Stats Chat

Team Ratings for Round 25

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.

Performance So Far

So far there have been 168 matches played, 121 of which were correctly predicted, a success rate of 72%.
Here are the predictions for last week’s games.

Predictions for Round 25

Here are the predictions for Round 25. 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.

Team Ratings for Week 12

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.

Performance So Far

So far there have been 60 matches played, 45 of which were correctly predicted, a success rate of 75%.
Here are the predictions for last week’s games.

Predictions for Week 12

Here are the predictions for Week 12. 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.

Team Ratings for the Semi-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.

Performance So Far

So far there have been 117 matches played, 80 of which were correctly predicted, a success rate of 68.4%.
Here are the predictions for last week’s games.

Predictions for the Semi-finals

Here are the predictions for the Semi-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.

Team Ratings for Round 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.

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.

Predictions for Round 10

Here are the predictions for Round 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.

From Ars Technica

An analysis of 2,007 damaged or defective hard disk drives (HDDs) has led a data recovery firm to conclude that “in general, old drives seem more durable and resilient than new drives.”

If you go out and lo0k at human deaths over a period of years, you will also find that Baby Boomers are much more likely die over age 70 than Gen X, and Gen X are more likely to die after 50 than millennials.  It’s too late for Boomers to die young.

Going on, we see that

backup and cloud storage company Backblaze uses hard drives that surpass the average life span Secure Data Recovery saw among the HDDs clients sent it last year. At the end of 2022, Backblaze’s 230,921 hard drives had an average age of 3.6 years versus the 2-year, 10-month average time before failure among the drives Secure Data Recovery worked on last year.

Again, if you look at an older group of people, the average age at death will be older than if you use a younger group.

This all wouldn’t matter so much, except that they are also trying to draw conclusions about novel disk drive technologies being less reliable.  There are statistical techniques to account for the different follow-up time of different groups of drives, but it’s quite possible those techniques would just tell you “nope, too soon to tell”.

Team Ratings for Round 10

I have had to update the predictions for this round since I just became aware the games are all being played at Suncorp.

I have to say that this arrangement is blatantly unfair to the non-Brisbane teams which lose a home game advantage but particularly the Sea Eagles, the Sharks and the Warriors on this occasion. The first two teams get to play their home game at the home stadium of their opponents so are doubly disadvantaged regarding home ground advantage. The Warriors have to play their home game across the Tasman and the home ground advantage they lose is greater for them according to my statistical analysis.

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.

Performance So Far

So far there have been 72 matches played, 41 of which were correctly predicted, a success rate of 56.9%.
Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 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.

Team Ratings for Week 11

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.

Performance So Far

So far there have been 54 matches played, 41 of which were correctly predicted, a success rate of 75.9%.
Here are the predictions for last week’s games.

Predictions for Week 11

Here are the predictions for Week 11. 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.

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.

Performance So Far

So far there have been 32 matches played, 21 of which were correctly predicted, a success rate of 65.6%.
Here are the predictions for last week’s games.

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.

There’s a 1News headline Only 1 in 4 Aucklanders back Wayne Brown’s sweeping cuts

I was expecting this to be based on proportions supporting the cuts in public submissions, but I was glad to see that the Council commissioned a proper survey in addition to accepting general comments and 1News reported it correctly.  That’s an excellent combination: open public comment allows people to point out problems or solutions you haven’t considered and a survey allows quantitative measurement for issues where you know the options in advance. More organisations should do this.

As you’d expect, trying to get quantitative results from the public comment doesn’t work very well. For example, 1News reported that 40% of submitters didn’t want any cuts, as opposed to the estimate of 7% for all Auckland in the survey.  People who have something to say are more likely to say it.

The number of submissions was very large: more than 10% of the number of votes in the mayoral election.  Even so, the submissions are very unrepresentative. That’s not a problem if you aren’t trying to get quantitative results; having the input biased towards experts and people who care a lot can be helpful.  It would be a problem if you were just counting the results.

Stuff has an important report on road injuries in Auckland, based on a review by Auckland Transport (that doesn’t seem to be available).   They looked at the numbers of serious injuries over three years, both as reported by the police crash analysis system and as reported by the hospitals where the people turned up for treatment. These numbers were not the same

Even the discrepancy for cars is a bit surprising: ‘serious injuries’ here mean hospital admission (an overnight stay).  It’s not clear whether the police are missing half the serious-injury crashes or under-reporting how many people end up in hospital, but I would have expected more completeness of reporting for hospitalisation of people in vehicles. (According to the law, even accidents involving bikes or e-scooters without cars  that result in injury have to be reported to the police, but I’m less surprised this doesn’t happen.)

Some of the bike and pedestrian and ‘transport device’ injuries that are being missed will be crashes that didn’t involve cars.  That’s the dashed/solid distinction in the graph. The data match international research on e-scooter crashes suggesting that most injuries are to the rider, and so may be relatively less likely to get reported to police.  For bikes, a potentially worrying category is injuries involving stationary cars — ‘doorings’ — which might be less likely to get reported than those involving moving cars.

Another important concern, though, is the definition of ‘serious injury’. An injury causing broken bones and resulting in weeks or months of significant disability, but not involving an overnight hospital stay, would not be meet the threshold.  This implies even the MoH statistics are also missing a lot of injuries that a normal person would call ‘serious’.