Moneyball is getting to be the new buzzword in recruiting. We’re supposedly on the cusp of a data-driven revolution in hiring. And it seems one is sorely needed, judging by the state of hiring practices today.
When NASA was just getting started many of the engineers that were hired were chosen only on the basis of their resume and cover letters. That was the norm for many jobs up until the 1950s. Interviews were not common for jobs where the candidates were located far from the worksite — the cost of travel, and even long-distance calls, made them unaffordable. Then employers started using all types of assessments, which would suggest that hiring must have improved dramatically over the 50 years that have elapsed.
One would be wrong to reach that conclusion.
A recent survey by the Corporate Executive Board found that almost three-quarters of hiring managers reported that they hired candidates primarily because they had personalities similar to their own. Another study of major investment banks, consultancies, and law firms found that many candidates are hired because they share leisure pursuits with the hiring manager! So much for the advice of leaving such items off a resume. Many hiring managers described their hiring practices to be similar to dating. No wonder eHarmony has gotten into the hiring business.
How could we have reached this level of dysfunction? The resume and cover letters that NASA relied on to hire employees produced people who built a rocket to get to the moon, using little more than slide rules. And now we’re hiring people using questions made up on the fly? Blame goes to everything from EEO laws to increased job-switching by employees that has made it less economical for employers to test thoroughly. Hence the desire for Moneyball — the new, new thing in the never-ending quest for silver-bullet solutions in hiring.
Why Moneyball?
As far as baseball is concerned, the concept is simple: use player performance stats to make hiring decisions. And in business we love using sports analogies. We also need new content for HR conferences — social media is getting to be passe. Enter Moneyball — it’s based on a true story, was the subject of a movie starring Brad Pitt, and has a David and Goliath theme. A topic with such roots is good for at least 5-6 years of webinars, presentations, and panel discussions featuring unrepresentative case studies peppered with unverifiable statistics. Makes for great conversations though. For once, the VP of HR and CFO can talk the same language. What’s not to like? Expect recruiting departments to start looking for people like the character played by Jonah Hill in the movie.
But in all seriousness, Moneyball applied to recruiting can make for huge improvements in hiring. The challenge is finding the data needed for this to work. In baseball everything is measured and recorded, and it’s near impossible to fake anything. Not so in hiring for other jobs, where data on candidates is either nonexistent or sparsely available, or getting it is not easy.
Consider an example of how Gild evaluates developers. The company’s algorithms scour the Web for open-source code, and for the coders who wrote it. They evaluate the code for factors like simplicity and documentation and the frequency with which it’s adopted by other programmers. They also look at questions and answers on forums like Stack Overflow for how popular a given coder’s advice is. Gild then scores programmers who haven’t written open-source code by analyzing clues embedded in their online histories.
Achievable Dream or Pie in the Sky
Big data is supposed to solve the problem of Moneyball and talent. Maybe. One can only use data — big or small — if it exists. What companies like Gild do may work for hiring programmers, but what about candidates for HR, marketing, accounting, and other categories of jobs where such data simply does not exist?
But that’s not to say it isn’t possible. Looking at candidates online profiles, posts in professional forums, quality of education using assessments like the CLA (Collegiate Learning Assessment) may allow for at least some hiring practices to become like Moneyball. However, this is not likely to ever be a complete solution; candidates lacking an online history are not incompetent. Also, since it’s possible to have one’s online history scrubbed, it’s also possible to have one created that would make the whole idea of Moneyball worthless.
We’re always looking for simple solutions. Ideally, we’d like a method by which any candidate can be scored like a baseball player. But in the entire history of major league baseball there have only been about 16,000 players and only about 750 active today. These numbers are miniscule compared to other jobs. Tracking performance data on that few is easy. As scoring algorithms become more sophisticated we’ll be able to evaluate candidates better, and the concepts of Moneyball may be applicable to aspects of recruiting — like sourcing, but expecting it to apply to all jobs is likely a pie in the sky.
image from http://www.imdb.com/title/tt1210166/