Stats Chat

Team Ratings for Round 24

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 172 matches played, 109 of which were correctly predicted, a success rate of 63.4%.
Here are the predictions for last week’s games.

Predictions for Round 24

Here are the predictions for Round 24. 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 3

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 14 matches played, 11 of which were correctly predicted, a success rate of 78.6%.
Here are the predictions for last week’s games.

Predictions for Week 3

Here are the predictions for Week 3. 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 4

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 12 matches played, 9 of which were correctly predicted, a success rate of 75%.
Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. 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 24

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 188 matches played, 130 of which were correctly predicted, a success rate of 69.1%.
Here are the predictions for last week’s games.

Predictions for Week 24

Here are the predictions for Week 24. 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.

Complicated interventions benefit from pilot studies, where you try to implement the intervention and see how feasible it is.  These are not designed as evaluations of how good the intervention is; they’re typically too small for that and they may have insufficient attention paid to representativeness.  You typically still would look at the outcome of the intervention, and you would have some idea of what you hoped to see.  As Dan Davies says if you don’t make predictions, you won’t know what to be surprised by (and if you don’t make recommendations, you won’t know what to be disappointed by)

In the new young-offenders bootcamp program, there has been a pilot with ten participants.  According to the news, 7 out of 10 have reoffended so far. Since one out of ten died, it would be generous to summarise the proportion with bad outcomes as 8 out of 10.

Speaking to RNZ, acting senior manager in charge Iain Chapman said at the time the pilot began, the 10 participants were the “most serious and persistent young offenders in the country”.

Going into the pilot and expecting no reoffending would have been naive, he said.

This is absolutely true.  What he didn’t say — and should have — was how much reoffending was reasonable to expect. Did he expect better results than two out of ten? Maybe he didn’t. Perhaps one out of ten is what he expected and getting two out of ten is an amazing success. That wasn’t the impression that the government and the media were giving when the program was announced, though. In particular, getting two out of ten not to reoffend doesn’t stack up well against the death.

If the ten pilot participants had been a representative sample of the sort of people who would go into the program, we could do some statistics.   However, we can’t really do this because the pilot program is so small and we don’t know how the participants were chosen. They presumably weren’t chosen specifically because they were unlikely to benefit, but we can’t say much more.

I would have expected that somewhere on a server in Wellington there is a business case for this program that has someone’s best guess at the likely success rate. It would be good to know if that person is surprised, or disappointed.

From economist Justin Wolfers on BlueSky, the record of payroll employment revisions by the US Bureau of Labor Statistics

First: look at 2020!

Next, though, the purple and green lines are quite close together compared to the scale of year-to-year change even when there isn’t a pandemic.

On the other hand, people do actually care about differences of the size we see between the initial and revised estimates, as is demonstrated by the stock market reaction to the revisions.  What this really shows is how difficult the estimation problem is.  People care deeply about changes that are almost invisibly small on this graph, and that are right at the limit of what’s feasible statistically.

The ideal solution is probably for people to be more relaxed about small changes in estimated payroll employment, just as the ideal solution for political opinion polling discourse is for people to have a more realistic view of the limits of estimation. Alternatively, if people want to be unrelaxed about small differences, they need to be willing to pay more to get better estimates.

As you may have heard, President Trump has dismissed the head of the US Bureau of Labor Statistics, claiming that payroll employment figures presented by the BLS were faked to make him look bad.

Politicians meddling with official statistics is a bad idea.  This isn’t because official statistics are Pure and Holy and True and above mere political concerns; it’s because official statistics are messy and difficult and hard to get right, but also very valuable.   The benefit-cost ratio of good official statistics is very high; for the NZ Census the ratio was estimated some years back as 10.  National and local governments, non-profits, and businesses use official statistics to make decisions and the stock market responds to the numbers.  On the other hand, the benefit-cost ratio of bad official statistics is very low — if no-one believes the numbers, there’s not a lot to be gained by publishing them.  Since estimation is messy and difficult and hard to get right, trust in official statistics agencies is critical for trust in official statistics.

Agencies don’t always do it perfectly.  It can definitely be necessary to have some sort of independent review at times.  I was on the External Data Quality Panel looking at the 2018 NZ Census, and there has just been an independent review of the UK Office of National Statistics.  The goal is to make sure the agencies have good procedures, evaluated carefully, to produce the best feasible answers.   Political interference, on the other hand, is discouraged by national and  international principles for official statistics.  It’s hard to get rid of once you have it, and very hard to prove you’ve gotten rid of it — like black mould.

The American Economic Association put out a statement on Friday saying that getting rid of the BLS head this way was  a bad idea.  They don’t do this sort of statement very often.  The International Statistical Institute put out a statement today — they do this more often, but it still takes a fairly significant event to get them moving.  The American Statistical Association haven’t said anything yet, but they were all travelling to their annual conference over the weekend, so it might come soon.

Team Ratings for Round 23

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 164 matches played, 104 of which were correctly predicted, a success rate of 63.4%.
Here are the predictions for last week’s games.

Predictions for Round 23

Here are the predictions for Round 23. 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 2

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 7 matches played, 5 of which were correctly predicted, a success rate of 71.4%.
Here are the predictions for last week’s games.

Predictions for Week 2

Here are the predictions for Week 2. 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 3

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 8 matches played, 7 of which were correctly predicted, a success rate of 87.5%.
Here are the predictions for last week’s games.

Predictions for Round 3

Here are the predictions for Round 3. 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.