News & Ideas

Moneyball, Wildfires, and a Bottle of Beer

Three quite disparate things but together they can help us better understand the difference between "using data" and being "data-driven."

Moneyball, wildfires, and a glass of beer… three things that normally don’t go together but that all relate to the natural human flaws in our assessment of things, whether the future performance of a baseball player, the risk of an electrical system, or the taste of a beer.

Dan Ariely, in his book Predictably Irrational, talks about an experiment where people were given two beers to taste—one a normal Budweiser, the other a Budweiser with a few drops of balsamic vinegar mixed into it. When the experiment was blind taste tested, participants consistently preferred the beer with vinegar.

However, if told in advance that they would be drinking beer with vinegar in it, participants rated the vinegar beer significantly lower.

The bias of having a negative idea about what a beer with vinegar in it would taste like was so strong that it convinced people to not like a beer they otherwise would have preferred.


The data from blind taste tests clearly shows that when people have an initial opinion about something, it acts as an anchor, and that anchor skews our opinion so strongly that we analyze things differently (and in some cases even taste things differently).

Similarly, in the real-life story turned book and movie, Moneyball, Billy Bean and the Oakland A’s identified a flaw in the logic of baseball scouts and executives who were incorrectly assessing the value of baseball players. It wasn’t that the traditional scouts were against data, but they were “using data” in the wrong way.

They believed they were using home runs, batting average, and RBIs to make an informed decision, but in reality, they were subconsciously making decisions based on intuition and then “using data” to confirm what they already believed. What Billy Beane and Paul DePodesta did in Moneyball was to stop incorrectly “using data” and start using an unbiased, data-driven approach to player analysis.

“Using data” and being “data-driven” are not the same.

This brings us to the recent wildfires in California. Utilities are facing a growing challenge: how do they prepare the grid for large, infrequent events? It wasn’t that California utilities stopped investing in the grid – to the contrary they were investing significantly, both in infrastructure and technology, and, just like the scouts in Moneyball, were “using data” to show the results they wanted: all-time best performances in grid reliability year after year after year.

But, as we now know, there is a big difference between “using data” wrapped in traditional human biases and making truly data-driven decisions. The challenge is that utilities historically have focused on preventing small, frequent events— reducing daily outages, preparing for annual storm seasons, etc., and they measured performance against how they did preparing the grid for these sorts of events.

Metrics like CMI, SAIDI, and SAIFI perfectly track performance relative to small, frequent events, but utilities across the country, not just in California, are caught in the middle of a paradigm shift. The East Coast experienced their own challenges with Superstorm Sandy and other large weather events that took down the power grid for extended periods of time. Historic metrics for tracking small, frequent events provide little insight into whether utilities are optimizing investment decisions as they work to reduce risk and improving resiliency for these sorts of large, infrequent events.

One of TROVE's big focus areas has been in helping to lead change and letting the data drive the decision making. In the same way that the Moneyball-approach has changed the way baseball teams evaluate talent, data-driven decision making helps utilities measure risk and evaluate capital improvements.

What do you think? Agree or disagree? Send me an email ( on areas where you think data-driven decision making will or won’t impact the electric utility business.

Tom Martin is TROVE’s Managing Director of Product, Energy & Utilities helping utilities become more data-driven and cost-effective in their decision making. Prior to TROVE, Tom led the Emerging Grid Technology at Pacific Gas & Electric leading pilot projects for new technology and analytics in support for PG&E’s Electric Operations as PG&E looked to reduce operational costs, improve safety, and increase reliability in support of grid modernization.


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