April 14, 2026

Super Rugby Predictions for Week 10

Team Ratings for Week 10

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
Chiefs 12.26 12.36 -0.10
Hurricanes 11.82 8.29 3.50
Blues 9.20 8.91 0.30
Crusaders 8.18 8.41 -0.20
Brumbies 5.76 5.59 0.20
Reds 0.41 1.74 -1.30
Highlanders -2.93 -3.06 0.10
Western Force -4.60 -6.29 1.70
Waratahs -5.05 -5.84 0.80
Fijian Drua -8.74 -7.64 -1.10
Moana Pasifika -11.72 -7.88 -3.80

 

Performance So Far

So far there have been 43 matches played, 29 of which were correctly predicted, a success rate of 67.4%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Highlanders vs. Brumbies Apr 10 10 – 14 -5.40 TRUE
2 Moana Pasifika vs. Chiefs Apr 11 17 – 62 -27.60 TRUE
3 Fijian Drua vs. Western Force Apr 11 24 – 22 0.70 TRUE
4 Hurricanes vs. Blues Apr 11 42 – 19 6.30 TRUE
5 Reds vs. Crusaders Apr 11 31 – 26 -5.20 FALSE

 

Predictions for Week 10

Here are the predictions for Week 10. 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 Blues vs. Highlanders Apr 17 Blues 17.10
2 Waratahs vs. Moana Pasifika Apr 17 Waratahs 10.20
3 Chiefs vs. Hurricanes Apr 18 Chiefs 5.40
4 Brumbies vs. Fijian Drua Apr 18 Brumbies 19.50
5 Western Force vs. Crusaders Apr 18 Crusaders -9.30

 

NRL 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
Panthers 14.91 8.77 6.10
Roosters 6.47 9.50 -3.00
Sharks 5.60 7.25 -1.60
Broncos 4.88 7.06 -2.20
Storm 4.16 6.96 -2.80
Warriors 3.52 -1.18 4.70
Bulldogs 1.41 2.13 -0.70
Sea Eagles -0.31 0.21 -0.50
Cowboys -0.84 -2.69 1.80
Dolphins -1.33 1.85 -3.20
Wests Tigers -1.55 -7.26 5.70
Raiders -2.45 1.62 -4.10
Rabbitohs -3.05 -5.05 2.00
Titans -6.00 -8.02 2.00
Eels -6.17 -0.37 -5.80
Knights -8.88 -14.06 5.20
Dragons -10.37 -6.72 -3.70

 

Performance So Far

So far there have been 48 matches played, 25 of which were correctly predicted, a success rate of 52.1%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Bulldogs vs. Panthers Apr 09 32 – 16 -13.40 FALSE
2 Dragons vs. Sea Eagles Apr 10 18 – 28 -4.90 TRUE
3 Broncos vs. Cowboys Apr 10 31 – 35 12.00 FALSE
4 Rabbitohs vs. Raiders Apr 11 34 – 36 4.90 FALSE
5 Sharks vs. Roosters Apr 11 22 – 34 1.10 FALSE
6 Storm vs. Warriors Apr 11 14 – 38 8.90 FALSE
7 Eels vs. Titans Apr 12 10 – 52 10.20 FALSE
8 Wests Tigers vs. Knights Apr 12 42 – 22 9.70 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 Cowboys vs. Sea Eagles Apr 16 Cowboys 3.50
2 Raiders vs. Storm Apr 17 Storm -2.60
3 Dolphins vs. Panthers Apr 17 Panthers -12.20
4 Warriors vs. Titans Apr 18 Warriors 13.50
5 Rabbitohs vs. Dragons Apr 18 Rabbitohs 11.30
6 Wests Tigers vs. Broncos Apr 18 Broncos -2.40
7 Roosters vs. Knights Apr 19 Roosters 19.30
8 Eels vs. Bulldogs Apr 19 Bulldogs -3.60

 

AFL Predictions for Week 7

Team Ratings for Week 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
Western Bulldogs 25.67 26.06 -0.40
Hawthorn Hawks 23.78 22.83 0.90
Brisbane Lions 23.36 26.20 -2.80
Geelong Cats 19.98 26.75 -6.80
Sydney Swans 17.97 0.56 17.40
Adelaide Crows 14.83 15.32 -0.50
Gold Coast Suns 12.65 10.24 2.40
Fremantle Dockers 12.63 6.56 6.10
Collingwood 8.31 11.76 -3.40
GWS Giants 5.43 9.54 -4.10
Melbourne Demons -2.80 1.64 -4.40
St Kilda Saints -5.14 -7.63 2.50
Carlton Blues -11.34 -4.77 -6.60
Port Adelaide Power -15.81 -14.65 -1.20
North Melbourne -17.63 -21.71 4.10
Essendon Bombers -24.30 -27.89 3.60
Richmond Tigers -35.84 -29.44 -6.40
West Coast Eagles -39.77 -39.36 -0.40

 

Performance So Far

So far there have been 45 matches played, 31 of which were correctly predicted, a success rate of 68.9%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Adelaide Crows vs. Carlton Blues Apr 09 114 – 86 39.20 TRUE
2 Collingwood vs. Fremantle Dockers Apr 10 39 – 45 -3.90 TRUE
3 North Melbourne vs. Brisbane Lions Apr 11 66 – 92 -43.80 TRUE
4 Essendon Bombers vs. Melbourne Demons Apr 11 113 – 68 -32.80 FALSE
5 Sydney Swans vs. Gold Coast Suns Apr 11 100 – 68 0.60 TRUE
6 Hawthorn Hawks vs. Western Bulldogs Apr 11 104 – 64 -9.20 FALSE
7 Geelong Cats vs. West Coast Eagles Apr 12 122 – 76 62.30 TRUE
8 GWS Giants vs. Richmond Tigers Apr 12 131 – 75 38.50 TRUE
9 Port Adelaide Power vs. St Kilda Saints Apr 12 67 – 81 3.00 FALSE

 

Predictions for Week 7

Here are the predictions for Week 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 Carlton Blues vs. Collingwood Apr 16 Collingwood -19.70
2 Geelong Cats vs. Western Bulldogs Apr 17 Geelong Cats 5.30
3 Sydney Swans vs. GWS Giants Apr 17 Sydney Swans 23.50
4 Gold Coast Suns vs. Essendon Bombers Apr 18 Gold Coast Suns 48.00
5 Hawthorn Hawks vs. Port Adelaide Power Apr 18 Hawthorn Hawks 50.60
6 Adelaide Crows vs. St Kilda Saints Apr 18 Adelaide Crows 31.00
7 North Melbourne vs. Richmond Tigers Apr 19 North Melbourne 18.20
8 Melbourne Demons vs. Brisbane Lions Apr 19 Brisbane Lions -15.20
9 West Coast Eagles vs. Fremantle Dockers Apr 19 Fremantle Dockers -52.40

 

April 9, 2026

Kōkako goneburger?

The North Island kōkako is one of Aotearoa’s most elegantly beautiful birds, and while rare, they still exist in the wild as well as in sanctuaries. I’ve seen them on Tiritiri, near Auckland.  The South Island kōkako is a bit more controversial. It has been regarded as extinct and but is officially classified at the moment as “Don’t Know”.

This week, the Press published a story about the South Island kōkako, based on a publication in a regional ornithology journal, claiming there was a 48% chance that the species is still around.  The story raises two questions: what even does that mean, and is it reasonable?

We know that many of the reported sightings of South Island kōkako must be wrong: if they were not, the bird would be everywhere and there would be no uncertainty about its survival.  The question is whether any of the reported sightings are correct. Now, if there are no South Island kōkako then clearly all the sightings are mistaken, no matter how skilled and careful the reporters are– just like all the sightings of Bigfoot.  If South Island kōkako are rare, then most of the sightings are mistaken, but some of them are probably correct. Most of the sightings aren’t all that convincing anyway, but some of them do look pretty convincing.

There are various ways to approach this problem statistically. One is to try to pick out some sightings that you are sure are correct and see whether these stop at some point, or become less frequent. Another is to look at sightings during the period we know the bird existed and see what the ratio of convincing to dodgy sightings was like then, and see if it changed.  These (described more elegantly and formalised with maths) are the methods of three papers by Andrew Solow and co-workers (a,b,c — the copyright industry probably won’t let you read them).

One of the key bits of data in this calculation is a 2007 observation of the South Island kōkako that the Ornithological Society of NZ thought was convincing. According to the research paper, the last reliable sightings from the period when  the bird was uncontroversially still around were five over the period 1954-1967.  The 48% kōkako probability in the new report relies very heavily on the bird not being extinct in 2007. Without that one report, the estimated survival probability would be basically zero.  The isolated 2007 sighting, if true, would also provide evidence that real sightings are rare even when the species is still present.

There’s a problem with the formulation of the extinction models.  The original paper describing the first method, the one that gives the 48% probability, says “The methods described in this note assume that, prior to extinction, sightings follow a stationary Poisson process”.  In English: we assume that (true) sightings occur independently at a constant underlying rate.  They probably don’t.  There are a lot more people out there now than in 1967, so the rate is probably not constant. Also, there will be clustering: if someone convincingly reports seeing a South Island kōkako, the birding community will descend on the area with cameras at the ready and the chance of true sightings should go up[1]  And if the population is diminishing slowly (as it would have to be), the true sightings will also diminish slowly. This method also requires that you can tell which sightings are true.

The third method I linked above allows for uncertain sightings, so you don’t have to be able to tell in advance which sightings are true. However, to make the maths tractable, it still models both true and false sightings as being stationary Poisson processes: there’s a constant random rate of true sightings before extinction and a constant random rate of false sightings before and after extinction. Under this model, if the kōkako is extinct then at least 99.75% of the sightings since 1967 are false.

That’s less impressive than it sounds. To start with, obviously if there aren’t any kōkako now and there were no reliable sightings between 1967 and 2007, then nearly all sightings are false.  Also, this doesn’t mean that people’s accuracy in distinguishing kōkako from other things is less than 1 in 100. The iNaturalist site records 350,000 observations with photo or sound recording of securely-identified birds that aren’t South Island kōkako over just the time since 2012, and people may have seen birds and not posted about it to iNaturalist. The proportion of times someone sees something and wrongly think it’s a South Island kōkako could still be tiny — it’s just large compared to the (possibly zero) number of true sightings.

So, overall the paper says that if there were South Island kōkako in 2007 it’s not unreasonable that there still are a few. Which is fair. If they exist, they’re probably in one remote area rather than all over the South Island.   The 48% probability was correctly presented in the research paper as the output of the statistical method they used, but you shouldn’t put a lot of weight on the precise number. When you don’t have good data to put into the model you aren’t going to get much certainty out of it, and the statistical modelling had to make some pretty big approximations.  In particular, the model is leaning quite hard on the approximation that the search effort (and number of false sightings) has not increased over time.

 

 

[1] there’s a type of statistical model called a “self-exciting point process”, whose name is very appropriate here.

April 7, 2026

NRL 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
Panthers 16.86 8.77 8.10
Sharks 6.58 7.25 -0.70
Storm 6.31 6.96 -0.60
Broncos 6.05 7.06 -1.00
Roosters 5.49 9.50 -4.00
Warriors 1.37 -1.18 2.50
Bulldogs -0.53 2.13 -2.70
Sea Eagles -0.87 0.21 -1.10
Dolphins -1.33 1.85 -3.20
Cowboys -2.00 -2.69 0.70
Rabbitohs -2.29 -5.05 2.80
Wests Tigers -2.35 -7.26 4.90
Eels -3.00 -0.37 -2.60
Raiders -3.21 1.62 -4.80
Knights -8.08 -14.06 6.00
Titans -9.18 -8.02 -1.20
Dragons -9.82 -6.72 -3.10

 

Performance So Far

So far there have been 40 matches played, 23 of which were correctly predicted, a success rate of 57.5%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Dolphins vs. Sea Eagles Apr 02 18 – 52 8.90 FALSE
2 Rabbitohs vs. Bulldogs Apr 03 32 – 24 0.60 TRUE
3 Panthers vs. Storm Apr 03 50 – 10 10.70 TRUE
4 Dragons vs. Cowboys Apr 04 0 – 32 0.40 FALSE
5 Titans vs. Broncos Apr 04 12 – 26 -10.40 TRUE
6 Sharks vs. Warriors Apr 05 36 – 22 7.90 TRUE
7 Knights vs. Raiders Apr 05 32 – 12 -4.20 FALSE
8 Eels vs. Wests Tigers Apr 06 20 – 22 4.90 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 Bulldogs vs. Panthers Apr 09 Panthers -13.40
2 Dragons vs. Sea Eagles Apr 10 Sea Eagles -4.90
3 Broncos vs. Cowboys Apr 10 Broncos 12.00
4 Rabbitohs vs. Raiders Apr 11 Rabbitohs 4.90
5 Sharks vs. Roosters Apr 11 Sharks 1.10
6 Storm vs. Warriors Apr 11 Storm 8.90
7 Eels vs. Titans Apr 12 Eels 10.20
8 Wests Tigers vs. Knights Apr 12 Wests Tigers 9.70

 

AFL Predictions for Week 6

Team Ratings for Week 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
Western Bulldogs 29.31 26.06 3.30
Brisbane Lions 24.75 26.20 -1.40
Geelong Cats 21.26 26.75 -5.50
Hawthorn Hawks 20.14 22.83 -2.70
Adelaide Crows 15.84 15.32 0.50
Sydney Swans 15.59 0.56 15.00
Gold Coast Suns 15.03 10.24 4.80
Fremantle Dockers 12.44 6.56 5.90
Collingwood 8.50 11.76 -3.30
GWS Giants 4.07 9.54 -5.50
Melbourne Demons 2.83 1.64 1.20
St Kilda Saints -6.47 -7.63 1.20
Carlton Blues -12.34 -4.77 -7.60
Port Adelaide Power -14.48 -14.65 0.20
North Melbourne -19.01 -21.71 2.70
Essendon Bombers -29.93 -27.89 -2.00
Richmond Tigers -34.48 -29.44 -5.00
West Coast Eagles -41.05 -39.36 -1.70

Performance So Far

So far there have been 36 matches played, 25 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 Brisbane Lions vs. Collingwood Apr 02 119 – 65 22.50 TRUE
2 North Melbourne vs. Carlton Blues Apr 03 96 – 86 -9.70 FALSE
3 Adelaide Crows vs. Fremantle Dockers Apr 03 76 – 78 17.40 FALSE
4 Richmond Tigers vs. Port Adelaide Power Apr 04 48 – 90 -3.20 TRUE
5 West Coast Eagles vs. Sydney Swans Apr 04 35 – 163 -31.90 TRUE
6 Melbourne Demons vs. Gold Coast Suns Apr 05 109 – 89 -5.00 FALSE
7 Western Bulldogs vs. Essendon Bombers Apr 05 99 – 65 63.80 TRUE
8 Hawthorn Hawks vs. Geelong Cats Apr 06 92 – 91 11.80 TRUE

Predictions for Week 6

Here are the predictions for Week 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 Adelaide Crows vs. Carlton Blues Apr 09 Adelaide Crows 39.20
2 Collingwood vs. Fremantle Dockers Apr 10 Fremantle Dockers -3.90
3 North Melbourne vs. Brisbane Lions Apr 11 Brisbane Lions -43.80
4 Essendon Bombers vs. Melbourne Demons Apr 11 Melbourne Demons -32.80
5 Sydney Swans vs. Gold Coast Suns Apr 11 Sydney Swans 0.60
6 Hawthorn Hawks vs. Western Bulldogs Apr 11 Western Bulldogs -9.20
7 Geelong Cats vs. West Coast Eagles Apr 12 Geelong Cats 62.30
8 GWS Giants vs. Richmond Tigers Apr 12 GWS Giants 38.50
9 Port Adelaide Power vs. St Kilda Saints Apr 12 Port Adelaide Power 3.00

Super Rugby Predictions for Week 9

Team Ratings for Week 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
Chiefs 11.58 12.36 -0.80
Hurricanes 11.16 8.29 2.90
Blues 9.86 8.91 1.00
Crusaders 8.62 8.41 0.20
Brumbies 5.86 5.59 0.30
Reds -0.04 1.74 -1.80
Highlanders -3.03 -3.06 0.00
Western Force -4.50 -6.29 1.80
Waratahs -5.05 -5.84 0.80
Fijian Drua -8.84 -7.64 -1.20
Moana Pasifika -11.03 -7.88 -3.20

 

Performance So Far

So far there have been 38 matches played, 25 of which were correctly predicted, a success rate of 65.8%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Crusaders vs. Fijian Drua Apr 03 69 – 26 19.20 TRUE
2 Chiefs vs. Waratahs Apr 04 42 – 14 19.30 TRUE
3 Reds vs. Western Force Apr 04 19 – 42 11.90 FALSE

 

Predictions for Week 9

Here are the predictions for Week 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 Highlanders vs. Brumbies Apr 10 Brumbies -5.40
2 Moana Pasifika vs. Chiefs Apr 11 Chiefs -17.60
3 Fijian Drua vs. Western Force Apr 11 Fijian Drua 0.70
4 Hurricanes vs. Blues Apr 11 Hurricanes 6.30
5 Reds vs. Crusaders Apr 11 Crusaders -5.20

 

April 4, 2026

NZTA much better?

This is an expansion from the “Briefly” post about an NZTA summary of public comment on their SH1 Wellington proposals.

On Bluesky, @gwynebs had pointed out that some of the bars indicating levels of support didn’t appear to match the numbers attached to them — the “much better” category seemed inflated

A couple of days ago I noted there was a pattern to the distortion: it really was only the “much better” bar that was inflated and the other four were compressed in the same proportion. That is, some varying percentage was effectively being added to the “much better” level.  This is true for all five of the specific sections of the proposal,  but is not true for the two overall ratings in the middle of page 2, which appear correct. The bars are also correct in the much more detailed community engagement report; it’s just the summary that is wrong — which should indicate something about where things went wrong.

This is not rounding error. It’s much larger than that.

I went and measured the widths of all the bars in the five charts. These are in the same order as in the report: from top to bottom we have “2nd Terrace tunnel”, “Te Aro”, “Basin Reserve”, “2nd Mt Victoria tunnel”, and “Hataitai and Kilburnie”. The lower bar for each is cut from the NZTA summary. The upper bar has the correct percentages plus the necessary additional amount to make the bars line up — so the red is the amount that has been added to the “much better” category in the graph compared to the numbers. My bars and their bars don’t line up perfectly; that is probably rounding error. One possible explanation is that the red is some sort of “Don’t know” value that has inadvertently been put into the last bar — I could see that happening if the bars were drawn as pictures rather than as charts.

How much should we care about this? On the one hand, this sort of thing is probably corrosive to public trust in government data. On the other hand, this purports to be quantitative analysis of a self-selecting survey of the sort that attracts highly motivated and unrepresentative minorities*, so there’s a real limit to how seriously you should be taking the numbers.

Arguably, the point of this sort of survey is to see if there are surprising results — either something NZTA didn’t know about, or stronger opposition than they expected.  Even so, most people who aren’t the Advertising Standards Authority would think there’s something wrong with graphs that don’t match the data they purport to present.

 

*eg, people such as me

April 2, 2026

Briefly

  • For the day between March 31 and April 2nd, Andrew Gelman takes on an app that claims to find patterns in lotto numbers and make you money.
  • RNZ reports the plans for tolls on the Road of Northland Significance, a charge of $4.50 each way from Warkworth to Te Hana (you will see some quotes of $14.20, which includes current tolls on the already-existing road to Puhoi). They don’t report what fraction of the cost the tolls will cover. Greater Auckland looked at the NZTA consultation papers about the tolling and say 35 years of tolling will raise $391m. That would be nearly 10% of the (phase 1) cost if you didn’t include interest; it’s a much smaller fraction when you do. And this is phase 1 — there are two more phases in the planned road to Whangārei.  Whether the road is worth the cost isn’t my specialty, but it’s a lot of cost.
  • Len Cook (former Government Statistician) is in the Otago Daily Times disapproving of the planned removal of the census enumerations. We’ve covered this topic before.  The changes to the Data and Statistics Act are up for public comment, as are the necessary changes to the Electoral Act.   The electoral changes are not intrinsically controversial but are needed because electoral redistricting is currently triggered by the census. The electoral changes are important because they need a 75% supermajority in Parliament.
  • RNZ reports on an NZTA report on public consultation about road changes in Wellington. First, the usual whinge: please link to this sort of report, so we can read it if your summary gets us interested!  Second, and the StatsChat motivation, the NZTA report displays pretty graphics of the public feedback, which are systematically wrong! For example, on the question “will a second Terrace Tunnel make things worse or better for you?” the lower bar is from the report and the upper bar is correct based on the percentages.  The right end of the bar is “better”, and is exaggerated

    Or the next question, about Te Aro improvements (original above, correct version below). Again, the “better” end is exaggerated

    I don’t think this is likely to be deliberate, but it’s a bad look

Oily rag

The Ministry of Transport have put up a fuel monitoring dashboard. It shows estimates of demand, supply, and price.

At the moment, the reduction in demand is less than 10%, a level of demand that’s probably not sustainable in the medium when global supply is down at least 25%. On the other hand, we are still at level 1 of the alert system, and even level 2 doesn’t ask for any real reductions in demand.

What this display doesn’t show is any sort of “time to running out”.  That’s probably sensible, because it’s not even well-defined, let alone predictable. If you define “running out” as some petrol stations being out of supplies then it’s already happened. If you define it as “no fuel in the country”, it probably won’t happen. And if you define it as level 3 or level 4 restrictions on supply then it’s a choice by the government based on unknown criteria, and so is hard to forecast statistically.