Lessons in Entrepreneurship: Moneyball (Big Data in Baseball)

I really enjoyed the Moneyball movie, starring Brad Pitt and co-written by Aaron Sorkin (one of my favorite screen writers).  So much so, that after watching the movie, I was compelled to read the Moneyball book by Michael Lewis, from which the movie was based.  I am glad that I did, because as much as Moneyball is a sports story, I think it is an even better business case study, for companies with limited budgets compared to their competitors, like many of the readers of this blog.

For those of you that did not read the book or see the movie, Moneyball is the story of the 2002 Oakland A's baseball team and their unorthodox front-office GM, Billy Beane.  Beane had the challenge of working for one of the "poorest" teams in baseball, with an annual payroll budget that was around 75% less than the budgets of perrenial World Series contenders, like the New York Yankees and Boston Red Sox.  Billy was determined to put a championship-caliber team on the field, regardless of his budget challenges, and he needed to think way "outside of the box" in order to do so.

What he basically did was counter to everything people in baseball would deem reasonable.  He implemented big data driven management techniques that were years ahead of the big data hoopla we are in the midst of today. He took certain control of the on-field decision making away from the manager on the field (in what other industry would the CEO not control every aspect of his business, was his thinking).  He overrode the decisions of all his talent scouts (for the same reasons, saying they were looking for all the wrong things in players based on years of "old school" thinking). 

The guts of Beane's strategy came down to him getting a competitive edge by using big data in baseball to glean insights for on-field advantages and other arbitrage opportunities he could exploit.  As an example, other teams were focused on recruiting high-potential high school players with high batting averages and perfect physiques, in terms of strength and speed.  Beane knew the market overvalued things like that, and that he couldn't afford them.  What he also knew from the big data, was that on-base percentage (not batting average) was a much better indicator for "buying runs and wins", and it didn't matter what the batter looked like physically, provided he could consistently get on base, via hitting or walks.  And, that proven college players, tended to have a much higher odds of Big League success than promising high schoolers who were still developing.  Those were the guys that were less "sexy"; maybe they were overweight or older in age or coming off an injury.  But, in all cases, their imperfection in the eyes of others, based on the wrong metrics for winning on a shoe-string budget, made them affordable "jewels" in Beane's system.

Beane was adamant that taking any optional on-field action that resulted in an out was never good for driving in runs and winning games.  So, he implemented hard and fast rules for his players and coaches.  Walks were of much higher importance, than swinging for the fences on bad pitches.  Sacrifice flyballs to advance the runners on base only handcuff the inning with an additional out.  And, God forbid if a player actually tried to steal a base, and risk being thrown out in the inning.  Beane tried to take much of the "risk" out of baseball, and basically tried to stack the casino odds in his favor.  All based on the big data in baseball that was available, that no one else was using to make business decisions in this  "good old boys club" called Major League Baseball, which had been run much the same as it was during the days of Ty Cobb or Babe Ruth.

Beane's success with this model was incredible.  Despite losing three all-star players from the year before, that he could no longer afford when their contracts came up for renewal, the Oakland A's won their division and set the record for all-time consecutive wins in the process.  Despite the budget constraints and all the naysayers saying Beane's big data strategies would never work, given the collective wisdom within the "MLB club" and the media, the Oakland A's defied all the odds and put a championship-caliber team on the field--one where discipline and hard work was rewarded, and big egos fueled by meaningless statistics were left behind. 

Even though the Oakland A's did not make it to the World Series that year, anyone with their head on straight took notice.  Many of the other teams were quickly trying to reinvent their ball clubs, in the same way Beane had reinvented his.  Beane had actually changed the game of baseball, and he was in hot demand from many other teams wanting his services, including the mighty Boston Red Sox, who made him an offer to join their team as the highest paid GM in professional sports history (which Beane turned down to stay at Oakland, closer to his family).

The business lessons from this story are numerous: (i) don't be afraid to "swim upstream", counter to conventional wisdom, if you are confident your methods will work; (ii) big data runs through all businesses: what datapoints can you exploit that your competitors are not; (iii) are you managing your business on the right metrics; (iv) make sure you run your business like a business, with proper controls to make sure the desired management outcomes are acheived; (v) Beane's insights came from his own personal experience as a former highly-regarded high school player that never lived up to Big League expectations, so take lessons from your own experience; and (vi) David can slay Goliath, regardless of budgets, with a little smart thinking. 

So, to all you aspiring startups out there going up against big well-funded competitors: Batter Up!!

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