MiLB League Average BABIPs, ISO, and Strikeout and Walk Rates


Context is everything in statistical analysis.  EVERYTHING.  Consider the following players:

  • Player A: In 659 PA, he hit .281/.345/.369, with 5 HR and 20 SB.
  • Player B: In 693 PA, he hit .324/.384/.490, with 20 HR and 0 SB.
  • Player C: In 672 PA, he hit .263/.347/.575, with 51 HR and 5 SB.

With the available data, which player – A, B, or C – had the best offensive season?  Stumped?  All three players, according to Weighted Runs Created Plus (wRC+), had identical seasons; all three performed 33% above the league average.  Player A is former Browns player George Stone and his production in 1908.  Player B is Hall of Famer Jim Rice’s production with the Red Sox in 1986.  And, finally, Player C is Andruw Jones during his 2005 season with the Atlanta Braves.

See, context is everything.  All three players performed in drastically different eras and, yet, with the proper context it’s easy to see how they all produced equal when compared to the league average during their respective seasons.

This mini-project started out as a way to kill time, a way to spend what precious few minutes I have at night.  And, of course, it became something entirely larger than I expected.  Using the data available on, I calculated the league average totals for BABIP (batting average on balls in play), ISO (isolated power), walk rate (BB%), strikeout rate (SO%), and walk-to-strikeout ratio (BB/SO), for each league, for every year since 1877.

Are you curious to see the BABIP for, oh, say, the 2011 Eastern League (Double-A)?  It’s .311, of course. Well how does that compare with the Appalachian Rookie League in 1964?  Actually, pretty well; it was .317 that season.  Or what about the ISO for the PCL in 1997?  Yup, it’s there too (.184).

Some information is incomplete and it’s noted in the final column, or it may not be available.  But for the most part the overwhelming majority of the data is there.

So, why is this useful?

Context.  It always comes back to context.

For example, say a player posted an ISO of .200 in the PCL in 1997 and another player posted an ISO of .180 in the Arizona League last season.  It’s now easier to see how they performed relative to the league average.  Context.

I posted the stats at my website,, or click here to take you directly to the numbers.  And, again, this was using the information already compiled at  None of the listed statistics were available however.

Hope this helps everyone!


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