Michael Lewis was describing how controversial his book, Moneyball, was when it first came out in 2003. Baseball coaches and scouts didn’t need no stinkin’ analytical rigor or new metrics. A decade later, as I attend the MIT Sloan Sports Analytics Conference (courtesy of HP Vertica, a sponsor), I expected to see plenty of mature Big Data savvy sports executives, as the Sabermetrics discipline has spread far beyond baseball. There is definitely that but more striking is the vast number of areas where analytics are still primitive.
Mark Cuban, owner of the Mavericks described the genetic markers and oxygen level metrics which can predict athlete performance. He described the role of his medical staff members including a sports psychologist and the health/psyche metrics that are still in their infancy. Other execs described the need to measure work ethic and work habits of athletes. Several NFL executives, in discussing the importance of the 40 yard dash in measuring wide receiver performance could not agree on the importance of the first 10 yard burst, the “flying 20” or the strength of the receiver to brush off the tackle. And why trades don’t often work as individual player metrics do not predict well how they may fit on other teams. Nate Silver, who has made his mark both in baseball and political analytics says there are so many new baseball metrics that have only been measured in recent years (compared to other baseball metrics dating over a century) that we are almost starting anew. Paraag Marthe, COO of the San Francisco 49ers described the location analytics he is planning at their new stadium to allow fans to find their friends and for redefined, potentially mobile vending stations.
The session titles at the event tell the newer areas where analytics are spreading. There’s ticketing analytics in a world of secondary ticket markets and dynamic pricing. There’s injury analytics as athletes appear to be bouncing back quicker and better after major surgeries. There’s “fanalytics” to measure sentiment and usage of before, during and after game apps. There’s scouting analytics as in one SAP has just announced. There’s in-game analytics including a session on missile tracking tech which can hone in on a pitcher’s curveball or the best position for a rebound. There are sessions about analytics in hockey, MMA, NASCAR, English Premier League Soccer. Other executives talked about how sensors are contributing data in sailing and other sports. ESPN is running a session on Use of Analytics in Storytelling. There are other sessions on visualization of all this data.
Clearly, these sports executives could offer much to other industries as they launch their own advanced analytics projects. But try recruiting Daryl Money, GM of the Houston Rockets as he described the tortured path to his dream job. Not something he would easily walk away from.
So the encouraging sign – hundreds of students from MIT and many other colleges are at the event. Not all are math/stats majors. Several are here (like a couple I talked to) because they love their game and would love to get a shot at an operational role either at the professional or college level. Or an analytical job in other industries.
And they got good advice from Paraag – communication skills are almost as important as the analytical rigor. He recommended they additionally take classes on negotiations and organizational dynamics. Another exec told them to spend time in the film room. Analytics without the context can be useless.
There is much to learn from sports analytics. But the biggest lesson came from a one-on-one I had with Mark Cuban. “if you just focus on one area (of analytics in any sport or industry), you are dead”.