Baseball + Learning + Analytics = Improved Performance
How does an organization create a culture of metrics, learning and analytics?
With baseball season well underway, an 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 (and organizations) 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 (who is now with the Chicago Cubs), 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’ve 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’ve 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’m proud that this computer program has been inducted in the National Baseball Hall of Fame & Museum as the oldest computer baseball game code. This was obviously not as big an achievement as the Wright brothers or Charles Lindbergh, but it’s something my grandsons marvel at.
Analytics to Improve Business Results
What does this have to do with CLOs and enterprise and corporate performance management (EPM/CPM) methods? A lot.
I’ve loudly advocated that EPM/CPM is the integration of dozens of methods, like strategy maps, key performance indicator (KPI) scorecards, customer profitability analysis, risk management and process improvement. But I’ve insisted that each method requires imbedded analytics of all flavors, and especially predictive analytics needed.
Predictive analytics anticipate the future with reduced uncertainty to enable being proactive with decisions and not reactive after the fact, when it may be too late.
A practical example is analytics imbedded in strategy maps; the visualization of how a leadership team’s training supports their organization’s strategy.
Statistical correlation analysis can be applied among influencing and influenced KPIs. Organizations struggle with identifying what is most relevant to measure and then determining what the best target is for that measure.
Software from business analytics vendors can now calculate the strength or weakness of causal relationships among the KPIs and display them visually, such as with the thickness or colors of the connecting arrows in a strategy map. This can validate the quality of KPIs selected; ultimately creating a scientific laboratory for strategy management.
Returning to baseball, an evolving application of business analytics relates to dynamic home stadium ticket prices to optimize revenues.
The San Francisco Giants experiment 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.[i]
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.[ii]
Greater statistical analysis is yet to come. The New York Times has reported on new technology that could shift previously hard-to-quantify baseball debates such as the rangiest shortstop or the quickest center fielder from the realm of 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.[iii]
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.
Create a Culture for Metrics
Here is some easy math:
baseball + analytics = improved performance
learning + learning analytics = improved business performance
If you can’t measure it, you can’t manage it. With metrics, you can improve it. Create a culture for metrics in your organization. It will provide a competitive edge.
[i] Ken Belson, “Baseball Tickets Too Much? Check Back Tomorrow,” The New York Times: May 18, 2009. http://www.nytimes.com/2009/05/18/sports/baseball/18pricing.html
[ii] Michael S. Schmidt, “Turning the Trainer’s Table into an Actuarial Table,” The New York Times: July 8, 2009. http://www.nytimes.com/2009/07/08/sports/baseball/08injuries.html?scp=1&sq=Actuarial&st=cse
[iii] Alan Schwarz, “Digital Eyes Will Chart Baseball’s Unseen Skills,” The New York Times: July 9, 2009. http://www.nytimes.com/2009/07/10/sports/baseball/10cameras.html?_r=1&em