Sports and Technology: The Art of Winning an Unfair Game
Three years ago, I set out on a quest to discover what I call the Holy Grail of Moneyball. The big idea is that we can use sophisticated techniques to unlock a better understanding of our beloved sports. Many have tried and failed, critics have said it is a myth, and even the best of the best have given up. But filled with ambition, a mind for math and sports, and the belief that underdogs like me can do anything, I put my career on the line to go after it. Here is my story.
I previously shared that my aha-moment in connecting math and sports took place at a 2017 supercomputing conference during a lecture on the philosophy of artificial intelligence. Before I embark on the tech and startup tales that occur after said aha-moment, I think I would like to tell you why the idea of using data in sports is so gosh-darn compelling to me.
September 2011 in Houston, Texas. Like a million others, I went to the theater for the new Brad Pitt movie, “Moneyball.” I had not read the book. I did not care much about baseball stats but I was an avid baseball fan. Sabermetrics (using baseball data to answer specific questions about a team or player) was not in my vocabulary. I just loved baseball, enjoyed a good baseball movie (which seemed to most-often star Kevin Costner), and I found Brad Pitt something to behold. Little did I know of the profound impact it would have on my life.
For those not familiar with the movie, allow me to fill you in. Based on the 2003 book ‘Moneyball: The Art of Winning an Unfair Game’ written by Michael Lewis, the Oakland A’s build a winning baseball team and change the course of their season by incorporating a sophisticated sabermetric approach to scouting and analyzing players. Faced with a less than ideal budget, Billy Beane (Brad Pitt), general manager of the Oakland A’s, has an epiphany that the conventional wisdom to selecting players in baseball is all wrong. He brings in an Ivy League graduate to help rebuild the team. Together, they challenge the system and compete with richer ball clubs by recruiting flawed and undesirable players whom the scouts passed over. The catch is these players have game-winning potential according to the math of the sport. The success of the season proved that sabermetrics is a force to be considered. With a few deviations, the movie depicts a true story.
Over the next few years, I watched the movie at least 30 times, read the book, and others like it, and explored the numerous critiques of the math and the conclusions about modern sabermetrics. I had somewhat of an obsession with the idea of looking deeply at the data. It took me years to understand the appeal. You see, before Billy Beane challenged old-school traditions, the system was deeply entrenched in the use of bias to make decisions. Well-deserving players never got a shot. The system thrived on exclusionary practices and the chest-beating of those in charge of selection. Yet, when an empowered advocate listened to the data and fought to remove subjective prejudice in the decision process, good gosh, things changed! That appealed to me deeply because “Moneyball” is the story I desperately wanted for my career. I was often the poster-girl for the underestimated and often undervalued woman in tech. Ouch. It was an ugly realization. So that is it in a nutshell. I love the story of using real facts to eliminate bias and uncover truths. I want to be part of that! I want to win against an unfair game!
I cannot wait to tell you of the tech we created, the people we met along the way, how we raised (and did not raise) money to do it. Oh, what a journey!
Speaking of journeys, I would love to someday thank Paul DePodesta, the real-life Ivy League graduate who touted the use of sabermetrics to Billy Beane. No stranger to controversy over the methods, I think he would have some tales to tell. Today he is the chief strategy officer for the Cleveland Browns, so he has done well. Mr. DePodesta took a risk to do something different — and for that I am grateful!
PS — As I wrote and rewrote this account, I found myself debating whether my gender should be part of a story about data analytics in sports. I decided to take a risk and share it as my gender might be what sets my results apart from others. I look at things differently, look with the eyes of a fan, and think with a heart of inclusion. It might matter, it might not. Time will tell!
