Posts filed under General (3148)

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 53 matches played, 33 of which were correctly predicted, a success rate of 62.3%.
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 19

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 108 matches played, 75 of which were correctly predicted, a success rate of 69.4%.
Here are the predictions for last week’s games.

Predictions for Round 19

Here are the predictions for Round 19. 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 20

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 133 matches played, 103 of which were correctly predicted, a success rate of 77.4%.
Here are the predictions for last week’s games.

Predictions for Round 20

Here are the predictions for Round 20. 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 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, 22 of which were correctly predicted, a success rate of 68.8%.
Here are the predictions for last week’s games.

Predictions for Round 5

Here are the predictions for Round 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.

I bet you they won’t play this song on the radio
I bet you they won’t play this new (bleep) song
It’s not that it’s (buzzer) or (horn) controversial
Just that the (bell)-ing words are awfully strong

— Monty Python, I Bet You They Won’t Play This Song on the Radio

Last year, I missed mentioning one of the minor jewels of the NZ regulatory landscape: the report ‘Language That May Offend in Broadcasting’ from the Broadcasting Standards Authority.

There’s pretty wide agreement around the world that some words should be avoided in radio and TV, and that what language is appropriate would vary according to the setting.  A gritty crime drama would be allowed more license that the main news bulletin, and someone using “Jesus” to refer to the Christian messiah would be different from someone using the word just as an expletive. There’s often reference to “community standards”.  In a lot of places, though, “community standards” are just pulled out of the air or handed down in precedent.

The Broadcasting Standards Authority does a survey of New Zealand every few years, where they actually ask a representative sample of the population how they feel about various words in various contexts (and asks them to suggest other offensive words that weren’t listed).

This time, the BSA added some words or phrases from Samoan and Te Reo Māori. They also added a few English words that are pejorative references to particular groups — in contrast to religious references and content-free profanity, this sort of word has remained unacceptable or perhaps become more unacceptable.

There’s a new tool for graphing the Twitter output of people (or bots). It shows number of tweets per hour, and either the type of tweet (single new, threaded new, retweet ,reply, quote) or the app used to do post it.  It also shows repeated tweet text

Here’s my Auckland bus Twitter bot. It only does standalone tweets, four per hour, and it broke down for a few days. There’s the standard evening tweet repeated every day and the “can’t find the internet” tweet a few times.

Here are some NZ media people, where you can see the impact of the Christchurch terrorist attacks, roughly in the middle of the time period (click to enlarge)

Matt Nippert

Kirsty Johnson

Sean Plunket

Keith Ng

and, for fairness, me

You can see I was overseas last week. My tweeting went down a bit after Friday 15th.

• Newshub headline: Massive earthquake, tsunami in New Zealand inevitable in our lifetime – experts.  In the story “We know a large earthquake and tsunami is something we will face in our lifetime, or that of our children and grandchildren.”
• From Stuff. Understatement watch: “Census general manager Kathy Connolly said 60 court cases were being lodged in relation to people not completing the census. That was not everyone who failed to fill out the census”
• The New York Times has an analysis of online links between white extremist terrorists.
• NZ statistician Peter Ellis has moved to Melbourne and is now predicting AFL results
• CNBC story about artificial intelligence in the criminal justice system.

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 46 matches played, 29 of which were correctly predicted, a success rate of 63%.
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 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 24 matches played, 16 of which were correctly predicted, a success rate of 66.7%.
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.

We’re three weeks into the month-long Ihaka Lecture Series, and it has been well received – thank you to those who have turned up in person and online.

Our final speaker, Robert Tibshirani, right, is up on Weds April 3 at the University of Auckland (details here). Robert is Professor of Statistics and Biomedical Data Science at Stanford University.

He is best known for proposing the ‘lasso’, a sparse regression estimator, and describing its relationship to the idea of boosting in supervised classification. He will talk about modern sparse supervised learning approaches that extend the lasso.

In the meantime, you might like to check out the films of the last three speakers. First up on March 13 was by Bernhard Pfahringer, left, who is Professor of Computer Science at the University of Waikato.

He is a member of the Weka project, New Zealand’s other famous open-source data science contribution, and here talks about the design and development of Weka and more recent projects.

Next was supposed to be JJ Allaire, the founder and CEO of RStudio, and the author of R interfaces to Tensorflow and Keras. However, ill-health prevented him coming, and our very own Professor Thomas Lumley stepped in.

Thomas talked entertainingly about deep learning, in particular how deep convolutional nets are structured and how they can be remarkably effective, but can also fail, as he puts it, “in remarkably alien ways”.

Following was Dr Kristian Lum, Lead Statistician at the Human Rights Data Analysis Group. Her research has concretely demonstrated the potential for machine learning-based predictive policing models to reinforce and, in some cases, amplify historical racial biases in law enforcement.

She talked about algorithmic fairness, and about ways in which policy, rather than data science, influences the development of these models and their choice over non-algorithmic approaches.