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May 22, 2018

Want an All-Star Team? Try the Moneyball Approach to Hiring

moneyball hiring

Too many recruiters are still using irrelevant hiring criteria. Here’s a quick lesson in baseball to help you up your game.

Ever heard of sabermetrics?

Unless you’re a baseball fan, we’re betting that’s a nooope. But it’s actually pretty cool. Sabermetrics is the empirical analysis of baseball. It’s how sports analysts and scouts accurately predict player performance. So, what’s that got to do with HR?

In case you haven’t seen the multi-Academy Award nominated movie (or read the book for those of us who still do that sort of thing), Moneyball is the story of former Oakland A’s manager Billy Beane, who literally went against all odds by scrapping the traditional scouting approach in favor of a more sophisticated, data-driven model.

Now you’re with me, right? 💡

The idea of using objective data to hire top-notch candidates isn’t new. In fact, when Moneyball hit theatres in 2011, many HR experts were quick to note the parallels. (As in, “Hallelujah! Finally, someone gets it!”)

But unlike baseball, HR moves fast and in today’s talent-driven hiring economy, we think it’s high time for a refresher on why this data stuff matters.

Judgmental hiring still happens way too often

Truth is, many recruiters are still using overly subjective or downright irrelevant criteria to select and hire candidates. But choosing a candidate because they’re a “perfect cultural fit” is the business equivalent of choosing a player based on your vision of how they’ll look on a baseball card.

Look, we get it. Putting together your team is hard — really hard.

Any exercise involving judging, measuring up and selecting one human being over others is always going to be fraught with nuance and grey area. And as humans, we tend to ignore things we don’t understand. Rather than open Pandora’s box, we fall back on the commonly accepted (but still totally false) “tried and true” hiring criteria like education and GPA.

But as Brad Pitt’s character Billy Beane says, how can you understand the problem if “you’re not even looking at the problem”?

Most hiring data isn’t all it’s cracked up to be

If you actually look at the data on high-performers within companies, you’d probably be shocked at what you find. Here’s a quick story from one of our biggest HR crushes, Josh Bersin (sorry, Brad!) that pretty much says it all:

“One of the companies I talked to told me that despite the fact that they were really kind of a blue blood company and they loved to hire people from east coast schools that had great Grade Point Averages, they did a lot of analysis and they found out that actually there was no relationship whatsoever with where they went to school, but in fact, the highest predictor of sales performance for the first two years within this company was whether or not they [the candidate] had typos on their resume.”

We’ve got bad news for you, grammar pedants. 😞

Turns out the ol’ Oxford comma isn’t the status indicator you thought it was. And that’s not all. In her mission to “make technical hiring suck less”, former-hirer-turned-fair-interview evangelist, Aline Lerner analyzed over 8,000 recruitment messages and found that typos and grammatical errors were a far better indicator of engineering talent than the prestige of their computer science degree, their GPA or even their “extra credit” side projects.

So while you’re weeding out resumes based on poor apostrophe placement, your competitors are doing their homework and scooping up top sales and engineering talent who have bigger things to worry about.

Objective hiring data is a friend and ally

It’s one thing to read posts like this one and say you see the value of a balanced and objective hiring process. It’s another thing altogether to actually use science-based systems to guide your hiring process.

After all, hiring is a tricky game to play. What if your savvy new “objective” process ends up violating your diversity and inclusion policy as in the cautionary tale of companies like Palantir Technologies?

Talk about a recruiter’s worst nightmare. 🙈

But experts like Frida Polli say we need to lean in. Frida is the entrepreneur and former neuroscientist behind candidate assessment tool, Pymetrics. In her 2013 article for Forbes, she makes a clear case for data collection as an pro-inclusion power tool:

“Does it limit diversity to use an objective test that’s blind to gender and race or to go on gut decisions made from resumes and interviews? Instead of limiting diversity, one could argue that data will lead to greater gender, ethnic and socioeconomic diversity. Think of it like The Voice (or blind auditions) for corporate hiring.”

It’s time to use data — not judgment — to make better hiring decisions.

Create a winning game plan

A modern recruitment system will help you make sense of your hiring data. In Breezy, we do this through easy-on-the-eyes analytics, seamless integration with your favorite BI tool and automatic EEOC and OFCCP data tracking.

For Billy Beane and the Oakland A’s, the penny-drop moment was realizing that both the subjective and the objective data baseball recruiters relied on was dead wrong — from batting average to the attractiveness of a player’s girlfriend, ALL the data was off.

Recruiters were so focused on who had a great swing or the perfect “baseball body”, they completely forgot the end goal. But in the words of Jonah Hill’s character Peter Brand, “Your goal shouldn’t be to buy players, your goal should be to buy wins.”