The Six Sigma methodology relies on decisions that are made based on facts and data. One of the challenges in the Analyze phase of Six Sigma is being able to identify what is really going on in a process based on the facts and data that were collected in the Measure phase. Another challenge is to look at the facts and data in new and more insightful ways.
Consider the game of baseball, which at the Major League level has a rich history of data and statistics spanning over 100 years. For decades, baseball executives and scouts valued their own opinions about a player far more than statistics. The statistics that they did rely on, such as stolen bases, runs batted in, and batting average, were relics from the 19th century view of the game and the data that was available back at that time.
Beginning after the 2001 season, Oakland Athletics General Manager Billy Beane, along with Harvard-educated statistician and Assistant General Manager Paul DePodesta, began analyzing what it took to win based on turning data into knowledge, rather than on beliefs and hunches. Sounds a lot like the Six Sigma approach!
In the book Moneyball: The Art of Winning an Unfair Game, author Michael Lewis focused on the approach used by the Oakland Athletics under the leadership of Beane to build a winning baseball team. This approach, which became known as sabermetrics, is analytical and evidence based. It also took into consideration that the Athletics were at a severe disadvantage compared to top tier teams with respect to how much money they had available to spend on players. Beane and DePodesta focused on identifying and signing undervalued players (and getting rid of overvalued players) by using non-traditional analytical measures that focused on what produced wins on the field in the most cost-effective manner. This approach resulted in the Athletics reaching the playoffs in 2002 and 2003 with an annual payroll ($44M) that was a fraction of that of leading teams such as the New York Yankees ($125M in payroll in 2002).
Here is an example of the way that sabermetrics looked at the game of baseball in a new light. The key to winning a baseball game is scoring runs. In Six Sigma we would refer to runs scored as the Y output of a process. In order to improve Y, we must understand the variables in the process (the X input variables) that contribute to Y. Sabermetric analysis showed that runs are not correlated to team batting average (old school thinking), but are highly correlated to on-base percentage and slugging percentage.
On-base percentage measures NOT making an out. As long as a team has an out remaining in an inning, anything is possible. Once three outs have been recorded in an inning, nothing is possible. Therefore, the goal of every player must be to get on base and stay on base. Beane was totally opposed to sacrifice bunting and base stealing, two very traditional tactics used by baseball managers, because they increase the odds of making an out.
Slugging percentage measures total bases divided by at bats. A player who gets more extra base hits has a higher slugging percentage than a player that hits for the same batting average but records mostly singles.
A new statistic, On-base Plus Slugging (OPS), was first proposed in 1984 and slowly gained popularity. It began appearing on Topps baseball cards in 2004. OPS is the sum of on-base percentage and slugging percentage. OPS correlates very well to team runs scored.
Another new statistic that was first proposed around 1984, the formula for which was modified in 2002 to be more representative of the value of a player, is Runs Created (RC). Highly technical and complex in nature, this statistic measures how many runs have resulted from what a player has done while at bat and on the basepaths. It values getting on base in any way possible – by drawing a walk, getting hit by a pitch, or being walked intentionally. It also values NOT getting caught stealing and NOT grounding into a double play. In other words, it values not making an out.
In baseball, runs win games. OPS and RC are two keys to generating more runs. Identifying players that have a high OPS and a high RC at the best value based on their salaries is the key to winning with limited resources. In our businesses we have the same goals – we want to create products and services that meet and exceed those of the competition in the most cost effective manner. In our businesses, just like in the business of baseball, we need to constantly look at better ways to gather and analyze data about our processes in order to gain new and meaningful insight about how to improve.
Your comments or questions about this article are welcome, as are suggestions for future articles. Feel free to contact me by email at firstname.lastname@example.org.
About the author: Mr. Roger C. Ellis is an industrial engineer by training and profession. He is a Six Sigma Master Black Belt with over 48 years of business experience in a wide range of fields. Mr. Ellis develops and instructs Six Sigma professional certification courses for Key Performance LLC. For a more detailed biography, please refer to www.keyperformance.com.