After reading Michael Lewis’ Moneyball, I asked myself: why hasn’t this data-driven approach to the evaluation and recruitment of talent been embraced by more teams and organizations outside of professional sports? Why, after all of these years and with the very tangible success of professional sports to look to as an example, why are we still evaluating and recruiting talent like we always have?
After all, when you cut through the sound and the fury of Lewis’ tale, the innovation described in Moneyball is pretty straightforward. Billy Bean and Paul DePodesta of the Oakland Athletics use data to identify players who are undervalued by other teams and then sign them to contracts at a bargain price. Essentially, they get more for less by exploiting information the other teams ignore. It’s smart, but it’s also a tactic that every bargain hunter, thrifter and value investor understands. Because the core idea described in Moneyball is so straightforward and has been so widely celebrated, you would think (or, at least, I would) the data-driven approach to the evaluation and recruitment of talent described in Moneyball (or something approximating it) would have swept through all other industries by now.
Instead, it seems that most teams and organizations rely on recruitment practices that are probably older than baseball. You know the drill: after a largely arbitrary sorting process based on self-reported data points (i.e. a resume is pulled out of a hat based on a crappy keyword search or because a friend-of-a-friend recommends that it be pulled), the evaluation of a potential hire boils down to a highly subjective gut-check, which may or may not be based on an assessment of the candidate’s skills in highly artificial circumstances. A few reference checks later — which everyone agrees are useless — and, blammo, a new hire is being onboarded. If a professional sports team recruited like this, it would be out of business in no time. How is it possible that so many teams and organizations continue to recruit in this essentially arbitrary fashion?
Overlooking the rhetorical nature of my question, you might reply, “well, probably because most teams, organizations and industries don’t have access to the kind of dataset baseball has. Baseball has always been kind of nutty for numbers.” To which, I might reply, great point, Dave, but there is no necessary reason why a baseball-like dataset couldn’t be developed and maintained by, say, a professional association. Isn’t the market supposed to identify opportunities like this and fill them? Potential employers, it is fair to say, would probably pay oodles of money to access this kind of data, if it led to better and less costly hires. Moreover, I would quickly add, not giving you a chance to get a word in edgewise, because that’s how I roll, once someone is hired, a team or organization can create and maintain as much data about the new hire and their performance as they would like. So, if some hungry-for-success team or organization wants to evaluate a new hire based on their contribution to the success of the team or organization, generating the right kind of data should be a straightforward exercise once the person is onboarded.
Instead, much like the recruitment process itself, the evaluation of new hires seem to be largely a matter of feel. If a new hire “fits” into their new team and seems to contribute, the recruitment process is normally judged a success, whether or not the person measurably contributes to the success of the team or organization. To be fair, group harmony and team cohesion is always going to play a role in any team’s success. However, group harmony and team cohesion are very often a by-product of team work rather than a catalyst for it. Whether or not a person “fits” is probably irrelevant, so long as they make some effort to cooperate and work well with others. Proximity and time will take care of the rest.
Before you interrupt me with another objection that I already have a clever reply to, it was probably around this point in my thinking and writing that the penny dropped. Duh, Sterling, of course, most organizations and industries hire based on “feel”, where “feel” more or less translates into, “yep, gut sure says that they’re like me.” We humans are tribal. From the very outset of our lives, we tend to form relationships and social groups based on physical proximity and physical similarities. Why would it be dramatically different for the workforce? Well, Sterling, I guess I was assuming that competition and/or the desire to achieve our aims would have nudged us to adopt more rational, coherent and less arbitrary approaches to building teams and organizations. Whether an organization is for-profit or not-for-profit, it makes much more sense to recruit people who measurably contribute to the achievement of the organization’s aims rather than people who just happen to look and talk like the friends-of-friends we have in common.
Think about it, if the jocks — of all people — have figured this one out, why hasn’t anyone else?
Then, it was around this point that another penny dropped for me. Most people agree that Michael Lewis’ version of the events in Moneyball “torques” the facts for the sake of a more compelling story. In particular, it seems likely that there was far less conflict and debate about the data-driven approach Bean and DePodesta championed. Strictly-speaking, once a certain caricature of scouts and scouting is set aside, the difference between player evaluation and acquisition as it was traditionally done in baseball and the approach described in Moneyball is one of degree rather than kind. Moreover, by the time that Beane and DePodesta had turned to data to drive their player acquisitions, amateur data aficionados had already been using data to dissect and criticize professional baseball’s approach to player evaluation and acquisition for some time. The notion that data could lead to better recruitment practices was already well and truly in the air.
It’s also important to remember that Bean and DePodesta were evaluating and recruiting players who had already been through a very long and very difficult vetting process. To be among the players who are even on the radar of being considered for a spot on a professional baseball team, a lot of people in the baseball community would have already vouched for that player in some way or the other. It’s not like the Athletics were using data to recruit hockey goalies to be catchers or signing Tim from the mailroom. If a team is trying to decide between signing this guy and that guy, and everyone already agrees that both of them are part of the very exclusive club known as professional baseball, why wouldn’t you roll the dice and pick the cheaper guy if the data also seems to predict he would do fine. Shorn of Lewis’ drama, the Athletics faced a pretty simple choice. On the one hand, they could continue evaluating and recruiting talent as they always had and expect the same middling results or, on the other, they could take a chance on a newish approach broadly recognized as having some merit, generate results no worse than they might otherwise expect, and save money while doing it. Really, when you think about it, it’s a no-brainer, but, “the not-so-remarkable tale of safely entrenched insiders making an even safer bet that works out better than expected” doesn’t make for compelling dust jacket copy.
With all of that throat-clearing now well in hand (uh, gross), the answer to the question I started with is this, I think: teams and organizations outside of professional sports haven’t yet broadly adopted a data-driven approach to the evaluation and recruitment of talent because, all things considered, the age-old approaches work well-enough; as a result, no well-established insiders have felt compelled to try something new. On the one hand, successful organizations tend to attract a lot people who have already been vetted in some fashion. Randomly picking, more or less, among those people who present themselves for selection is probably a safe bet and, if random selection is a safe bet, why not also pick people “like me,” if it will make you and everyone else on the team feel more comfortable with the new hire. On the other hand, struggling organizations tend to cut employees rather than than make new hires and, you can be sure, any hires they do make are going to be on the safe and familiar side. In other words, even after very many years of working together in groups to achieve different aims, it seems that we humans haven’t confronted any situation that would compel us to change how we recruit people or how we evaluate their contributions to our efforts. And, if it hasn’t happened yet, don’t hold your breath! Businesses fail every day and entire industries have collapsed over the years and yet these very negative consequences have not driven business or industry insiders to fundamentally and systematically rethink how they evaluate and acquire talent. If it hasn’t happened yet, I doubt it it will happen anytime soon.
Now, if you are like me, at this point, you might be somewhat disheartened to realize that organizations build their teams using methods that wouldn’t look out of place on the schoolyard (i.e. pick that kid, he dresses like us!). However, if you are a normal human being, you are probably actually thinking, “Are you serious?” Did you really only just figure out that hiring decisions are primarily an exercise of “like” hiring “like”?” Well, sort of. I have always understood that humans have a habit of grouping together based on superficial similarities and excluding those who are superficially different, but I have always thought of it as a bad habit, which would eventually be broken, both at the individual and group level, either consciously as people and societies matured or unconsciously through something like competition. What has dawned on me (thanks to Moneyball and baseball!?) is that the human tendency to socialize, build teams and act collectively by looking for and finding people “like us” is so fundamental that nothing will ever compel us to change, other than a true evolutionary shift in our DNA, which, strictly-speaking, is just a fancy way of saying, “if people who embrace difference reproduce more than all those other people who prefer homogeneity.” It’s “we like us” and “different like them” all the way down.
Moreover, on a personal level, it is also dawning on me that whatever I have accomplished in my life is probably best understood as being a consequence of my similarities to others rather than my differences. I’m not a beautiful unique snowflake; I’m a me-too drug. And, yes, while I am one hundred per cent talking about social privilege, I am also driving at something that runs deeper. Returning again to evolution (which probably should be the subordinate clause that starts every discussion about human nature), in my experience, evolution is often characterized as a triumph of difference because it is a heritable difference in phenotype that leads to a reproductive advantage that, over many generations, leads to a new breeding population. Hurray for difference — so long as you overlook the fact that the difference is one tiny bit in a whole lot of sameness. Without the sameness, the little bit of difference wouldn’t ever take hold in a breeding population. To put it bluntly, if you are too different, your difference ain’t being passed along to anyone because you won’t get the opportunity to reproduce and, if you are really different, the breeding might not even work. In other words, what makes you and me human are the ways in which we are the same; insofar as we aspire to be unique, it it only possible because we are like others — and not in spite of it.
And that is the moral of a completely different after-school special than the ones I watched growing up.