Breaking News: (July 16, 2036) The national Comprehensive and Reliable Assessment of Performance (CRAP) database reached its goal of 100 percent coverage with the last employer — Roto Rooter of Northern Idaho — getting connected to share employee performance data. Employers nationwide now have a central resource to evaluate candidates for jobs, using the concept of Moneyball that was developed in the late 20th century. The database, established by the Dream On Act, is administered by the BUFFOONS (Bureau of Unreliable and Freely Flexible Or Objectionable Numbers and Statistics) at the Department of Labor.
Maybe this will come to pass, but don’t hold your breath and be careful what you wish for. Let’s think about what it’ll take to make Moneyball work.
First, thanks to all that commented on my last article on the subject of Moneyball and suggested an ongoing conversation. The key is having “good, common, open data” (thanks Daryl Clements). Even if the data was available, we would need common standards for it to have any relevance. That is highly unlikely to occur, though there are precedents. In India, the software industry has established the National Skills Registry — a database of registered and verified knowledge workers in the industry.
Big Data and Moneyball
Several commenters pointed out that Moneyball is more about discovering undervalued assets (players/employees) than just using statistics to hire employees. This is where a big data infrastructure is likely to help in discovering nuggets of information buried in social networks and other sites, to suggest that a candidate has more (or less) to offer than is revealed in a resume or from interviews. For example, a chef whose recipes were posted on Pinterest; a customer service manager who promptly responded to complaints through tweets; or from reviews of work-related books or products posted by a candidate on Amazon. However, these pieces of information will never reach the level of objectivity or comprehensiveness achieved by baseball stats. It’s more likely that such data will help in sourcing, by revealing candidates who were not identified from common sources — the real undervalued assets.
For employers, statistics can also help reveal where they are most at risk for turnover. The SAS Institute has done pioneering work on this. Combine that with data like recent additions of references to LinkedIn profiles or comments on Facebook about other jobs and an employer can better plan for turnover and how to focus their recruiting strategies. Call it reverse Moneyball.
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AI and Automation: How They Will Impact the Future of Recruiting?
Where the concepts of Moneyball can really help are in changing the conversation around how to evaluate candidates. Billy Beane — the manager of the Oakland A’s — challenged the conventional wisdom about statistics typically used to gauge players, focused on speed and contact — such as stolen bases, RBIs, and batting average. He found that on-base percentage and slugging percentage were better predictors of success. That flew in the face of conventional wisdom and the beliefs of many baseball scouts and executives. Expect the same as a recruiter when you want to use a profile based on big data and confront a hiring manager who goes by gut feel or claims he can evaluate a candidate from their handshake.
Moneyball concepts are already being applied to college admissions by matching students with colleges. ConnectEDU is a company that uses data on students from seventh through twelfth grade to do so. They also are trying to do the same for employers hiring new grads. This is very feasible because the data is standardized, comprehensive, and accessible. Regrettably the data stops being all that once the new grad is hired into their first job.
We may have to wait for the buffoons to get their crap together before Moneyball becomes more common among employers.