Stats Chat

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 54 matches played, 42 of which were correctly predicted, a success rate of 77.8%.
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 10

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, 26 of which were correctly predicted, a success rate of 74.3%.
Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 10. 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.

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday October 7 2016.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of October 1 – 7 2016 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

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• There has been some … free and frank exchange of views… this week on the question of criticising published research. The phrase “methodological terrorism” was used. Rather than linking to the combatants, I’ll give you Hilda Bastian and Jeff Leek (who have themselves had strongly-worded exchanges here and elsewhere)

• “Before analytics, businesses often had policies that every customer should be treated like they’re the best customer – because absent the data, the assumption was that every customer had that potential. But in the data age, there is no more benefit of the doubt.” Cathy Carleton. Some people (mostly economists) will probably feel that this is all good. That’s  a defensible position, but poor service for the poor wasn’t a goal of the analytics system.

• Fact checking can help (sometimes)

• Pretty isochrone (equal travel time) maps of US cities (via Alberto Cairo)

• There are people here and in the US claiming that self-selected (‘bogus’) internet polls with no reweighting or modelling give useful information. Those people are wrong. Do not be those people.

Team Ratings for the Grand Final

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 200 matches played, 129 of which were correctly predicted, a success rate of 64.5%.
Here are the predictions for last week’s games.

Predictions for the Grand Final

Here are the predictions for the Grand Final. 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.

Note that because that is how it appears in the official fixture list, I have listed the Storm as the first team here which is normally the home team. I have treated the venue as neutral. If the Sharks are considered to have home ground advantage, the expected margin drops to 0.70, still a win to the Storm, but close to even money.

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 46 matches played, 36 of which were correctly predicted, a success rate of 78.3%.
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.

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 31 matches played, 22 of which were correctly predicted, a success rate of 71%.
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.

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday September 30 2016.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of September 24 – 30 2016 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)

• A post from Minding Data looking at the proportion of syndicated stories in the Herald.  I’m not sure about the definition — some stories are edited here, and it’s not clear what it takes to not have an attribution to another paper.

• Headlines matter: an analysis of how often a news site gives multiples headline to the same news story, from the Princeton Web Transparency and Accountability Project.

• On measuring the right numbers, from Matt Levine at Bloomberg View “The infamous number is that 5,300 Wells Fargo employees were fired for setting up fake customer accounts to meet sales quotas, but it is important — and difficult — to try to put that number in context. For instance: How many employees were fired for not meeting sales quotas because they didn’t set up fake accounts? “

• Data Visualisation: how maps have shown elevation, from National Geographic — including why maps of European mountains are lit from the northwest,  rather from somewhere the sun might be. (via Evelyn Lamb)

• I was Unimpressed when the authors of an unconvincing paper on GMO dangers had a ‘close-hold embargo’ — allowing journalists an advance look only if they promised not to get any expert input to their stories. It’s not any better when the FDA does it.

• What it takes to get accurate survey data on evictions, from 538

• A map of apartment rental costs in Auckland over time, by Chris McDowall, at the Spinoff.

Team Ratings for the Preliminary Finals

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 198 matches played, 128 of which were correctly predicted, a success rate of 64.6%. Here are the predictions for last week’s games.

Predictions for the Preliminary Finals

Here are the predictions for the Preliminary Finals. 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.