Big Data and IoT in Sports: Forecast Come True

Screenshot CIO Story on IoT in NFL by Thor Olavsrud http://bit.ly/1Zio3ZxIn a blog post written in January 2014 at Syncsort.com (“Big Game, Big Data: How Football is Being Transformed by Big Data”) I forecast that Big Data and the Internet of Things would eventually impact major sports in the U.S.  In a feature story written for CIO magazine by Thor Olavsrud (@ThorOlavsrud) in September 2015, parts of this forecast may becoming reality for the National Football League.

Question: How will it affect your bets in fantasy sports?  IBM Watson for unstructured expert advice? QlikView and Tableau for analytics?

Reviewing Peer Review

Screenshot of Retraction Watch post: http://bit.ly/1M0tJO8Peer review is thought to be the gold standard for advancing “proven” science, but those who regularly publish and act as peer reviewers know that peer review has its problems. A recent study published in BMJ Open looked at this issue, and was the subject of a post on Retraction Watch. I posted a somewhat lengthy comment which addresses some broader issues that have surfaced in my work with the Elsevier-sponsored Innovation Explorers group.

Chasing Big Data Variety: Predictive Analytics, Meet Your Market Foe

 

Linkedin Stock Price Graph - Yahoo Finance via Google Search 20150430 (screenshot)

The graphic shows the market behavior of LinkedIn’s stock price late afternoon of 2015-04-30. Did your analytics engine (What’s an analytics engine? See International Institute for Analytics) predict this? If not, what (big?) data were you missing?

If not, chances are, yours was a Big Data Variety problem. Correlating with, for example, only Facebook, Pinterest and other social media platforms may have been a tipoff, but not enough to forecast a 25% single day plunge.

And before you reach for the “Sell” button, you might want to revisit this two-year-old story on Forbes, when the stock price also fell. Did your analytics take that into account? The loss was less dramatic, but the cause was similar.

You may need data from other sources, and more than just sniffing URLs from corporate PR departments a la Selerity. Perhaps your forecasting engine treated that as just a day’s or a quarter’s data point, without consideration of the underlying cause. A mix of complex event processing combined with other types of machine intelligence might have had better results.