Posts filed under Sports statistics (1776)

Team Ratings for Round 3

This year the predictions have been slightly changed with the help of a student, Joshua Dale. The home ground advantage now is different when both teams are from the same country to when the teams are from different countries. The basic method is described on my Department home page.

Here are the team ratings prior to Round 3, along with the ratings at the start of the season.

Performance So Far

So far there have been 9 matches played, 8 of which were correctly predicted, a success rate of 88.9%.

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 2

This year the predictions have been slightly changed with the help of a student, Joshua Dale. The home ground advantage now is different when both teams are from the same country to when the teams are from different countries. The basic method is described on my Department home page.

Here are the team ratings prior to Round 2, along with the ratings at the start of the season.

Performance So Far

So far there have been 2 matches played, 2 of which were correctly predicted, a success rate of 100%.

Here are the predictions for last week’s games.

Predictions for Round 2

Here are the predictions for Round 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 1

Welcome to the new Super Rugby season. This year the predictions have been slightly changed with the help of a student, Joshua Dale. The home ground advantage now is different when both teams are from the same country to when the teams are from different countries. The basic method is described on my Department home page.

The introduction of a new team causes problems. I have arbitrarily assigned a rating of -10 to the Kings. This value worked reasonably well when the Rebels were introduced but obviously there will be uncertainty about games involving the Kings until they have some history.

Here are the team ratings prior to Round 1, 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 points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Q:  Have you started eating more chocolate yet?

A: I assume this is about the New England Journal paper.

Q: Of course.  You could increase your chance of a Nobel Prize

A: There are several excellent reasons why I am not going to get a Nobel Prize, but in any case I don’t have to eat the chocolate: anyone in Australia or New Zealand would do just as well. You can have my share.

Q:  What do you mean?

A: The article didn’t look at chocolate consumption by Nobel Prize winners, it looked at chocolate consumption in countries named in the official biographical information about Nobel Prize winners.  This typically includes where they were born and where they worked when they did the prize-winning research, and in some cases yet another country where they currently work.

Q: Does the article admit this?

A: In part.  The author admits that this is just per-capita data, not individual data.  Because he just got the Nobel Prize data from Wikipedia, rather than from the primary source, he doesn’t seem to have noticed that multiple countries per recipient are counted.

Q: Would the New England Journal of Medicine usually accept Wikipedia as a data source when the primary data are easily available?

A: No.

Q: What about the chocolate data?

A: The author doesn’t say whether the chocolate consumption measures weight as consumed (ie, including milk and sugar) or weight of actual chocolate content. That’s especially sloppy since he goes on and on about flavanols. Also, the Nobel Prize data is for 1901-2011 and the chocolate data is mostly just from 2010 or 2011: chocolate consumption in many countries has changed over the past century.

Q: Do you want to say something about correlation and causation now?

A: No, that’s what you say when you don’t know what causes spurious correlations.

Q: So what did cause this correlation?

A: There are at least two likely contributions.  The first is just that wealthy countries tend to have more chocolate consumption and more Nobel Prizes.  Chocolate and research are expensive.  The second is more interesting: it’s the same reason that storks per capita and birth rates are correlated.

Q: Storks bring chocolate as well as babies?

A: Not quite.  Birth rates and storks per capita tend to be correlated because they are both multiples of the reciprocal of population size.   Jerzy Neyman pointed this out in the prehistory of statistics, and Richard Kronmal brought it up again in 1993.  More recently, someone has done the computation with real data (p=0.008). Imperfect standardisation will induce correlation, and since Nobel Prizes almost certainly don’t depend linearly on population, the correction is bound to be imperfect.

Q: Why did the New England Journal publish this article?

A: It wasn’t published as a research article; it was in their ‘Occasional Notes’ series, which the journal describes as “accounts of personal experiences or descriptions of material from outside the usual areas of medical research and analysis.”

Q: Isn’t it good that stuffy medical journals do this sort of thing occasionally? There’s nothing like a good joke

A: Well, you might hope they would do it better, like the BMJ does.  This is nothing like a good joke.

Team Ratings for the Grand Final

Here are the team ratings prior to the Grand Final, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 200 matches played, 123 of which were correctly predicted, a success rate of 61.5%.

Here are the predictions for last week’s games.

Prediction for the Grand Final

Here is my prediction for the Grand Final.

Team Ratings for the Preliminary Finals

Here are the team ratings prior to the Preliminary Finals, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department
home page to see this.

Performance So Far

So far there have been 198 matches played, 121 of which were correctly predicted, a success rate of 61.11%.

Here are the predictions for last week’s games.

Predictions for the Preliminary Finals

Here are the predictions for the Preliminary Finals

Team Ratings for Finals Week 2

Here are the team ratings prior to Finals Week 2, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 196 matches played, 119 of which were correctly predicted, a success rate of 60.71%.

Here are the predictions for last week’s games.

Predictions for Finals Week 2

Here are the predictions for Finals Week 2

Team Ratings for Finals Week 1

Here are the team ratings prior to Finals Week 1, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 192 matches played, 115 of which were correctly predicted, a success rate of 59.9%.

Here are the predictions for last week’s games.

Predictions for Finals Week 1

Here are the predictions for Finals Week 1

This appeared in Uni News,  the internal University of Auckland magazine sent to a wide outside audience, a week or so after the end of the Super 15. David came top equal with sports writer Dylan Cleaver, and says he’ll be spending time over this summer “looking at some possible improvements to the prediction method”. Watch this space!

Team Ratings for Round 26

Here are the team ratings prior to Round 26, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 184 matches played, 108 of which were correctly predicted, a success rate of 58.7%.

Here are the predictions for last week’s games.

Predictions for Round 26

Here are the predictions for Round 26