Briefly
- CDC’s “disease detectives” halved as part of DOGE cuts at health agencies
- Bird flu seen in cow veterinarians
- Louisiana will no longer promote mass vaccination (paywalled original)
- Measles outbreak continues to expand in Texas
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 | |
---|---|---|---|
Leinster | 15.79 | 12.09 | 3.70 |
Bulls | 9.00 | 8.83 | 0.20 |
Glasgow | 8.52 | 9.39 | -0.90 |
Munster | 4.82 | 9.28 | -4.50 |
Stormers | 3.99 | 6.75 | -2.80 |
Lions | 3.41 | 6.73 | -3.30 |
Edinburgh | 1.95 | 0.09 | 1.90 |
Ulster | 1.49 | 2.52 | -1.00 |
Sharks | 1.40 | -2.94 | 4.30 |
Cheetahs | 0.80 | 0.80 | 0.00 |
Connacht | -0.91 | -0.76 | -0.10 |
Ospreys | -2.29 | -2.51 | 0.20 |
Scarlets | -3.54 | -10.65 | 7.10 |
Benetton | -4.69 | 1.02 | -5.70 |
Southern Kings | -6.52 | -6.52 | 0.00 |
Cardiff Rugby | -7.46 | -2.55 | -4.90 |
Zebre | -11.11 | -16.17 | 5.10 |
Dragons | -14.65 | -15.41 | 0.80 |
So far there have been 78 matches played, 58 of which were correctly predicted, a success rate of 74.4%.
Here are the predictions for last week’s games.
Game | Date | Score | Prediction | Correct | |
---|---|---|---|---|---|
1 | Stormers vs. Bulls | Feb 09 | 32 – 33 | -2.90 | TRUE |
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.
Game | Date | Winner | Prediction | |
---|---|---|---|---|
1 | Edinburgh vs. Zebre | Feb 15 | Edinburgh | 18.60 |
2 | Ospreys vs. Leinster | Feb 15 | Leinster | -12.60 |
3 | Lions vs. Stormers | Feb 16 | Lions | 1.90 |
4 | Bulls vs. Sharks | Feb 16 | Bulls | 10.10 |
5 | Benetton vs. Ulster | Feb 16 | Ulster | -0.70 |
6 | Munster vs. Scarlets | Feb 16 | Munster | 13.90 |
7 | Connacht vs. Cardiff Rugby | Feb 16 | Connacht | 12.00 |
8 | Dragons vs. Glasgow | Feb 17 | Glasgow | -17.70 |
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 | |
---|---|---|---|
Blues | 14.92 | 14.92 | 0.00 |
Chiefs | 11.43 | 11.43 | 0.00 |
Hurricanes | 10.97 | 10.97 | -0.00 |
Crusaders | 8.99 | 8.99 | 0.00 |
Brumbies | 6.19 | 6.19 | -0.00 |
Reds | 1.35 | 1.35 | 0.00 |
Highlanders | -2.50 | -2.50 | 0.00 |
Waratahs | -5.17 | -5.17 | 0.00 |
Western Force | -6.41 | -6.41 | 0.00 |
Fijian Drua | -7.98 | -7.98 | -0.00 |
Moana Pasifika | -11.25 | -11.25 | -0.00 |
Here are the predictions for 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 | Crusaders vs. Hurricanes | Feb 14 | Crusaders | 1.50 |
2 | Waratahs vs. Highlanders | Feb 14 | Waratahs | 1.30 |
3 | Fijian Drua vs. Brumbies | Feb 15 | Brumbies | -10.70 |
4 | Blues vs. Chiefs | Feb 15 | Blues | 7.00 |
5 | Western Force vs. Moana Pasifika | Feb 15 | Western Force | 8.80 |
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 | |
---|---|---|---|
Leinster | 15.79 | 12.09 | 3.70 |
Bulls | 9.21 | 8.83 | 0.40 |
Glasgow | 8.52 | 9.39 | -0.90 |
Munster | 4.82 | 9.28 | -4.50 |
Stormers | 3.78 | 6.75 | -3.00 |
Lions | 3.41 | 6.73 | -3.30 |
Edinburgh | 1.95 | 0.09 | 1.90 |
Ulster | 1.49 | 2.52 | -1.00 |
Sharks | 1.40 | -2.94 | 4.30 |
Cheetahs | 0.80 | 0.80 | 0.00 |
Connacht | -0.91 | -0.76 | -0.10 |
Ospreys | -2.29 | -2.51 | 0.20 |
Scarlets | -3.54 | -10.65 | 7.10 |
Benetton | -4.69 | 1.02 | -5.70 |
Southern Kings | -6.52 | -6.52 | 0.00 |
Cardiff Rugby | -7.46 | -2.55 | -4.90 |
Zebre | -11.11 | -16.17 | 5.10 |
Dragons | -14.65 | -15.41 | 0.80 |
So far there have been 77 matches played, 57 of which were correctly predicted, a success rate of 74%.
Here are the predictions for last week’s games.
Game | Date | Score | Prediction | Correct | |
---|---|---|---|---|---|
1 | Glasgow vs. Connacht | Jan 25 | 22 – 19 | 17.00 | TRUE |
2 | Ospreys vs. Benetton | Jan 25 | 43 – 0 | 2.80 | TRUE |
3 | Lions vs. Bulls | Jan 26 | 22 – 37 | -1.30 | TRUE |
4 | Scarlets vs. Edinburgh | Jan 26 | 30 – 24 | -1.70 | FALSE |
5 | Leinster vs. Stormers | Jan 26 | 36 – 12 | 15.70 | TRUE |
6 | Cardiff Rugby vs. Sharks | Jan 26 | 22 – 42 | -0.60 | TRUE |
7 | Dragons vs. Munster | Jan 26 | 19 – 38 | -12.50 | TRUE |
8 | Ulster vs. Zebre | Jan 27 | 14 – 15 | 21.20 | FALSE |
Here are the predictions for Delayed Games. 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 | Stormers vs. Bulls | Feb 09 | Bulls | -2.90 |
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 | |
---|---|---|---|
Stade Toulousain | 10.37 | 8.76 | 1.60 |
Toulon | 6.46 | 5.32 | 1.10 |
Bordeaux Begles | 5.97 | 3.96 | 2.00 |
Stade Rochelais | 3.05 | 4.85 | -1.80 |
Montpellier | 2.34 | -0.96 | 3.30 |
Clermont | 1.24 | 0.41 | 0.80 |
Racing 92 | 0.66 | 2.75 | -2.10 |
Bayonne | 0.08 | -1.69 | 1.80 |
Castres Olympique | -0.33 | -0.09 | -0.20 |
Lyon | -0.42 | -0.18 | -0.20 |
Section Paloise | -0.74 | 1.38 | -2.10 |
Stade Francais | -1.33 | 1.86 | -3.20 |
USA Perpignan | -3.00 | -0.66 | -2.30 |
Vannes | -8.62 | -10.00 | 1.40 |
So far there have been 105 matches played, 80 of which were correctly predicted, a success rate of 76.2%.
Here are the predictions for last week’s games.
Game | Date | Score | Prediction | Correct | |
---|---|---|---|---|---|
1 | Bordeaux Begles vs. Lyon | Jan 26 | 20 – 22 | 14.00 | FALSE |
2 | Racing 92 vs. Castres Olympique | Jan 26 | 20 – 27 | 9.90 | FALSE |
3 | Section Paloise vs. Clermont | Jan 26 | 20 – 14 | 6.50 | TRUE |
4 | Stade Toulousain vs. Montpellier | Jan 26 | 27 – 17 | 14.90 | TRUE |
5 | USA Perpignan vs. Bayonne | Jan 26 | 16 – 11 | 3.50 | TRUE |
6 | Vannes vs. Stade Francais | Jan 26 | 33 – 28 | -1.30 | FALSE |
7 | Toulon vs. Stade Rochelais | Jan 27 | 45 – 26 | 8.80 | TRUE |
Here are the predictions for Round 16. 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 | Bayonne vs. Bordeaux Begles | Feb 16 | Bayonne | 0.60 |
2 | Lyon vs. Stade Rochelais | Feb 16 | Lyon | 3.90 |
3 | Montpellier vs. Toulon | Feb 16 | Montpellier | 2.40 |
4 | Racing 92 vs. Vannes | Feb 16 | Racing 92 | 15.90 |
5 | Stade Francais vs. Section Paloise | Feb 16 | Stade Francais | 5.00 |
6 | USA Perpignan vs. Castres Olympique | Feb 16 | USA Perpignan | 5.00 |
7 | Clermont vs. Stade Toulousain | Feb 17 | Stade Toulousain | -2.80 |
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 | |
---|---|---|---|
Bath | 14.06 | 5.55 | 8.50 |
Northampton Saints | 5.92 | 7.50 | -1.60 |
Bristol | 5.33 | 9.58 | -4.30 |
Sale Sharks | 4.32 | 4.73 | -0.40 |
Leicester Tigers | 1.65 | 3.27 | -1.60 |
Saracens | 1.42 | 9.68 | -8.30 |
Gloucester | 0.77 | -9.04 | 9.80 |
Harlequins | -0.24 | -2.73 | 2.50 |
Exeter Chiefs | -2.16 | 1.23 | -3.40 |
Newcastle Falcons | -20.32 | -19.02 | -1.30 |
So far there have been 55 matches played, 36 of which were correctly predicted, a success rate of 65.5%.
Here are the predictions for last week’s games.
Game | Date | Score | Prediction | Correct | |
---|---|---|---|---|---|
1 | Harlequins vs. Northampton Saints | Jan 25 | 22 – 19 | -0.30 | FALSE |
2 | Exeter Chiefs vs. Saracens | Jan 26 | 31 – 22 | 1.40 | TRUE |
3 | Gloucester vs. Leicester Tigers | Jan 26 | 38 – 31 | 5.30 | TRUE |
4 | Bristol vs. Newcastle Falcons | Jan 27 | 55 – 35 | 35.20 | TRUE |
5 | Sale Sharks vs. Bath | Jan 27 | 23 – 32 | -1.80 | TRUE |
Here are the predictions for Round 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.
Game | Date | Winner | Prediction | |
---|---|---|---|---|
1 | Newcastle Falcons vs. Sale Sharks | Mar 22 | Sale Sharks | -18.10 |
2 | Bristol vs. Exeter Chiefs | Mar 23 | Bristol | 14.00 |
3 | Northampton Saints vs. Leicester Tigers | Mar 23 | Northampton Saints | 10.80 |
4 | Saracens vs. Harlequins | Mar 23 | Saracens | 8.20 |
5 | Bath vs. Gloucester | Mar 24 | Bath | 19.80 |
The Herald (from the Telegraph) says People with divorced parents are at greater risk of strokes, study finds
The study is here and the press release is here. It uses 2022 data from a massive annual telephone survey of health in the US, the Behavioral Risk Factor Surveillance System, “BRFSS” to its friends.
Using BRFSS means that the data are representative of the US population, which is useful. On the other hand, you’re limited to variables that can be assessed over the phone. That’s fine for age, and probably fine for parental divorce. It’s known to be a bit biased for BMI and weight. The telephone survey doesn’t even try to collect blood pressure, cholesterol, or oral contraceptive use, all known to be risk factors for stroke. And if you call people up on the phone and ask if they’ve ever had a stroke, you tend to miss the people whose strokes were fatal or incapacitating (about a quarter of people die immediately or within a year if they have a stroke).
Still, the researchers collected some useful variables to try to adjust away the differences between people with and without divorced parents. As usual, we have to worry about whether they went too far — for example, if the mechanism was via diabetes or depression, then adjusting for diabetes or depression would induce bias in the results.
This sort of research can be useful as a first step, to see if it’s worth doing an analysis using more helpful data from a study that followed people up over time — either a birth cohort study or a heart-disease cohort study. It’s interesting as initial news that there’s a relationship — though you also might think adverse effects of divorce would get smaller in recent decades as divorce became less noteworthy.
All this is background for my main point. While looking for links to published papers, I found that one of the same researchers had done the same sort of analysis with the BRFSS data from 2010 and published it in 2012. They found a stronger association twelve years ago than now. I don’t know about you, but I would have appreciated this fact being in the press release and in the news story.
Listening to the Slate Money podcast, I heard about an interesting survey result
Elizabeth Spiers: My number is 73 and that’s percent and that’s the number of men in a recent YouGov survey who say they do most of the chores in their household.
I found a Washington Post story
60 percent of women who live with a partner say they do all or most of the chores. But 73 percent of similarly situated men say that they do the most — or that they share chores equally.
Here’s the top few lines of the YouGov table
Breaking out advanced statistical software, 23%+19% is 42%, and 33%+30% is 63%. The figure for women matches the story, allowing for reasonable rounding. The figure for men doesn’t?
If we add in “shared equally”, which is given as 33% for men and 22% for women we can get to 75% “all” or “most” or “equally” for men, but 83% “all” or “most” or “equally” for women. And while the story is supposed to be that men say they are doing more and are delusional, the reported table has more work self-reported by women than men at all levels. It’s still possible, of course, that some the 42% of men claiming to do all or most chores are not fully aware of the situation, but the message that makes the results headline-worthy is not in the data.
The Washington Post story is still well worth reading — it uses the YouGov poll as a hook to discuss much more detailed data from the American Time Use Survey, which has the advantage that people write down contemporaneously what they are doing for an actual two weeks rather than trying to guess at an average.
Except notice the data points being used to come up with this story: the visible population of landless men in Rome and the Roman census returns. But, as we’ve discussed, the Roman census is self-reported, and the report of a bit of wealth like a small farm is what makes an individual liable for taxes and conscription.
In short the story we have above is an interpretation of the available data but not the only one and both our sources and Tiberius Gracchus simply lack the tools necessary to gather the information they’d need to sound out if their interpretation is correct.
Ipsos, the polling firm, has again been asking people questions to which they can’t reasonably be expected to know the answer, and finding they in fact don’t. For example, this graph shows what happens when you ask people what proportion of their country are immigrants, ie, born in another country. Everywhere except Singapore they overestimate the proportion, often by a lot. New Zealand comes off fairly well here, with only slight underestimation. South Africa and Canada do quite badly. Indonesia, notably, has almost no immigrants but thinks it has 20%.
Some of this is almost certainly prejudice, but to be fair the only way you could know these numbers reliably would be if someone did a reliable national count and told you. Just walking around Auckland you can’t tell accurately who is an immigrant in Auckland, and you certainly can’t walk around Auckland and tell how many immigrants there are in Ashburton. Specifically, while you might know from your own knowledge how many immigrants there were in your part of the country, it would be very unusual for you to know this for the country as a whole. You might expect, then, that the best-case response to surveys such as these would be an average proportion of immigrants over areas in the country, weighted by the population of those areas. If the proportion of immigrants is correlated with population density, that will be higher than the true nationwide proportion.
That is to say, if people in Auckland accurately estimate the proportion of immigrants in Auckland, and people in Wellington accurately estimate the proportion in Wellington, and people in Huntly accurately estimate the proportion in Huntly, and people in Central Otago accurately estimate the proportion in Central Otago, you don’t get an accurate nationwide estimate if areas with more people have a higher proportion of immigrants. Which, in New Zealand, they do. If we work with regions and Census data, the correlation between population and proportion born overseas is about 50%. That’s enough for about a 5 percentage point bias: we would expect to overestimate the proportion of immigrants by about 5 percentage points if everyone based their survey response on the true proportion in their part of the country.
Fortunately, if the proportion of immigrants in your neighbourhood or in the country as a whole matters to you, you don’t need to guess. Official statistics are useful! Someone has done an accurate national count, and while they probably didn’t tell you, they did put the number somewhere on the web for you to look up.
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