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Betfair prediction market

January 22, 2016 By editor

Betfair Predicts screenshot

Those who are disillusioned by the failings of opinion polling may be interested in Betfair Predicts—a set of predictions based on markets in their betting exchange.

Filed Under: General

Kaggle provide home for high quality public datasets

January 21, 2016 By editor

Kaggle have launched Kaggle Datasets—a repository of “high quality public datasets”.

The repository will support:

  • Access: simple, consistent access to the data with clear licensing
  • Analysis: a way to explore the data without downloading it
  • Results: visibility of previous work performed using the data
  • Conversation: forums for discussing the nuances of the data

Filed Under: Data analysis Tagged With: dataset

NFL players join the Internet of Things

January 21, 2016 By editor

football field

The NFL is fitting players’ shoulder pads with data sensors. This will allow the real-time collection of metrics such as position, speed and acceleration.

In last year’s Pro Bowl sensors were installed in footballs to measure the length of throws. Surprisingly, given “Deflategate”, they have chosen not to expand this idea.

In addition to being used for the “game stats” that are popular in the US, the data will be used by coaches and be available to the public—for a fee. Will be interesting to see what the market can do with it.

While NFL players are clearly “high value assets”, falling prices for sensor technology and computing resources mean we are going to see similar approaches deployed in more “mundane” areas—such as ranching.

Filed Under: Big data, Data analysis Tagged With: IoT, NFL, sensor

Election polling errors blamed on bias

January 19, 2016 By editor

UK polling station sign

A report has concluded that the spectacular failure of pollsters to predict the result of the 2015 UK Parliamentary elections was largely due to

systematic over-representation of Labour voters and under-representation of Conservative voters

The report, compiled by a panel of academics and statisticians, was commissioned by the polling industry to determine why they had predicted a “photo-finish” in an election where Conservatives outpolled Labour by 36.9% to 30.4%—a crushing defeat for Labour that lead to the resignation of their leader.

Pollsters apparently used collection methods that were more likely to be used by young (Labour-leaning) voters than older (Conservative-leaning) voters. Frankly, not realising that online surveys are going to under-represent the over 70s is a shocking oversight.

While betting markets also under-estimated the extent of the win, they did better than the polling industry—without the expense.

Filed Under: Data analysis, Data science Tagged With: betting, bias, polling, prediction market

R is no more scary than Excel

January 18, 2016 By editor

John Mount, Win-Vector

John Mount of Win-Vector looks at doing analysis in Excel and R. He concludes that R isn’t as scary as it sounds.

If you are an Excel jockey who’s heard about R, but has yet to take the plunge, it’s worth a look.

Filed Under: General

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