Little League Teams, Cy Young Winners, and High Performing Analytics Teams

Kimberly WestFLOCK Notes

With most sports conversations in the U.S. swirling around which college teams deserve (or not) to be in the College Football Playoff Semi-Finals, I might be out of step to be thinking about baseball right now.

I have had several conversations recently – including conversations within my own organization – around what kind of analytical talent is needed and when as an organization’s data analytics strategy matures.  Should we be hiring data engineers? Database managers?  Statisticians?  Undergrads?  PhDs?

I see a parallel here with baseball.  Depending on your spending cap, you may be tempted to hire a Cy Young Winner for your little league team – having a Ph.D. Computer Scientist on your analytics team would be a huge coup – think of all the wins!  The trophies!  The free ice cream!

Well…maybe.  Hiring a world class pitcher for a little league team is probably a bad idea for three reasons:

  1. The MLB champion pitcher’s skills can never be fully appreciated by the little league team. A pitcher with a 90mph curveball with a 12inch break will not win any more little league games than a pitcher with a slower fastball who can get the ball consistently across the plate – not to mention the appreciation of the 10 year old catcher.
  2. The other kids won’t play with him. A superstar data scientist will likely have difficulty fitting in.  User acceptance and adoption of technical solutions is the hardest part of any digital transformation – learning new technical skills can be challenging.  And solutions that depend upon broad-based proficiency with the newest technology might be a hard sell to people who have been comfortably using Excel for 20 years.
  3. Simply put, hiring the champion pitcher is an inefficient use of resources (even if you don’t have a salary cap). According to the most recent Burch Works Salary Study[1] data science professionals with over 10 years’ experience, average over $250-$300K per year depending on the industry.  Alternatively, more junior people with less than 3 years’ experience average less than $100K per year.  Many organizations would be better served with 2-3 junior data scientists than with an expensive superstar.

The analytics maturity curve – like the journey from Little League to the Major League – is a process without shortcuts.  You may be better off hiring a little league player who can grow into a Cy Young winner.

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