For a variety of cultural reasons, the sports world has long been the battleground of a clash of styles about how to use mathematics and data analysis; sports statistics have existed as long as sports have, but for most of history they have been used more often as tools to support conventional wisdom than as avenues for questioning it. While this began to change with the sabermetric revolution in baseball that started with Bill James in the 1980s and hit the mainstream with the Michael Lewis book and subsequent movie Moneyball about the mid-2000s Oakland A’s, there is still an inherent mistrust of analytics and “nerd stuff” among many in the sports world.
The New York Times article “What Happens When Baseball-Stats Nerds Run a Pro Team?,” by Sam Miller is both an example of how and why this mistrust happens and a helpful source of ways to overcome it. When Miller and his colleague Ben Lindbergh were handed the metaphorical keys to the Sonoma Stompers, they dove in headlong with complicated formulae and confident predictions, not realizing that their plans might not be well understood or received by the players and coaches. In addition, even basic applications of math and scouting seemed esoteric when presented under the vaguely ominous umbrella of “analytics,” meaning that sometimes they weren’t trusted even when what they were doing and saying wasn’t necessarily complicated math at all.
The most relevant section of the article to any person or entity working in sports analytics (such as Decision Lens) is this: “We sold our story as something imposing — ‘data analytics’ — and we made it about us. We should have sold it as providing them information, and made it about the team.”
In my nearly seven years working with Decision Lens’ sports clients, I have always tried to be careful not to present Decision Lens as a threat to the established order and myself as a conquering nerd-hero wielding math like a sword from Dungeons and Dragons to take all the scouts’ jobs. (I’m not even any good at D&D!) On the contrary, the business of improving a sports team should never be about Stats Versus Scouts, but a healthy synthesis along the lines of the Hegelian dialectic. (Although I tried to read one of Hegel’s philosophy books once and gave up in a confused haze.) Stats AND Scouts. Quantitative data AND qualitative analysis. Crunching the numbers AND locker room chemistry.
Both myself, Miller and Lindbergh have learned that, in sports as in life, the best data analysis in the world doesn’t mean as much if you can’t get people to buy into it. Sports statistics, software, Decision Lens models, all the intelligence and computing power we can bring to bear on problems: those serve to help customers supplement and challenge their preconceived notions, not replace the people and obliterate their notions without giving them a say in the matter. As much as anything else, that ability to combine mathematical analysis and the wisdom of people is what Decision Lens is about.