The term “moneyball” was first coined by financial journalist Michael Lewis in his 2003 book, “Moneyball: The Art of Winning an Unfair Game,” later made into a 2011 film starring Brad Pitt. The story is about a major league baseball team’s use of data analysis to identify undervalued players with inexpensive contracts in order to build a winning team at minimal cost.
The story goes: Before the 2002 Major League Baseball season began, the Oakland Athletics team had to let go of a handful of top players. As the poorest team in the league, they needed to rebuild their roster—on the cheap. Then-owner, Billy Beane, lacked the deep pockets of teams like the Yankees but was able to build a roster of star performers without busting his budget on top draft picks. How’d he do it? He dug through years of numbers and data analysis to look for trends, statistical patterns, and indicators of winning potential.
“I would describe the premise of ‘moneyball’ as coming up with evaluation systems that more precisely predict whether a baseball player will succeed in the major leagues. A premise that suggests statistical data is just as important as a scout’s estimation of a player’s ability,” says David Finocchio, CEO and co-founder of sports analytics media company Bleacher Report in an interview with Visier.
“Before ‘Moneyball’ the book there was a bias in baseball toward players that had particular athletic and physical measurements,” he continues. “There were players that were slipping through the cracks because they didn’t have the physical attributes that scouts were looking for.”
Buying power vs. baseball stats
To understand how the moneyball strategy changed the game, you need to first understand how the Major League Baseball player draft, held each June, operates. In order to determine who gets drafted, general managers, scouts, and professional consultants evaluate players and make selections. There are fifty rounds of selections by all thirty teams. The earlier in the draft pick rounds a draftee appears, the more valuable he is perceived to be.
Historically a player’s value wasn’t evaluated based on performance statistics. Instead, scouts based their decisions on speed, quickness, arm strength, hitting ability, and mental toughness—all measured using a series of tests. In fact, scouts were specifically told not to take into account the actual performance of the athlete but instead to look at how they perform specifically on these tests.
Beane flipped the script and completely transformed the game when he took over the club in 1997. Instead of evaluating players on their physical attributes, he focused entirely on results, answering questions like whether the player can hit or get on base. Beane’s approach expanded sabermetrics, a strategy established by Bill James in 1980 which attempts to quantify a baseball players’ performance based on objective statistical measurements rather than on the hunches of scouts.
A data-driven approach to selecting star players
Adopting this moneyball method meant widening the scope of attributes that were valued by baseball managers. It also meant increasing the general understanding that technical improvements in the collection of data could have a staggering impact on how players were evaluated. It arrived just in time for a revolution in statistics and artificial intelligence and it completely changed the game. As the data-based method spread to other sports such as basketball and football, it went far beyond the parameters traditionally used to define talent.
These days it is hard to find a major league sports team that doesn’t use some form of data analytics when evaluating players. Finocchio says the National Basketball Association (NBA) has been completely transformed by sports analytics instead of relying only the intuition of scouts.
Moneyballing was originally conceived as an underdog strategy that allowed sports teams with a smaller budget to still bring in what turned out to be star performers. While it can still help sports teams gain a competitive advantage, its use has become commonplace outside of sports as well. Moneyball identified a way of identifying talent that wasn’t just dependent on money.
Moneyball your recruiting efforts
If you’re a technology company in Silicon Valley that is having trouble recruiting engineers because you are losing them to Google, Apple, and Facebook, a moneyball-style strategy can help. The same is true of a rural hospital in need of nurses that is losing them to more prestigious institutions. By better understanding a potential employee’s needs and wants, hiring managers may also be able to offer them something even more tempting than a high salary alone.
Winning the talent game could mean looking for candidates who didn’t attend Ivy League schools or those that have less experience in the field but who display some characteristic that sets them apart. With some training, there might be an opportunity to shape them into the perfect person to fill a specific role. Don’t overlook the fact that finding the candidate is merely the first step in the recruiting process.
It starts by recognizing that the current ways in which you are evaluating potential employees may be out of date, not inclusive enough, or too narrow. The right analytics can help you segment job seekers into categories that will help you determine whether a candidate has potential as high-performing individual contributors, or are likely to be stars, promoted to manager or senior executive-level positions.
Armed with better data, you can make better decisions about where someone might slot into your org chart. Not only does moneyballing give you a better understanding of your potential employee, it can allow you to know exactly how they performed in a particular role and can provide insights into how the decisions they made over their career trajectory can affect their candidacy.
Finocchio says applying a strategy like moneyballing is a good way to confront whatever biases you may have. Sure, it’s good to know where the applicant went to school, and where else they’ve been working but (like a home run hitter’s batting average) that kind of data should be only the start of your evaluation.
“If you are overly biased, the way Facebook and Google used to be, and just go after the people with the top degrees, you miss a lot,” he states. “For example, in the NFL, you might want to look for people with other interests, people that are planning for life after football. I prefer people with a positive attitude who show resilience—I’ll take that any day over someone who went to Harvard Business School. Resumes can tell you a lot but they aren’t everything.”
Rethink traditional hiring approaches
Searching for candidates may still start with a keyword search on LinkedIn but a data-driven hiring approach allows you to learn much more about the person. Data-driven hiring can be used to find top talent because it uses an analytical approach that considers what differentiates potential high performers from average ones by removing the subjectivity out of hiring.
It’s also a way to find employees that are a better cultural fit for your company. For example, if you are looking for risk takers who will shake things up, you won’t find that via a keyword search. In the past, you might have easily dismissed a person who had many jobs as a job hopper. Digging deeper can help you better understand that person’s motivations and turn a negative into a positive. For instance, someone who has had shorter job tenures and moved from one industry to another might be seen as less risk adverse and more daring—not just as a flighty “job hopper.”
A moneyball method allows you to widen your net while at the same time considering individual characteristics that might not be on your radar. For example, a person’s hobbies can tell you a lot about how effective the candidate will be in your organization. Do they pursue individual activities like running or playing the piano or do they play sports where being a good team player is key? That could mean they are more collaborative or easier to get along with.
Reframe how you evaluate a candidate
Moneyballing is a different way of evaluating talent, that helps to make sure you are getting a more accurate and well-rounded sense of potential employees. Is using data better than using intuition and more traditional approaches to hiring? It doesn’t have to be one way or the other, there’s an in-between answer, says Finocchio.
“I think the right answer is that you should be getting signals from different places—you want information that has worked in the past, but you also want to judge people’s emotional intelligence, their EQ, as well as their IQ,” comments Finocchio. “You have to take both into account. If you are skewing too much on the analytics side, you can miss things that are going to make them a good performer. I hope we can get back to some of that middle ground.”
Having data takes the emotion out of judging a potential employee but even Finocchio, who as a serial entrepreneur has hired hundreds of people, recognizes its limitations.
When ESPN purchased Bleacher Report 15 years ago, it forced layoffs that were painful because some of the older employees, who were perceived to be too expensive, were let go. Finocchio felt they had contributed a lot to the culture and deserved to be retained. Perhaps a data-driven approach to employment would have seen their value in ways that a bean counter couldn’t.
5 tips for moneyballing your talent acquisition strategy in 2022 and beyond
Don’t rely on hiring hunches alone.
Use data to complement intuition and personal chemistry. Intangible qualities of candidates help tell a more comprehensive story.
Survey the market to set compensation.
Looking for recent trends in compensation for specific roles, and taking into account the total package on offer will pay off more than relying on past salary alone.
Add value to your hiring budget.
Today’s workforce places a high value on work/life balance, autonomy, and agency—as well as salary.
If a candidate is lacking in years of experience—see if they’ve been promoted often; if they’re applying for a slightly different role, study their output for signs they could be a good fit.
Update your assumptions.
Attributes that have been traditionally viewed as negative (moving jobs often), could be indicators of a positive in the new world of work.
Today’s workforce is less monolithic than ever. It’s hard to identify the individual characteristics that can make someone successful, but harnessing the power of big data can make decisions much less risky.
People’s motivations have also changed in the workplace and the moneyball strategy can help address that, too. Studies show that now more than ever people want to work for companies that respect their preferences for work/life balance and that provide them with more autonomy. To do that you need to understand them better, and to understand them better you need a data-driven approach to stacking your roster.
About the author: Lee Sherman
Lee Sherman is a data-driven journalist with 30 years experience covering technology, personal finance, music, and fashion. He prides himself on his ability to make complex topics more relatable. When not writing about the future of work, he enjoys film photography, playing synthesizers, and traveling.
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