Stats Chat

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 87 matches played, 64 of which were correctly predicted, a success rate of 73.6%.
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 Round 18

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 119 matches played, 89 of which were correctly predicted, a success rate of 74.8%.
Here are the predictions for last week’s games.

Predictions for Round 18

Here are the predictions for Round 18. 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 10 matches played, 7 of which were correctly predicted, a success rate of 70%.
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 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.

Predictions for Round 1

Here are the predictions for Round 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.

• USA: Unfortunately, the Economy Runs On the Data Trump Is Trying to Delete (article, podcast discussion)
• New Zealand: Len Cook: Trust must be recovered after a second Census failure (The Post)

As you have probably heard, a modest-sized asteroid will pass really quite close to the earth in seven years. Perhaps very close. Maybe even closer than that. If this happens, it won’t be civilisation-destroying but could be locally devastating. The main StatsChat relevant issue is that the probability of the asteroid hitting the earth keeps going up: 1.4%, 2.2%, 3.1%.  Why is it getting consistently higher? Does that mean we can expect it will keep going up?

Most likely, the probability will keep going up for a while, then suddenly plunge to zero — though it might keep going up and up to 100%.  The sudden plunge to zero and the increase before that point happen for the same reason.    Because of our limited measurements so far, we know only approximately where the asteroid will be in seven years’ time.  You can think of a little moving cloud of possible asteroid positions in space. At the moment, the cloud of possible positions intersects where the Earth will be, but the Earth is a pretty small target (Space is big. Really big.)

As we get more precise information (for example, from the Webb Space Telescope) the cloud of possible positions gets smaller.  If the cloud still intersects the Earth’s position, the smaller cloud of possibilities means the probability of impact goes up.  If it doesn’t still intersect the Earth’s position, the probability of impact drops to zero.

If the randomness in predictions is approximately correct in some consistent way, the predicted average value of the estimate should stay the same over time (the technical term is ‘martingale’). The chance that the estimate has gone to zero will go up over time, so the estimate in scenarios where it doesn’t go to zero must also go up over time.

Update: it’s down to 0.36%
Update: and now down to 0.0022%. Asteroid goneburger.

Team Ratings for Delayed Games

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 86 matches played, 63 of which were correctly predicted, a success rate of 73.3%.
Here are the predictions for last week’s games.

Predictions for Delayed Games

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.

Team Ratings for Round 17

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 112 matches played, 84 of which were correctly predicted, a success rate of 75%.
Here are the predictions for last week’s games.

Predictions for Round 17

Here are the predictions for Round 17. 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 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 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.

When you get access to some data, a first step is to see if you understand it: do the variables measure what you expect, are there surprising values, and so on.  Often, you will be surprised by some of the results. Almost always this is because the data mean something a bit different from what you expected. Sometimes there are errors. Occasionally there is outright fraud.

Elon Musk and DOG-E have been looking at US Social Security data. They created a table of Social Security-eligible people not recorded as dead and noticed that (a) some of them were surprisingly old, and (b) the total added up to more than the US population.

That’s a good first step, as I said. The next step is to think about possible explanations (as Dan Davies says: “if you don’t make predictions, you won’t know when to be surprised”). The first two I thought of were people leaving the US after working long enough to be eligible for Social Security (like, for example, me) and missing death records for old people (the vital statistics records weren’t as good in the 19th century as they are now).

After that, the proper procedure is to ask someone or look for some documentation, rather than just to go with your first guess.  It’s quite likely that someone else has already observed the existence of records with unreasonable ages and looked for an explanation.

In this case, one would find (eg, by following economist Justin Wolfers) a 2023 report “Numberholders Age 100 or Older Who Did Not Have Death Information on the Numident” (PDF), a report by the Office of the Inspector General, which said that the very elderly ‘vampires collecting Social Security’ were neither vampires nor collecting Social Security, but were real people whose deaths hadn’t been recorded.   This was presumably a follow-up to a 2015 story where identity fraud was involved — but again, the government wasn’t losing money, because it wasn’t paying money out to dead people.

The excess population at younger years isn’t explained by this report, but again, the next step is to see what is already known by the people who spend their whole careers working with the data, rather than to decide  the explanation is the first thing that comes to mind.