I am not the biggest football fan around – I am a big fan of cricket though. And despite my day job is about making sense of data – I don’t use much of quantitative methods when it comes to sports . I think it takes away my excitement .
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 . A few friends on twitter pointed out that SAP made a bad prediction before the game , and they are not wrong .
In parallel, I decided to skip watching the India vs NewZealand cricket series thinking India will win this 5-0 and it will be boring . I was close on my gut prediction – the score was 4-0, just that India was on the losing side of that equation . On the bright side , I am happy that I didn’t have to watch the massacre and live with the nightmares .
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 since I am sitting at PHX waiting for my flight in the midst of many dejected Denver fans who are analyzing the result in painful detail – I wanted to share my personal views on this matter .
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.
That being said , I heard next year Super Bowl is played in Arizona . Maybe I should start following the game a bit more closely 🙂