Monday, January 26, 2009

What’s the best BABIP estimator?

Background info

The test

The combatants

• Previous year BABIP (BABIP): This is simply the player's BABIP from the previous year.
  
• Expected BABIP (xBABIP): This is a BABIP model created by Chris Dutton and Peter Bendix, introduced at THT last month in this article. xBABIP is the primary reason for this article as I have been very curious how well our newest model does what it intends. Also, please note that Chris has tweaked the model a little since the original article ran. Please check the bottom of this article for more details.
  
• Quick Expected BABIP (qxBABIP): As Dutton and Bendix's xBABIP includes some stats that aren't readily available to the casual fan, they've created a simplified version using stats that are readily available, of course, at the (expected) expense of accuracy.
  
• Line drive BABIP (ldBABIP): This is the one that gets the most play. Everyone seems to be using it these days, but for reasons I've explained many times before, I'm not a fan. It's calculated as line drive rate plus .120.
  
• Studes BABIP (studesBABIP): This one was created around the same time Dave Studeman put out Line drive BABIP but doesn't get nearly the same attention. Not a whole lot more difficult to calculate, but uses more than one variable. It's calculated as 0.245 + 0.52 * LD% - 0.16 * FB% + 0.11 times K%.
  
• Expected Batting Average BABIP (xBA BABIP): This one is the BABIP portion of Baseball HQ's Expected Batting Average (xBA) statistic. I should note that this uses HQ's SX stat, which I couldn't replicate precisely. I was, however, able to get it very close. Also, because SX and PX are indexes based on league (American/National) average, for player's switching teams mid-year, I weighted each based on games spent with each team.
  
• Marcels BABIP (mBABIP): This isn't so much an estimator as a projection, but I thought it would be good to include for context. It's simply what Marcels projects for the following year. It's also currently what I'm using in my True Batting Average calculations.

The process

The results

+---------------+-------+--------+---------+---------+-------------+-----------+--------+
| TEST          | BABIP | xBABIP | qxBABIP | ldBABIP | studesBABIP | xBA BABIP | mBABIP |
+---------------+-------+--------+---------+---------+-------------+-----------+--------+
| Correlation   | 0.38  |  0.50  |   0.45  |   0.20  |       0.32  |     0.40  |  0.46  |
| R-Squared     | 0.14  |  0.25  |   0.20  |   0.04  |       0.10  |     0.16  |  0.21  |
| Average Error | 0.028 |  0.021 |   0.022 |   0.029 |       0.024 |     0.022 |  0.022 |
+---------------+-------+--------+---------+---------+-------------+-----------+--------+

+------+-------------+
| RANK |   ESTIMATOR |
+------+-------------+
|    1 |      xBABIP |
+------+-------------+
|    2 |      mBABIP |
|    3 |     qxBABIP |
+------+-------------+
|    4 |   xBA BABIP |
|    5 |       BABIP |
+------+-------------+
|    6 | studesBABIP |
+------+-------------+
|    7 |     ldBABIP |
+------+-------------+

Going forward

Some notes from Chris Dutton

The differences are basically that I used hr/fb as a measure of power rather than pitches per extra base hit, added popups/FB to measure poorly hit balls, and included an interaction variable of lefty*(fb/gb%) to adjust for the fact that lefty ground ball hitters tend to often hit balls to the right side of the field (which rarely become hits). I also removed pitches_per_AB, which seemed to be potentially correlated with other variables, and removed hitter_eye since contact rate seemed to be capturing a very similar effect.

References and resources

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