How does an organization create a culture of metrics? One example is the community of baseball, including its managers, team owners, scouts, players and fans. With better information and analysis of that information, baseball teams perform better – they win!
Legendary baseball manager Connie Mack’s 3,776 career victories is one of the most unbreakable records in baseball. Mack won nine pennants and five World Series titles in a career that spanned the first half of the 20th century. One way he gained an advantage over his contemporary managers was by understanding which player skills and metrics most contributed to winning. He was before his time in that he favored hitting power and on-base percentage players to those with a high batting average and speed – an idea that would later become the standard throughout the sport.
The 2003 book about the business of baseball, Moneyball, describes the depth of analytics that general managers like Billy Beane of the Oakland Athletics apply to selecting the best players, plus batter and pitcher tactics based on the conditions of the team scores, inning, number of outs, and runners on base. More recently, the relatively young general manager of the Boston Red Sox, Theo Epstein, assured himself of legendary status for how he applied statistics to help overcome the Curse of the Bambino – supposedly originating when the team sold Babe Ruth in 1920 to the New York Yankees – to finally defeat their arch-rival Yankees in 2004 and win a World Series. It ended Boston’s 86-year drought – since 1918 – without a World Series title.
An obsession for baseball statistics
Gerald W. Sculley was an economist most known for his article, “Pay for Performance in Major League Baseball,” which was published in The American Economic Review in December 1974. The article described a method of determining the contribution of individual players to the performance of their teams. He used statistical measures like slugging percentage for hitters and the strikeout-to-walk ratio for pitchers and devised a complex formula for determining team revenue that involved a team’s won-lost percentage and market characteristics of its home stadium, among other factors.
The Society for American Baseball Research of which I have been a member since the mid-1980s, includes arguably the most obsessive “sabermetrics” fanatics. As a result of hard efforts to reconstruct detailed box scores of every baseball game ever played, and load them into accessible databases, SABR members continue to examine daily every imaginable angle of the game. Bill James, one of SABR’s pioneers and author of The Bill James Baseball Abstract, first published in 1977, is revered as a top authority of baseball analytics.
I have been intrigued by baseball statistics from an early age. As a child I used dice for each player’s at-bat to play hundreds of baseball games. In 1970 for a course on game theory at Cornell University, I recreated my childhood game with computer software using a random number generator calibrated to the actual statistics of each batter and pitcher for the 1969 National League season. The result was that each team’s won-lost record closely matched their records of the actual season. I am proud that this computer program has been inducted in the National Baseball Hall of Fame & Museum as the oldest computer baseball game. This was obviously not as big an achievement as the Wright brothers or Charles Lindbergh, but it is something my grandsons marvel at.
What does this have to do with enterprise performance management, my monthly topic since 2004 for Information-Management.com? A lot. I have loudly advocated that performance management is the integration of dozens of methodologies, like strategy maps, key performance indicator (KPI) scorecards, customer profitability targeting, risk management and process improvement. But I have insisted that each methodology requires imbedded analytics of all flavors, and especially predictive analytics needed to anticipate the future with reduced uncertainty to be proactive with decisions and not be reactive after the fact, when it may be too late.
A practical example is analytics imbedded in strategy maps, the visualization of an executive team’s causally linked strategic objectives. Statistical correlation analysis is applied among influencing and influenced KPIs. Organizations struggle with identifying what is most relevant to measure and then determine what the best target is for that measure. Software from business analytics vendors such as SAS can now calculate the strength or weakness of causal relationships among measures and display them visually, such as with the thickness or colors of the connecting arrows in a strategy map.
Returning to baseball, an evolving application of business analytics relates to dynamic home stadium ticket prices to optimize revenues. The San Francisco Giants are experimenting with mathematical equations that weigh ticket sales data, weather forecasts, upcoming pitching matchups and other variables to help decide whether the team should incrementally raise or lower prices right up until game day. The revenue from a seat in a baseball stadium is immediately perishable after the game is played. So any extra available seat sold at any price directly drops to the bottom line as additional profit.
Another baseball analytics example involves predicting player injuries, which are increasing at an alarming rate. Using an actuarial approach similar to the insurance industry, the Los Angeles Dodgers’ director of medical services and head athletic trainer, Stan Conte, has been refining a mathematical formula designed to help the Dodgers avoid players who spend their days in the training room and not on the ball field. A player on the injured reserve list is expensive in terms of the missed opportunity from their play and the extra cost to replace them. Conte has compiled 15 years of data plus medical records to test his hypothesis that predict the chances a player will be injured and why.
Dozens, possibly hundreds, of baseball debates such as the rangiest shortstop or the quickest center fielder will soon shift from argument to mathematical equations. A new camera and associated software in its final testing phases will record the precise speed and location of the ball and every player on the field. It will dynamically digitize everything allowing a treasure trove of new statistics to analyze. Which right fielders charge the ball quickest and then throw the ball the hardest and most accurately? Guesswork and opinion will give way to fact-based measures. You can read about this at http://www.nytimes.com/2009/07/10/sports/baseball/10cameras.html?_r=2&em . Paste it into your Internet service.
Create a culture for metrics
Here is some easy math:
baseball + analytics = improved performance
business + business analytics = improved business performance
If you cannot measure it, you cannot manage it. With metrics, you can improve it. Create a culture for metrics in your organization. It will provide a competitive edge.
About the Author
Gary Cokins is the global product marketing manager for Performance Management solutions at SAS, a market leader in data management, business intelligence and analytical software. He is an internationally recognized expert, speaker and author on advanced cost management and performance improvement systems. He is the author of five books, An ABC Manager's Primer, Activity-Based Cost Management: Making It Work, Activity-Based Cost Management: An Executive's Guide (Wiley), Activity-Based Cost Management in Government and his latest work, Performance Management: Finding the Missing Pieces to Close the Intelligence Gap (Wiley). You can contact him at firstname.lastname@example.org. For more of Cokins' unique look at the world, visit his blog at http://blogs.sas.com/content/cokins.