It’s rare when popular culture uses Six Sigma principles to entertain the masses. It is rarer still when that entertainment incorporates professional sports. The rarest of the rare is when Six Sigma and professional sports are combined to help create a blockbuster movie.
Six Sigma, Major League Baseball and a hit Hollywood film all intersect seamlessly in the 2011 film, Moneyball.
Six Sigma makes its appearance early in the movie when Oakland A’s General Manager Billy Beane, played by Brad Pitt, sits down with a group of talent scouts to help select the next year’s draft picks. The grizzled baseball scouts sit around the table and – between pauses to spit tobacco juice into Styrofoam cups – the scouts give Billy Beane, and the audience, the criteria they use to identify the best potential baseball talent.
“This kid’s got a great swing,” an old scout begins. This scout predicts a player’s future in baseball based on his feeling about the player’s current performance. The scout is impressed by the talent he sees, and Billy Beane says nothing.
Another scout speaks of a different prospect, “This player looks like he was born to wear a baseball uniform, and he’s got a good body.” The scout implies that he can judge a player’s talent and performance in professional baseball based on how well the player appears to fit the part. Billy Beane is unmoved.
Their collective wisdom falls to its low point when a third scout introduces the “girlfriend rule.” You can tell how much confidence a kid has in his baseball abilities by how good looking his girlfriend is.
As we watch, it is clear that Billy Beane is not buying any of it, and soon we realize that neither are we.
Moneyball showed us that a critical flaw in Major League Baseball was how many teams chose and paid players based on criteria that were superficial and subjective. The subjectivity in identifying baseball talent is what Billy Beane spends the rest of the movie trying to change.
Six Sigma fights this same battle against subjectivity in an evaluation process.
Six Sigma and Objectivity
A baseball franchise must use objective metrics to choose its players so that it may begin the season on a solid foundation. These metrics must accurately gauge the player’s true performance and potential. Likewise, a Six Sigma team must objectively assess the project’s current performance before it can effectively analyze the processes, diagnose its problems and offer appropriate solutions.
Six Sigma imposes objectivity by requiring a data collection plan that ensures objective measurement. This ensures that any resulting assumptions are based on objective criteria rather than a few subjective opinions.
As in baseball, it is not important how the process looks on a white board or how it makes the team feel when they see it in motion. What matters is how the process performs as measured and quantified by objective data.
Why is rigorous objectivity more important to processing performance than subjective guesswork? Objectivity rules for the same reason that the eyes of even the most experience baseball scouts can be deceived. A player’s hitting performance cannot be adequately judged with the relatively few at-bats the scout sees when sizing up the hitter. By evaluating only a few at-bats, an average player may appear to perform as well as, or better than a superior player, and the scout may be misled by what he perceives to be better form or appearance.
The eyes can be deceived, but objective hitting statistics are a solid indicator of a player’s performance, potential and worth.
The same can be said about evaluating a production process. Evaluating a process with casual observation can lead the project team into misunderstanding the process, misdiagnosing its problems and presenting the wrong solution. This is the recipe for striking out and sitting back down on the bench.
However, when subjectivity is replaced by a data collection plan and rigorous Six Sigma statistical analysis, it is much easier to identify the root causes of a problem. When the problem is clearly understood, the solutions the team implements are far more likely to work. This is the recipe for a home run and a remarkable winning season.
