school is not 60% lower, it’s about 20% lower.  This is before you worry  about the extrapolation from moderate to heavy users and the causality question. The 60% figure is unambiguously wrong. It isn’t even what the paper claims.  It’s an easy mistake to make, though the researchers should have done more to prevent it, and that’s why it was part of my comments last week.

You can read all the Science Media Centre commentary here.

 

[Update: The erroneous '60% less likely to complete high school' statement is in the journal press release. That's unprofessional at best.]

(I could also be picky and point out 3News have the journal wrong: The Lancet Psychiatry, which started this year, is not the same as The Lancet, founded in 1823)

September 9, 2014

NRL Predictions for Finals Week 1

Team Ratings for Finals Week 1

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.

Current Rating Rating at Season Start Difference
Roosters 11.41 12.35 -0.90
Rabbitohs 10.49 5.82 4.70
Cowboys 8.66 6.01 2.60
Storm 7.40 7.64 -0.20
Broncos 4.18 -4.69 8.90
Sea Eagles 4.15 9.10 -4.90
Panthers 3.18 -2.48 5.70
Warriors 2.82 -0.72 3.50
Knights -0.28 5.23 -5.50
Dragons -2.10 -7.57 5.50
Bulldogs -2.95 2.46 -5.40
Raiders -7.64 -8.99 1.40
Eels -8.12 -18.45 10.30
Titans -8.40 1.45 -9.90
Sharks -10.92 2.32 -13.20
Wests Tigers -13.68 -11.26 -2.40

 

Performance So Far

So far there have been 192 matches played, 113 of which were correctly predicted, a success rate of 58.9%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Roosters vs. Rabbitohs Sep 04 22 – 18 5.80 TRUE
2 Storm vs. Broncos Sep 05 22 – 12 7.10 TRUE
3 Wests Tigers vs. Sharks Sep 06 26 – 10 -1.40 FALSE
4 Raiders vs. Eels Sep 06 33 – 20 3.10 TRUE
5 Cowboys vs. Sea Eagles Sep 06 30 – 16 7.80 TRUE
6 Knights vs. Dragons Sep 07 40 – 10 1.40 TRUE
7 Titans vs. Bulldogs Sep 07 19 – 18 -1.50 FALSE
8 Panthers vs. Warriors Sep 08 22 – 6 2.30 TRUE

 

Predictions for Finals Week 1

Here are the predictions for Finals Week 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.

Game Date Winner Prediction
1 Sea Eagles vs. Rabbitohs Sep 12 Rabbitohs -1.80
2 Roosters vs. Panthers Sep 13 Roosters 12.70
3 Cowboys vs. Broncos Sep 13 Cowboys 9.00
4 Storm vs. Bulldogs Sep 14 Storm 14.90

 

Currie Cup Predictions for Round 6

Team Ratings for Round 6

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.

Current Rating Rating at Season Start Difference
Western Province 6.17 3.43 2.70
Lions 2.29 0.07 2.20
Sharks 2.14 5.09 -2.90
Cheetahs -1.27 0.33 -1.60
Blue Bulls -2.81 -0.74 -2.10
Pumas -5.58 -10.00 4.40
Griquas -7.37 -7.49 0.10
Kings -12.89 -10.00 -2.90

 

Performance So Far

So far there have been 20 matches played, 15 of which were correctly predicted, a success rate of 75%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Western Province vs. Kings Sep 05 49 – 14 22.50 TRUE
2 Cheetahs vs. Pumas Sep 06 17 – 31 12.40 FALSE
3 Sharks vs. Griquas Sep 06 18 – 21 16.90 FALSE
4 Blue Bulls vs. Lions Sep 06 36 – 26 -1.50 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 Kings vs. Blue Bulls Sep 12 Blue Bulls -5.10
2 Griquas vs. Pumas Sep 13 Griquas 3.20
3 Cheetahs vs. Sharks Sep 13 Cheetahs 1.60
4 Lions vs. Western Province Sep 13 Lions 1.10

 

ITM Cup Predictions for Round 5

Team Ratings for Round 5

Here are the team ratings prior to Round 5, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

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 19.69 18.09 1.60
Tasman 12.10 5.78 6.30
Wellington 8.20 10.16 -2.00
Counties Manukau 2.19 2.40 -0.20
Hawke’s Bay 1.75 2.75 -1.00
Waikato 0.77 -1.20 2.00
Auckland 0.31 4.92 -4.60
Otago -1.29 -1.45 0.20
Taranaki -3.68 -3.89 0.20
Bay of Plenty -4.12 -5.47 1.30
Southland -5.25 -5.85 0.60
Northland -9.09 -8.22 -0.90
Manawatu -9.45 -10.32 0.90
North Harbour -14.19 -9.77 -4.40

 

Performance So Far

So far there have been 30 matches played, 18 of which were correctly predicted, a success rate of 60%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Manawatu vs. Bay of Plenty Sep 03 29 – 27 2.90 TRUE
2 Otago vs. Canterbury Sep 04 16 – 23 -17.00 TRUE
3 Tasman vs. Waikato Sep 05 23 – 16 15.30 TRUE
4 Northland vs. Hawke’s Bay Sep 05 23 – 21 -6.80 FALSE
5 Auckland vs. Wellington Sep 06 31 – 30 -3.90 FALSE
6 Taranaki vs. Southland Sep 06 41 – 19 5.60 TRUE
7 Manawatu vs. Counties Manukau Sep 07 26 – 10 -7.60 FALSE
8 Bay of Plenty vs. North Harbour Sep 07 21 – 14 5.50 TRUE

 

Predictions for Round 5

Here are the predictions for Round 5. 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 Otago vs. Northland Sep 10 Otago 11.80
2 Tasman vs. Taranaki Sep 11 Tasman 19.80
3 North Harbour vs. Manawatu Sep 12 Manawatu -0.70
4 Canterbury vs. Wellington Sep 12 Canterbury 15.50
5 Bay of Plenty vs. Auckland Sep 13 Auckland -0.40
6 Southland vs. Northland Sep 13 Southland 7.80
7 Waikato vs. Counties Manukau Sep 14 Waikato 2.60
8 Hawke’s Bay vs. Otago Sep 14 Hawke’s Bay 7.00

 

September 8, 2014

Poll meta-analyses in NZ

As we point out from time to time, single polls aren’t very accurate and you need sensible averaging.

There are at least three sets of averages for NZ:

1. Peter Green’s analyses, which get published at DimPost (larger parties, smaller parties). The full code is here.

2. Pundit’s poll of polls. They have a reasonably detailed description of their approach and it follows what Nate Silver did for the US elections.

3. Curiablog’s time and size weighted average. Methodology described here

The implementors of these cover a reasonable spectrum of NZ political affiliation. The results agree fairly closely except for one issue: Peter Green adds a correction to make the predictions go through the 2011 election results, which no-one else does.

According to Gavin White, there is a historical tendency for National to do a bit worse and NZ First to do a bit better in the election than in the polls, so you’d want to correct for this, but you could also argue that the effect was stronger than usual at the last election so this might overcorrect.

In addition to any actual changes in preferences over the next couple of weeks, there are three polling issues we don’t have a good handle on:

  • Internet Mana is new, and you could make a plausible case that their supporters might be harder for the  pollers to get a good grip on (note: age and ethnicity aren’t enough here, the pollers do take account of those).
  • There seems to have been a big increase in ‘undecided‘ responders to the polls, apparently from former Labour voters. To the extent that this is new, no-one really knows what they will do on the day.
  • Polling for electorates is harder, especially when strategic voting is important, as in Epsom.

 

[Update: thanks to Bevan Weir in comments, there's also a Radio NZ average. It's a simple unweighted average with no smoothing, which isn't ideal for estimation but has the virtue of simplicity]

Briefly

  • Interesting maps: a Moral Topography of Portland “The [1913] report found, specified per type of dwelling, only 22 of 80 apartments, merely 5 out of 59 hotels and no more than 71 out of 408 rooming and lodging houses to be ‘moral’.”  If you cynically expected that “immoral” didn’t refer to racial discrimination, rent-gouging, unhygienic conditions, or lack of fire escapes, you were right. (via consumerist.com)

 

  • An interactive display of US lifetime earnings for various groups by education and gender. The underlying data are good, but there’s inevitably an assumption that the correlations with education are broadly a result of education (including social status and networking effects) rather than selection for existing differences.