How well will you do tomorrow? How can we be sure?

Algorithmic and biomedical advances are now producing sports coaches, mangers and team owners the tools to predict which players have picked and which ones have their full potential ahead of them.


I don’t use much of quantitative methods when it comes to sports. I think it takes away my excitement.

http://content.intweetiv.com/view?title=SAP+Uses+Own+Big+Data+Analytics+to+Project+Super+Bowl+Winner&iframe=http://www.eweek.com/enterprise-apps/sap-uses-own-big-data-analytics-to-project-super-bowl-winner.html/

After the Super Bowl game finished – I saw on twitter that SAP had predicted that Denver will win over Seattle in a close match. As it turned out – Seattle won a rather one sided match with a very young side.

I didn’t work on the predictive Analytics solution that made the prediction for Super Bowl and I am not authorized by SAP to provide a response. But I wanted to share my personal views on this matter.


Then I saw Vijay Vijayasankar’s discussion about the perils of predictive analytics. He makes the crucial points:

Predictive Analytics in general cannot be used to make absolute predictions when there are so many variables involved . In fact – I think there is no place for absolute predictions at all . And when the results are explained to the non-statistical expert user – it should not be dumbed down to the extent that it appears to be an absolute prediction .

Predictive models make assumptions – and these should be explained to the user to provide the context . And when the model spits out a result – it also comes with some boundaries (the probability of the prediction coming true , margin of error , confidence etc). When those things are not explained – predictive Analytics start to look like reading palms or tarot cards . That is a disservice to Predictive Analytics .

If the chance of Denver winning is 49% and Seattle winning is 51% – it doesn’t exactly mean Seattle will win . And not all users will look at it that way unless someone tells them more details .

In business , there is hardly any absolute prediction ever . Analytics provide a framework for decision making for the business leaders . Analytics can say that if sales increases at the same historic trend , Latin America will outperform planned numbers next year compared to Asia. However , the global sales leader might know more about the nuances that the predictive model had no idea of, and hence can decide to prioritize Asia . The additional context provided by predictive Analytics enhances the manager’s insight and over time will trend to better decisions . The idea definitely is not to over rule the intuition and experience of the manager . Of course the manager should understand clearly what the model is saying and use that information as a factor in decision making .

When this balance in approach is lost – predictive Analytics gets an unnecessary bad rap.

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