Competitive Advantage

I was asked to have a cup of coffee by a fellow data scientist in the valley. He had a particular motive. He wanted to know if there was a way to make a profit in daily fantasy sports betting. He had done well betting some games in the NBA, but when he tried baseball, he was getting his clock cleaned. He wanted to know why, so I asked about his approach and methods. He was closed off to sharing, which in the era of open source, was a bit of a red flag—but I pressed on. I asked, “What exactly  are you trying to do?” His response was that he wanted to have better projections than everyone. I asked him why he wanted to do that. He stared back blankly at me in the same way my four-month-old son does when I ask him why he keeps trying to put everything in his mouth. I expounded on it. Statisticians love love love baseball. They love it so much, that love manifested itself into a Brad Pitt movie. If bioinformatics did this, we would get a feature film starring Tommy Wisseau. Therefore, it would be extremely difficult to outproject the leading sites. So how do you gain a competitive advantage when everyone is already using state of the art? First, make sure everyone is using state of the art. Rather than building a better mousetrap, maybe find where the mice are. In this particular example, I asked if they tracked the users’ stats. He said they did. I told him to follow the 80/20 rule and go after the weaker players. If that did not work then change to a different sport. The takeaway is that data science is much like gambling in general; a lot of progress is made in the strategy and not the implementation. No one group will compete against Google, but luckily Google is not ubiquitous . . . yet. Pick the battles that no one else is fighting.