Stats Chat

Team Ratings for Round 11

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 60 matches played, 41 of which were correctly predicted, a success rate of 68.3%.
Here are the predictions for last week’s games.

Predictions for Round 11

Here are the predictions for Round 11. 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 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 76 matches played, 58 of which were correctly predicted, a success rate of 76.3%.
Here are the predictions for last week’s games.

Predictions for Round 12

Here are the predictions for Round 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 11

I missed posting predictions for this round thinking that there was a longer break between games. Because I use an algorithm and code and no subjective analysis, these predictions are exactly what I would have posted and I am posting them now for completeness.

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 70 matches played, 54 of which were correctly predicted, a success rate of 77.1%.
Here are the predictions for last week’s games.

Predictions for Round 11

Here are the predictions for Round 11. 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 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 14 matches played, 8 of which were correctly predicted, a success rate of 57.1%.
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

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 7 matches played, 5 of which were correctly predicted, a success rate of 71.4%.
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.

Back in early December, I wrote about a political ad authorised by Simon Bridges, showing the price of fuel.

As I said the, the numbers do not remotely match the graph.  A graph using those numbers would look more like

Dylan Reeve and other people complained to the Advertising Standards Authority, both about the graph itself and about the choice of numbers, which (in his opinion and mine) was cherrypicked in a misleading way.

The ASA decided (in a split decision) that the graphic was not misleading

The majority said the data displayed was correct which saved the hyperbolic graphic from being misleading, given the political medium used and the principles of advocacy advertising.

I believe this is decision is bad in terms of norms for mainstream political advertising, and that it’s likely to be factually incorrect as to the impact of the graphic.

The cherrypicked numbers are misleading, but they are misleading in a way that is, sadly, routine in political advertising.  I’ve written about examples from both parties here since StatsChat started. My starting point for any political advocacy involving numerical comparisons is always that the numbers are likely to be correct as quoted, but chosen to mislead. Given the established norms,  I can understand the ASA not wanting to get involved.

The distorted graph, on the other hand, seems to be new.  I was genuinely surprised at the extent of the distortion — well beyond common tricks of perspective or false baseline.

If writing the numbers on a misleading graph was enough to stop it being misleading, there would be no point having data graphics.  The whole point of data graphics is that they provide a clearer and more forceful impression of the data than just tabulating the numbers.  Misleading graphs are misleading.

Team Ratings for Round 9 Delayed Match

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 69 matches played, 53 of which were correctly predicted, a success rate of 76.8%.
Here are the predictions for last week’s games.

Predictions for Round 9 Delayed Match

Here are the predictions for Round 9 Delayed Match. 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 54 matches played, 37 of which were correctly predicted, a success rate of 68.5%.
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.

• Via Alberto Cairo’s blog, a beautiful example of how to annotate a data graphic of time-use data, at the Financial Times
• If you happen to want data-visualisation themed coffee mugs, Martin Telefont has some
• The Geography of FM Radio (via Nathan Yau on Twitter): spatial patterns of US radio stations. Some types follow population density; others strikingly don’t.
• From Peter Ellis: how much effect does seeding have in a tennis tournament? It doesn’t seem to do a lot for the top player, but it really does increase the chance of the next few getting into the final rounds.
• A computer algorithm can do surprisingly well in answering questions about pictures just by knowing what answers are common for particular questions and without looking at the picture.
• “The data set covers just 300 patients, and only 38 of them gave birth prematurely”, nevertheless, a bunch of computer algorithms thought they could accurately predict preterm birth from electrical activity in the muscles of the uterus, before 37 weeks.  The algorithms used some created data examples, intended to boost the effective sample size, which is a perfectly respectable technique.  Using those created examples in estimating the final accuracy, on the other hand, can be a problem.  [Research preprint]
• HPV vaccination is preventing cancer-causing HPV infections: data from the UK (it’s also not currently increasing rates of other HPV strains) (PDF report)
  

One reputable source of moderately technical information about the new coronavirus is the MRC Centre for Global Infectious Disease Analysis, at Imperial College, London.  They’ve worked on outbreak modelling and control advice for a long time, across a wide range of epidemics (including both previous coronavirus outbreaks: SARS and MERS).

Their latest (third) report says that it’s clear there has been sustained person-to-person transmission — a spreading epidemic — in China, and that basically nothing else is clear.

From the Discussion section

Whether transmission continues at the same rate now critically depends on the effectiveness of the intense control effort now underway in Wuhan and across China. We note the large body of evidence that suggests that the reproduction number for SARS changed considerably when populations became fully aware of the threat. If a similar change to contact patterns is occurring in this outbreak, rates of transmission are likely to be lower now than during the period for which these estimates were made, due to control measures and risk avoidance in the population. Whether the reduction in transmission is sufficient to reduce R to below 1–and thus end the outbreak –remains to be seen. Reports point to mildly symptomatic but infectious cases of 2019-nCoV, which were not a feature of SARS. Prompt detection and isolation of such cases will be extremely challenging, given the larger number of other diseases (e.g. influenza) which can cause such non-specific respiratory symptoms.While more severe cases will always need to be prioritised, control may depend upon successful detection, testing and isolation of suspect cases with the broadest possible range of symptom severity.Our results emphasise the need to track transmission rates over the next few weeks, especially in Wuhan. If a clear downwards trend is observed in the numbers of new cases, that would indicate that control measures and behavioural changes can substantially reduce the transmissibility of 2019-nCoV. Genetic data from Wuhan after the implementation of strong public health measures may also provide valuable insight into the patterns and rate of transmission.

Despite the recent decision of the WHO Emergency Committee to not declare this a Public Health Emergency of International Concern at this time, this epidemic represents a clear and ongoing global health threat. It is uncertain at the current time whether it is possible to contain the continuing epidemic within China. In addition to monitoring how the epidemic evolves, it is critical that the magnitude of the threat is better understood. Currently, we have only a limited understanding of the spectrum of severity of symptoms that infection with this virus causes, and no reliable estimates of the case fatality ratio –the proportion of cases who will die as a result of the disease. Characterising the severity spectrum, and how severity of symptoms relates to infectiousness, will be critical to evaluating the feasibility of control and the likely public health impact of this epidemic.

When they talk about ‘containing’ the epidemic within China, they don’t mean whether or not there are cases outside China — there are already are — they mean whether or not there’s sustained transmission from person to person outside China.