What can a regulated electric utility learn from Moneyball?
When Michael Lewis’ “Moneyball: The Art of Winning an Unfair Game” was published in 2003, it became a must-read for a small group of analytically-oriented baseball fans. The book tells the story of how the Oakland A’s, a small-market, low-budget team led by General Manager Billy Beane and Assistant General Manager Paul DePodesta turned the baseball world upside down by taking a team with one of the lowest total payrolls in baseball and transforming it into one of the winningest teams in the Major Leagues.
Word spread quickly about the book, and it became apparent that it wasn’t just a book about baseball. Within a few years the term Moneyball became an eponym for analytically-driven decision making. A mobile phone application that helps golfers keep statistics on their round is advertised as “Moneyball for your golf game.” Marketers talk about “Moneyball Marketers.” CIOs use Moneyball approaches to recruit technical talent. A book about applying analytics to political campaigns is described as “Moneyball for Politics.”
Baseball generates an enormous and ever-growing amount of data that lends itself to analysis. Paul DePodesta (now with the New York Mets) has said that the last day of the season is his favorite day because that means he has 4-5 months to analyze the data from the season and refine decision-making models. For electric utilities, with more and more data coming in from a myriad of devices and sensors, a season’s worth of data arrives every day…and analysis needs to take place in near real time, and in hours instead of months.
Principles and best practices from Moneyball as well as other books such as Competing on Analytics are well suited for decision makers in unregulated utilities. But what lessons can a regulated utility learn from Moneyball?
1. Pick the right things to measure. Oakland de-emphasized traditional measurements of baseball performance such as batting average and runs batted in and started focusing on new ones such on-base percentage and expected run value.
For utilities, the process starts with understanding what contributes to better performance whether the term performance relates to reliability, safety, operational efficiency, cost, or revenue. This is often a challenge for several reasons. The value of reliability and safety, while key components of the mission statement, is difficult to measure and justify. Regulators tend to focus on these issues only after major catastrophes. Cost and revenue are also complicated because tariffs are often dictated by legislative and regulatory decisions due to the monopolistic nature of the industry.
2. Ask Peter Drucker’s “naïve question”: “If we hadn’t always done it this way, is this how we would do it?” Oakland assembled a front office staff that challenged the long held beliefs and practices of baseball scouts. As Michael Lewis put it: “The evaluation of young baseball players had been taken out of the hands of old baseball men and placed in the hands of people who had what Billy valued most (and what Billy didn’t have) - a degree in something other than baseball.”
Given the aging workforce at most utilities with large percentages of skilled and experienced workers eligible for retirement in the next five years, this same question may be forced upon some organizations. At the same time, disruptive technologies such as solar, batteries, fuel cells and microgrids are driving new investments into the energy sector and are beginning to introduce competitive forces into the industry. Utilities are questioning how they can leverage such technologies–especially given regulatory constraints - and whether the new and the old can be safely merged to improve reliability and costs.
3. Learn from other industries. Many of the key concepts successfully employed by Oakland were borrowed directly from other industries. A baseball statistics company founded by two former Wall Street traders provided insights to a young Paul DePodesta when they showed how the concept of financial derivatives could be applied to the evaluation of baseball players. When reduced to its essence, the book is essentially the story of how Oakland successfully arbitraged the mispricing of baseball players.
For utilities, financial services and telecommunications are natural sources of inspiration but less obvious ones such as healthcare, life sciences, hospitality & travel, and manufacturing should not be ruled out. For example, like the medical industry using devices that communicate the health of patients, utilities are leveraging the falling prices of sensors to install smart meters and other devices to monitor the condition and dynamic capabilities of assets to determine optimum operating levels for safe and reliable service, when replacement is necessary, and whether an outage is imminent. San Diego Gas & Electric leverages new sensors and telecommunications technology to continuously measure the health of large, expensive transformers thereby avoiding outages and pre-mature loss of multi-million dollar equipment in addition to deferring the replacement of older equipment that is still going strong.
4. Focus on the process, not just the outcome. Like all sports, success in baseball is measured by outcomes, but Paul DePodesta warns that too many people make decisions based on outcomes rather than process. The author relates a story about watching an Oakland game from the video room where he says that he realized that not only was he watching the game differently but that he was watching a different game. The analysts in the Oakland video room focused on process and to them each at bat was a game in itself with constantly shifting odds depending on the count. With a Moneyball mindset, a good process with a bad outcome is written off as bad luck just as a bad process with a good outcome is attributed to (undeserved) good luck. Bad process and a bad outcome is poetic justice while good process and good outcome is the ideal. The adoption of statistical techniques drives towards high probability outcomes that optimize desired outcomes while recognizing failures will occur with some minimal frequency.
Utilities use rules of thumb based on “profiles” taking into account the size and location of each home to estimate how much energy customers consume on a circuit of thousands of homes, when the energy is used, and what the maximum usage is. These profiles have been finely tuned over the decades based on the reliability results they produce. However, by modeling the rich data now being provided by digital meters and other sensors, work that would have been performed to add additional capacity can be reprioritized in other areas that may have been incorrectly profiled to have remaining capacity, providing more efficient service to customers
One of the best and most thorough reviews of Moneyball came from (of all places) the Michigan Law Review—further proof that although Moneyball appears to be a book about baseball, it’s more than that. The author concludes his review as follows:
“We suspect that countless areas of enterprise, both private and governmental, would benefit from their own Billy Beanes and Paul DePodestas, challenging widespread intuitions, or what "everyone knows," with statistical information about what works and what does not, and with performance measures that more accurately reflect the true contribution to organizational success.” (Read the full review here.)
So as the utility industry enters a new “smart grid” era that is defined by the effective analysis and use of increased amounts of data, we must continue to challenge the status quo by asking simple questions that lead to great benefit for our customers and our world. What are we measuring? What processes and assumptions need to be questioned in our organizations? Are we looking outside the industry for ideas and best practices? Are we focused on processes and not just outcomes? Are we playing Moneyball?
AUTHOR: Lee Krevat is the Director, Smart Grid for Sempra Energy's San Diego Gas & Electric (SDG&E).
CO-AUTHOR: Tim Fairchild is the Global Utilities Practice Director at SAS.