Recruiter and Sourcer Metrics – THE LIST

This is one in a series of posts on “Moneyball for Recruiting”. This post covers the primary metrics that should be tracked for sourcers and recruiters. Other posts on the methodology, concepts, and definitions can be found on this site by clicking the tag or category “Moneyball” on our blog site.

These are NOT your typical HR or Recruiting Metrics
You can do the quality, speed, cost, experience route – and that has LOTS of value. There are plenty of dashboards, or basic metrics that will help guide on how a business is doing / performing. Plenty of people have an opinion on what is needed, what is important, and what can be measured. There are attempts to standardize, and I applaud those efforts, but I have a different point of view on how business analytics should be applied to human performance.

Over the years, I have been accused of overcomplicating metrics, as well as trying way too hard to make the analogy between baseball and recruiting exist. I am totally guilty of that behavior – then again I guarantee 30% improvement in performance and/or 30% reduction in costs associated with staffing and recruiting as part of a contract, and take a percentage of the savings of lift as payment. I put my money where my mouth is. I have paid my dues, and still here doing analysis for a living. So for those who doubt these methods because they are different than what you have done, different than what you do, different than what you have authored, or different than what you have recommended…I am going to ask that you consider leaning in and listening – and hold off on throwing shade as a defense mechanism.

The Metrics
What are the Moneyball metrics a recruiter / sourcer should track that helps them honestly understand their performance, and IMPROVE their performance over time?

1. On Base Percentage (OBP)
2. Slugging Percentage (SLG)
3. On Base Percentage plus Slugging (OPS)
4. At Bat to Home Run Ratio (ABHR)
5. Walk to Plate Appearance Ratio (BB/PA)
6. Isolated Power (ISOP)
7. Runs Created (RC)

WTF — Baseball stats?
Well it is Moneyball.

In business analytics, we pose questions to data. Questions as simple as “what is the mean of this data?” or “what is the mode of this data?”. We ask more complex questions like “which of these variables are significant, and which are not?” We then use a combination of these questions to understand causality, make predictions, or just explore data. The various insights are derived from taking data and placing into formats so they can be analyzed (data preparation), and then using models to analyze the data.

In this case, we are asking actually a pretty complex question – how much do my recruiters or sourcers contribute to talent acquisition?  It is not as easy as saying “how many hires happened”. This question actually requires SEVERAL questions to be asked first…not just one, which actually is standard for data mining. Business analytics and the pending insight is the result of understanding what IS significant and what IS NOT significant…and the large share of the work is in what IS NOT significant.

To find the underlying questions to our big question, we need to first understand the key drivers (aka variables) that indicate recruiting success. If you were to run a regression analysis of candidate data and the stages of recruiting and hiring that every applicant, interviewee, and hire goes through you would learn that that the most significant factor of making a hire quickly is how many days elapse while an organization gets 3 candidates through the assessment process. In other words, the faster you get three candidates through all your interviewing, the faster you hire.

It is NOT how fast the first candidate is presented.
It is NOT the number of candidates that apply.
It is NOT the source or origin of the candidates.
It is NOT who the hiring manager is.
It is NOT who the recruiter is.
It is NOT the location.
It is NOT how or where it posted.
It is NOT how much money is spent.

All of those items do kick off little itty bitty readings…but nothing statistically significant. You can learn all about t and p values by Googling it – meanwhile there are hardly ANY in your candidate data.

In fact, a recruiter who helps the hiring manager get 3 candidates through the entire interview process within 14 days of a requisition being opened experiences a time to fill that is 30% LESS than his/her counterparts who take more than 14 days. They also require less candidates to go through the actual interview process in total AND those recruiters tend to work on more hires annually than their counterparts.

(insert pause for that statement to sink in….still pausing….reread it….okay, now moving on)

Now that we know that, we need to focus on how recruiters perform tasks that focus on that independent variable. That enables us to ask the underlying questions below. Of course a business’s brand presence, its marketing spend, and the overall talent of the recruiter / sourcer create different outcomes to these questions (but that is what we are trying to find out)…

  1. How often and when exactly does a hiring manager accept the candidates submitted by the sourcer / recruiter?
  2. What type of volume and choices were provided to the hiring manager?
  3. How good are the choices recruiters and sourcers make?
  4. How good, how often, and how fast are the slates of candidates that the sourcer / recruiter produced given the number of chances they are given?
  5. How many slates were easy to produce out of the number of requisitions they worked on?
  6. How often does the sourcer / recruiter shorten the process on their own through sweat / sourcing?
  7. How many hires would a sourcer / recruiter make if hiring managers did as expected?

These 7 questions are answered by calculating the metrics listed above – OBP, SLG, OPS, AB/HR, BB/PA, ISOP and RC respectively.

What did you think? Did you REALLY think I was going to throw up Source of Hire and Time to Fill? Or ?  Come on – this is much more complex system that depends on the daily analysis of human performance. Remember to read the other posts to understand the background of this method and do the math to get your preliminary numbers. You will have to do a “deep dive on hitting” to really get started.

So why these metrics?

1) It is because we are talking about RECRUITER performance…not requisition performance. I am suggesting we turn away from our organization’s results, and pose questions about recruiter / sourcer performance directly when we talk to the data. We can start looking closely at the team members and their performance each day, in every instance. We then should be offering coaching, assistance, and resources to help them get incrementally better on achieving daily results…and that means a VERY different set of measures from those we have used in the past (time to fill, etc) and also different data (candidate data not requisition data).

2) We have a huge database for comparison – and we don’t need to pay ANYTHING extra to get to it. It is the Major League Baseball database. You want to know how good a sourcer or recruiter really is? Get your candidate data together, run the numbers for a recruiter or sourcer, convert your activity into baseball statistics as indicated here, and THEN go to and compare to real Major League baseball players.  You can compare to teams or players from various seasons. Be prepared though…you may not be as good as you think you are…or maybe you are a star and not being rewarded for it.


OBP – On Base Percentage
This indicates how often a submission is accepted by the hiring manager. This is also known as Submission Business Accepted, or SBA. It is calculated by taking the sum of Hits (H), Base on Balls (BB), and Hit by Pitch (HBP) and dividing by Plate Appearances (PA). The formula is (Hits + BB + HBP) / PA

SLG – Slugging Percentage
Slugging in recruiting and sourcing helps us understand the “recruiting power” that the team member is bringing to the employer. In baseball, it counts how many bags a player touches, but in recruiting it helps us understand how close to a hire a sourcer or recruiter is with their overall number of submissions that were accepted per job. Since we are counting total “bases”, we need to add our Hits, Doubles, Triples, and Home Runs together and divide by the number of At Bats. The formula is (((Hits-(2b+3b+HR)) + (2B*2) + (3B*3) + (HR*4)) / AB

OPS – On Base Percentage plus Slugging
Here we simply add OBP and SLG. The number can be above 1.00. It is my belief that OPS is one of the most telling and leading indicators of the contribution a recruiter or sourcer is making versus the rest of the team.  In fact, an organization can measure the health of all recruiting by creating a team OPS, and measure that against other organizations as a whole. It is the LEADING indicator in Major League Baseball for hitting performance. The formula is (OBP + SLG)

AB/HR – At Bat to Home Run Ratio
The AB to HR ratio helps us understand how often a sourcer or recruiter is able to make a significant one time impact on their own, without assistance from the brand or other assets. It allows us to focus on how good of a “sourcer” they really are, and how good they are at getting the hiring managers multiple candidate choices in spite of how bad the brand is, how tight the market is, or where the role is located. The formula is (AB / HR)

BB/PA – Walk to Plate Appearance Ratio
Since walks are part of the game, we need to see how much is being served up by the pitcher as an easy way to get on base. Most recruiters draw walks through the year, meaning they get requisitions that are not that tough. The question is how many AND how does that impact their on base percentage. The formula is (BB / PA)

ISOP – Isolated Power
This helps us understand how rich the slates of candidates are that we have, and how often a recruiter puts a powerful slate together as a percentage of their overall efforts. Its one thing to produce choices, its another to do it with diversity and speed. The formula is (SLG – BA)

RC: Runs Created
Is A FASCINATING metric for recruiting. In baseball, it helps simulate how many runs would have been created by the player assuming stable conditions by the opposing teams. It sort of takes all their hits and says “this is how much all these hits are worth as runs for our team”. In our recruiting case  is becomes “even though they did X hires, they actually produced enough work to really produce Y hires”. The formula is ((H + BB – CS) * (TB + (.55 + SB))) / (AB + BB).

Next Steps
So you want to do this right away, and start understanding how your teams are performing. You have to get your candidate data tight. That means your teams using systems and automation to get things right. Of course we have tools here at Aspen to help you, but if your teams are really not using their systems religiously, this will be a little harder, so you might have to proxy some data to get it all to work, or just do some spot analysis.

Start by seeing if you can actually track the hits, doubles, triples, and home runs that your team members are executing (see other posts and the deep dive on hitting). If you have those, you are ready for prime time. If not, don’t be too disappointed. Most organizations are in their infancy in regards to recruiting data compliance. We have tools, automation, AI and analytics to help.