Quick All-Star Break Study #1: Batting Average Regression

Over the break, I’m going to run a few quick studies. If you want to request, add it to this Twitter thread.

I am running a few quick fantasy baseball studies over the next few days. I'll take suggestions. The two I have planned:

1. If a hitter's AVG is below his projection, how much rebound is expected?

2. How much does a month's ERA/FIP/xFIP matter when looking forward?

— Jeff Zimmerman (@jeffwzimmerman) July 16, 2024

Someone I know said they drafted for batting average (AVG) this spring, but their team is struggling in the category. They wondered how much upward regression should be expected. This is a simple study with a reasonable answer, some but not all.

Here is what I used from the parameters.

• Steamer projections from 2021 to 2023 for hitters projected for 300 or more plate appearances.
• The hitter must have had 100 PA in the first half and 50 PA in the second half. I used a lower PA threshold in the second half to account for survivor bias. Bad performers might get demoted even if they are talented.
• The standard deviation for the difference is projected at .026 AVG. I used .025 for simplicity and found the hitters who under and overperformed their projection.

The first table shows how much hitters who under and overperformed their batting average projections expected to rebound toward their Steamer projection.

The hitters rebound but not all the way back to their projections. A talent change kept them from returning to their projection.

I wanted to see if their strikeout rate or BABIP was causing the dip in batting average. I grouped the hitters who saw a 25-point drop in average into those who did or didn’t exceed their preseason projections.

Both sets of hitters saw their BABIP and K% take a hit in the first half. In the second half, both saw an improvement in K% (not as much for the non-rebounder) but the driving factor behind the batting average bound was BABIP. The overperformers saw a 64-point jump in BABIP while the underperformers saw just a 9-point improvement.

BABIP is the driving force behind batting average missing and with changes to the shift rules and pitcher approach (fewer fastballs), it’s safe to say old rules no longer apply.

While the question on the expected rebound is answered, some but not all, I’ve opened up a whole new inquiry on BABIP regression. Are there traits for hitters who see a different amount of regression? With new metrics and a changing game, I’m going to dive into what someone should be looking at to see if a rebound or a dead cat bounce should be expected.