Posts from November 2023 (15)

Team Ratings for Week 7

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 48 matches played, 31 of which were correctly predicted, a success rate of 64.6%.
Here are the predictions for last week’s games.

Predictions for Week 7

Here are the predictions for Week 7. 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 56 matches played, 42 of which were correctly predicted, a success rate of 75%.
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.

Team Ratings for Round 8

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 35 matches played, 24 of which were correctly predicted, a success rate of 68.6%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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.

Many news stories and some StatsChat posts have talked about detecting the output of Large Language Models. At the moment, tools to do this are very inaccurate.  Denouncing, for example, a student paper, based on these detectors wouldn’t be supportable. Even worse, the error rate is higher for people who aren’t native English speakers, a group who can already be accused unfairly.

We might hope for better detectors in the future.  If people using ChatGPT have access to the detector, though, there’s a pretty reliable way of getting around it. Take a ChatGPT-produced document, and make small changes to it until it doesn’t trigger the detector.  Here we’re assuming that you can make small changes and still get a good-quality document, but if that’s not true — if there’s only one good answer to the question — there’s no hope for a ChatGPT detector to work.  Additionally, we’re assuming that you can tell which random changes still produce a good answer.  If you can’t, then you might still be able to ask GPT whether the answer is good.

A related question is whether Large Language Model outputs can be ‘watermarked’ invisibly so as to be easier to detect. ChatGPT might encode a signature in the first letters of each sentence, or it might have subtle patterns in word frequencies or sentence lengths. Regrettably, any such watermark falls to the same attack: just make random changes until the detector doesn’t detect.

On the preprint server arXiv recently was a computer science article arguing that even non-public detectors can be attacked in a similar way. Simply take the Large Language Model output and try random changes to it, keeping the changes that don’t mess up the quality.  This produces a random sample from a cloud of similar answers. If there aren’t any similar answers accessible by small changes, it’s going to be hard for the AI to insert a watermark, so we can assume there will be.  ChatGPT didn’t actually produce these similar answers, so a reasonable fraction of them should not trigger the ChatGPT detector.  Skeptics might be reassured that the researchers tried this approach on some real watermarking schemes and it seems to work.

From Ars Technica (and while it’s not a story about baseball, it is is trying to use numbers to mean something)

It’s actually 162 regular season games a year for 30 teams which means, 2,430 games a year. That’s 32,805 hours of baseball based on the average length of a game lasting 162 minutes. The regular season is 185 days long, which equals 4,440 hours. So there’s more baseball than time.

These numbers struck me as wrong immediately.  If there are 32k hours of baseball in 4k hours of regular season, it means an average of eight baseball games being played at any hour of the day or night. Since there’s a maximum of 15 games being played simultaneously (because 30 teams), that would mean a full baseball schedule for an average of nearly 12 hours every day.  There is a lot of baseball, but not that much.  They don’t play at 3am, and they take occasional days off to travel.

So, let’s run the numbers:

• 162 games by 30 teams is 162×15 games, or 2430 games.
• Average game lasts 162 minutes. 162×2430 is 393660 minutes, or 393660/60=6561 hours.
• 185 day season is 185×24=4440 hours

The total hours of baseball seems off. In fact, it’s off by exactly a factor of five, suggesting the story was working with 12-minute hours for some reason.  With 6561 hours of baseball in a 4440 hour season, we’re looking at about 1.5 baseball games simultaneously, averaged over the season, which is more plausible.

While we’re at it, we might want to check on the 162 minutes/game since it’s a bit suspicious for two unrelated numbers in the same calculation to both be 162.  It’s right, at least for 2023, though it’s down from over 3 hours the previous season.

Team Ratings for Week 6

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 40 matches played, 25 of which were correctly predicted, a success rate of 62.5%.
Here are the predictions for last week’s games.

Predictions for Week 6

Here are the predictions for Week 6. 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 8

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 49 matches played, 36 of which were correctly predicted, a success rate of 73.5%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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 7

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 30 matches played, 19 of which were correctly predicted, a success rate of 63.3%.
Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. 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.

For years, Bird of the Year has been the only self-selected (‘bogus’, ‘straw-poll’, ‘unscientific’) survey endorsed by StatsChat.  The unique feature of Bird of the Year as a bogus poll is that no-one pretends it is anything else. The pūteketeke won Bird of the Century fair and square, due to John Oliver’s marketing efforts, and no-one seriously thinks this says anything real about the relative popularity of New Zealand birds.

The key takeaway from Bird of the Century is this is what bogus polls are like. All of them. When a bogus poll agrees with population opinion, it’s just an accident.  When someone claims to have information from a survey result, it’s always good to ask whether it’s the type of survey that’s more accurate than just pulling a number out of your arse, or not.

A couple of weeks ago, there was a widely–reported claim made by a spokesperson for AA Insurance that 53% of people in New Zealand wanted a ban on domestic use of fireworks. None of the media outlets asked (or reported asking) anything about how he got that number.  When a news report says something that’s attributed to an anonymous source like that, you want to know who is vouching for the credibility of the source.

I happened to see a post on social media by someone who had been in a survey that could have been the one quoted, which was run by my2cents.  I don’t know how good their surveys are, but they at least qualify as trying to get the right answer.  If that survey was actually the one reported by AA Insurance, it would be good to know.

In some contexts, such as election polling or policy decisions, you might want to know more about the methods used and the reputation of the pollsters.  Even in simple news reporting, though, it’s important to ask if this is the sort of survey that gives you information or the sort of survey that just gives you grebes.

Team Ratings for Week 5

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, 20 of which were correctly predicted, a success rate of 62.5%.
Here are the predictions for last week’s games.

Predictions for Week 5

Here are the predictions for Week 5. 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.