Category: Statcast

Inspired by the curious case of Harrison Bader

whose average exit velocity is horrific, hard hit% is average, and barrel/contact% is great (not shown, but a little better than the xwOBA marker), I decided to look at which one of these metrics was more predictive.  Barrels are significantly more descriptive of current-season wOBAcon (wOBA on batted balls/contact), and average exit velocity is sketchy because the returns on harder-hit balls are strongly nonlinear. The game rewards hitting the crap out of the ball, and one rocket and one trash ball come out a lot better than two average balls.

Using consecutive seasons with at least 150 batted balls (there’s some survivor bias based on quality of contact, but it’s pretty much even across all three measures), which gave 763 season pairs, barrel/contact% led the way with r=0.58 to next season’s wOBAcon, followed by hard-hit% at r=0.53 and average exit velocity at r=0.49.  That’s not a huge win, but it is a win, but since these are three ways of measuring a similar thing (quality of contact), they’re likely to be highly correlated, and we can do a little more work to figure out where the information lies.

I split the sample into tenths based on average exit velocity rank, and Hard-hit% and average exit velocity track an almost perfect line at the group (76-77 player) level.  Barrels deviate from linearity pretty measurably with the outliers on either end, so I interpolated and extrapolated on the edges to get an “expected” barrel% based on the average exit velocity, and then I looked at how players who overperformed and underperformed their expected barrel% by more than 1 SD (of the barrel% residual) did with next season’s wOBAcon.

Avg EV decile	>2.65% more barrels than expected	average-ish barrels	>2.65% fewer barrels than expected	whole group
0	0.362	0.334	none	0.338
1	0.416	0.356	0.334	0.360
2	0.390	0.377	0.357	0.376
3	0.405	0.386	0.375	0.388
4	0.389	0.383	0.380	0.384
5	0.403	0.389	0.374	0.389
6	0.443	0.396	0.367	0.402
7	0.434	0.396	0.373	0.401
8	0.430	0.410	0.373	0.405
9	0.494	0.428	0.419	0.441

That’s.. a gigantic effect.  Knowing barrel/contact% provides a HUGE amount of information on top of average exit velocity going forward to the next season.  I also looked at year-to-year changes in non-contact wOBA (K/BB/HBP) for these groups just to make sure and it’s pretty close to noise, no real trend and nothing close to this size.

It’s also possible to look at this in the opposite direction- find the expected average exit velocity based on the barrel%, then look at players who hit the ball more than 1 SD (of the average EV residual) harder or softer than they “should” have and see how much that tells us.

Barrel% decile	>1.65 mph faster than expected	average-ish EV	>1.65 mph slower than expected	whole group
0	0.358	0.339	0.342	0.344
1	0.362	0.359	0.316	0.354
2	0.366	0.364	0.361	0.364
3	0.389	0.377	0.378	0.379
4	0.397	0.381	0.376	0.384
5	0.388	0.395	0.418	0.397
6	0.429	0.400	0.382	0.403
7	0.394	0.398	0.401	0.398
8	0.432	0.414	0.409	0.417
9	0.449	0.451	0.446	0.450

There’s still some information there, but while the average difference between the good and bad EV groups here is 12 points of next season’s wOBAcon, the average difference for good and bad barrel groups was 50 points.  Knowing barrels on top of average EV tells you a lot.  Knowing average EV on top of barrels tells you a little.

Back to Bader himself, a month of elite barreling doesn’t mean he’s going to keep smashing balls like Stanton or anything silly, and trying to project him based on contact quality so far is way beyond the scope of this post, but if you have to be high on one and low on the other, lots of barrels and a bad average EV is definitely the way to go, both for YTD and expected future production.

 

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Using the same grouping methodology as The Statcast GB speed adjustment seems to capture about 40% of the speed effect, except using barrel% (barrels/batted balls), I got the following for air balls (FB, LD, Popup):

barrel group	FB BA-xBA	FB wOBA-xwOBA	n
high-barrel%	0.006	-0.005	22993
avg	0.006	0.010	22775
low-barrel%	-0.002	0.005	18422

These numbers get closer to the noise range (+/- 0.003), but mashers simultaneously OUTPERFORMING on BA while UNDERPERFORMING on wOBA while weak hitters do the opposite is a tough parlay to hit by chance alone because any positive BA event is a positive wOBA event as well.  The obvious explanation to me, which Tango is going with too, is that mashers just get played deeper in the OF, and that that alignment difference is the major driver of what we’ve each measured.

 

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Statcast recently rolled out an adjustment to its ground ball xwOBA model to account for batter speed, and I set out to test how well that adjustment was doing.  I used 2018 data for players with at least 100 batted balls (n=390).  To get a proxy for sprint speed, I used the average difference between the speed-unadjusted xwOBA and the speed-adjusted xwOBA for ground balls.  Billy Hamilton graded out fast.  Welington Castillo didn’t.  That’s good.  Grouping the players into thirds by their speed-proxy, I got the following

 

speed	Actual GB wOBA	basic xwOBA	speed-adjusted xwOBA	Actual-basic	Actual- (speed-adjusted)	n
slow	0.215	0.226	0.215	-0.011	0.000	14642
avg	0.233	0.217	0.219	0.016	0.014	16481
fast	0.247	0.208	0.218	0.039	0.029	18930

The slower players seem to hit the ball better on the ground according to basic xwOBA, but they still have worse actual outcomes.  We can see that the fast players outperform the slow ones by 50 points in unadjusted wOBA-xwOBA and only 29 points after the speed adjustment.

 

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