Stats Chat

Team Ratings for Finals Week 1

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 192 matches played, 119 of which were correctly predicted, a success rate of 62%.
Here are the predictions for last week’s games.

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.

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 22 matches played, 14 of which were correctly predicted, a success rate of 63.6%.
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 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.
Note that Cheetahs2 refers the Cheetahs team when there is a Pro14 match. The assumption is that the team playing in the Pro14 is the top team and the Currie Cup team is essentially a second team.

Performance So Far

So far there have been 8 matches played, 6 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.

Business confidence surveys are in the news recently.  I thought it might be useful to look at why anyone even does them.

Obviously, there’s no great social interest in making businesses happy and confident. Businesses are people only as a legal fiction. And even the business-related confidence of business owners and executives isn’t an obvious target — if it impacts something that matters, such as GDP or employment, we’ll see it there, and if it doesn’t, well then.

What makes countries around the world measure business confidence is that it’s thought to be a leading indicator.  That’s a slightly unfortunate phrase; we don’t mean it in the “very good indicator, we have the best indicators” sense. “Leading indicator” is less metaphorical than that: it means something that goes up and down earlier than the real economy does, and so helps with economic forecasting.

It’s plausible that business confidence could be useful this way.  Your boss knows if you’re going to be laid off before you do. A business owner has more idea than anyone else whether she’ll respond to a drop in sales by cutting production or expanding marketing.  If we could extract this information from a representative sample of businesses, it should help predict the economic future. Business confidence figures also have the advantage of timeliness: it takes a lot longer to get unemployment or GDP data collected and published.

On the other hand, business owners and managers have opinions as well as knowledge. Part of ‘business confidence’ will reflect the general political reckons of business owners and managers, both their overall feeling about the party in power and their views on bike lanes or the Americas Cup. That part of business opinion isn’t likely to help with forecasting.

Whether a business confidence survey will be economically useful is an empirical question, to be answered by data. It’s likely to help if the survey has questions designed to measure plans and intentions rather than feelings, and it’s likely to help if there’s a well-defined business population and a representative sample of it. The United Nations Statistics Division has written a book on how and why to conduct business confidence surveys and other ‘economic tendency surveys’, with chapters on questionnaire design, sampling frame, weighting, and other nerdy issues. If you feel like running one, you should maybe read it.

• There’s been a lot of coverage of a recent paper claiming ‘no safe level’ of drinking.  The best rebuttal is probably this one from David Spiegelhalter.
• Harvard Business Review published “Want Less Biased Decisions? Use Algorithms”. There’s a rebuttal at fast.ai pointing out that the initial argument misses a lot of the scaling-up harms of algorithms.
• Mark Hanna looks at Official Information Act reporting delays

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 184 matches played, 112 of which were correctly predicted, a success rate of 60.9%.
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 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 14 matches played, 9 of which were correctly predicted, a success rate of 64.3%.
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.

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.
Note that Cheetahs2 refers the Cheetahs team when there is a Pro14 match. The assumption is that the team playing in the Pro14 is the top team and the Currie Cup team is essentially a second team.

Performance So Far

So far there have been 5 matches played, 4 of which were correctly predicted, a success rate of 80%.
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.

When government policies change in different ways in different parts of the country, you can try to estimate the impact of the changes by comparing trends in affected and unaffected areas.  This week I’ve seen two examples, one in the NZ media and one from a Twitter request.

Chris Knox, in the Herald, has both an interactive graph and a story about the changes in petrol prices, using data from Gaspy. There was a clear jump of 11.5c/litre when the 11.5c/litre tax was increased.  What’s not so clear is whether petrol companies have subsequently tried to spread the price rise to other parts of the country.  Averaged over the whole rest of the country the gap has narrowed from the 11.5c start, but that’s driven by the South Island.  In the Waikato and Bay of Plenty regions, which were previously the most similar to Auckland, there’s no sign of price spreading.

So, right now, it’s unclear whether Auckland is bearing the whole cost (as it should) or just most of it.  The Herald story makes this fairly clear, quoting the economist Sam Warburton, who has been looking at the data — but also quoting someone who doesn’t need data to be sure that the costs are being borne by the regions. A similar analysis was done by Peter Ellis, using Sam Warburton’s data from pricewatch.co.nz, with basically similar conclusions.

The other example from this week is a research paper looking at how fatal workplace injuries changed in US states that introduced medical cannabis laws. They found a decrease starting at the time of legislation and slowly increasing to about a 1/3rd reduction after five years, compared to states without legal medical cannabis.  The analysis looks sensible and the data on fatal workplace injuries are of high quality, but there’s still a bit of potential concern about publication bias — the researchers might have published a negative result, but I wouldn’t have heard about it and told you.  I’ve written before about an analysis that suggested banning single-use plastic bags caused deaths from food poisoning —  and while the analysis looked reasonable it was clear based on additional data that the conclusion wasn’t plausible.

On the cannabis issue what’s most unclear is the contribution of various possible reasons to the reduction (if it is real):

• medical cannabis works to treat, say, pain or nausea, so people are healthier and better rested and don’t get injured
• medical cannabis substitutes for legal or illegal opioid use, and while it might increase injury risk compared to nothing, it’s better than opioids
• medical cannabis ends up in recreational use and substitutes for alcohol, and while it might increase injury risk compared to nothing, it’s better than alcohol
• medical cannabis ends up in recreational use and substitutes for alcohol, and people are less likely to use at work.
• it actually makes you safer

To the extent that the second reason is important in the US, where there’s an opioid use epidemic, it won’t generalise to New Zealand. The other reasons would probably generalise.

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 176 matches played, 106 of which were correctly predicted, a success rate of 60.2%.
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.