Category: Statcast

Everything else about the opportunity being equal, corner OFs have a significantly harder time catching pulled balls  than they do catching opposite-field balls.  In this piece, I’ll demonstrate that the effect actually exists, try to quantify it in a useful way, and give a testable take on what I think is causing it.

Looking at all balls with a catch probability >0 and <0.99 (the Statcast cutoff for absolutely routine fly balls), corner OF out rates underperform catch probability by 0.028 on pulled balls relative to oppo balls.

(For the non-baseball readers, position 7 is left field, 8 is center field, 9 is right field, and a pulled ball is a right-handed batter hitting a ball to left field or a LHB hitting a ball to right field.  Oppo is “opposite field”, RHB hitting the ball to right field, etc.)

The joint standard deviation for each L-R difference, given those Ns, is about 0.005, so .028+/- 0.005, symmetric in both fields, is certainly interesting.  Rerunning the numbers on more competitive plays, 0.20<catch probability<0.80

Now we see a much more pronounced difference, .095 in LF and .111 in RF (+/- ~.014).  The difference is only about .01 on plays between .8 and .99, so whatever’s going on appears to be manifesting itself clearly on competitive plays while being much less relevant to easier plays.

Using competitive plays also allows a verification that is (mostly) independent of Statcast’s catch probability.  According to this Tango blog post, catch probability changes are roughly linear to time or distance changes in the sweet spot at a rate of 0.1s=10% out rate and 1 foot = 4% out rate.  By grouping roughly similar balls and using those conversions, we can see how robust this finding is.  Using 0.2<=CP=0.8, back=0, and binning by hang time in 0.5s increments, we can create buckets of almost identical opportunities.  For RF, it looks like

If there’s truly a 10% gap, it should easily show up in these bins.

and it does.  Whatever is going on is clearly not just an artifact of the catch probability algorithm.  It’s a real difference in catching balls.  This also means that I’m safe using catch probability to compare performance and that I don’t have to do the whole bin-and-correct thing any more in this post.

Now we’re on to the hypothesis-testing portion of the post.  I’d used the back=0 filter to avoid potentially Simpson’s Paradoxing myself, so how does the finding hold up with back=1 & wall=0?

.11x L-R difference in both fields.  Nothing new there.

In theory, corner OFs could be particularly bad at playing hooks or particularly good at playing slices.  If that’s true, then the balls with more sideways movement should be quite different than the balls with less sideways movement.  I made an estimation of the sideways acceleration in flight based on hang time, launch spray angle, and landing position and split balls into high and low acceleration (slices have more sideways acceleration than hooks on average, so this is comparing high slice to low slice, high hook to low hook).

And there’s not much to see there.  Corner OF play low-acceleration balls worse, but on average those are balls towards the gap and somewhat longer runs, and the out rate difference is somewhat-to-mostly explained by corner OF’s lower speed getting exposed over a longer run.  Regardless, nothing even close to explaining away our handedness effect.

Perhaps pull and oppo balls come from different pitch mixes and there’s something about the balls hit off different pitches.

The effect clearly persists, although there is a bit of Simpsoning showing up here.  Slices are relatively fastball-heavy and hooks are relatively Other-heavy, and corner OF catch FBs at a relatively higher rate.  That will be the subject of another post.  The average L-R difference among paired pitch types is still 0.089 though.

Vertical pitch location is completely boring, and horizontal pitch location is the subject for another post (corner OFs do best on outside pitches hit oppo and worst on inside pitches pulled), but the handedness effect clearly persists across all pitch location-fielder pairs.

So what is going on?  My theory is that this is a visibility issue.  The LF has a much better view of a LHB’s body and swing than he does of a RHB’s, and it’s consistent with all the data that looking into the open side gives about a 0.1 second advantage in reaction time compared to looking at the closed side.  A baseball swing takes around 0.15 seconds, so that seems roughly reasonable to me.  I don’t have the play-level data to test that myself, but it should show up as a batter handedness difference in corner OF reaction distance and around a 2.5 foot batter handedness difference in corner OF jump on competitive plays.

TL;DR OAA is far better, as expected.  Read after the break for next-season OAA prediction/commentary.

As a followup to my previous piece on defensive metrics, I decided to retest the metrics using a sane definition of opportunity.  BP’s study defined a defensive opportunity as any ball fielded by an outfielder, which includes completely uncatchable balls as well as ground balls that made it through the infield.  The latter are absolute nonsense, and the former are pretty worthless.  Thanks to Statcast, a better definition of defensive opportunity is available- any ball it gives a nonzero catch probability and assigns to an OF.  Because Statcast doesn’t provide catch probability/OAA on individual plays, we’ll be testing each outfielder in aggregate.

Similarly to what BP tried to do, we’re going to try to describe or predict each OF’s outs/opportunity, and we’re testing the 354 qualified OF player-seasons from 2016-2019.  Our contestants are Statcast’s OAA/opportunity, UZR/opportunity, FRAA/BIP (what BP used in their article), simple average catch probability (with no idea if the play was made or not), and positional adjustment (effectively the share of innings in CF, corner OF, or 1B/DH).  Because we’re comparing all outfielders to each other, and UZR and FRAA compare each position separately, those two received the positional adjustment (they grade quite a bit worse without it, as expected).

Using data from THE SAME SEASON (see previous post if it isn’t obvious why this is a bad idea) to describe that SAME SEASON’s outs/opportunity, which is what BP was testing, we get the following correlations:

OAA wins running away, UZR is a clear second, background information is 3rd, and FRAA is a distant 4th, barely ahead of raw catch probability.  And catch probability shouldn’t be that important.  It’s almost independent of OAA (r=0.06) and explains much less of the outs/opp variance.  Performance on opportunities is a much bigger driver than difficulty of opportunities over the course of a season.  I ran the same test on the 3 OF positions individually (using Statcast’s definition of primary position for that season), and the numbers bounced a little, but it’s the same rank order and similar magnitude of differences.

Attempting to describe same-season OAA/opp gives the following:

As expected, catch probability drops way off.  CF opportunities are on average about 1% harder than corner OF opportunities.  Positional adjustment is obviously a skill correlate (Full-time CF > CF/corner tweeners > Full-time corner > corner/1B-DH tweeners), but it’s a little interesting that it drops off compared to same-season outs/opportunity.  It’s reasonably correlated to catch probability, which is good for describing outs/opp and useless for describing OAA/opp, so I’m guessing that’s most of the decline.

Now, on to the more interesting things.. Using one season’s metric to predict the NEXT season’s OAA/opportunity (both seasons must be qualified), which leaves 174 paired seasons, gives us the following (players who dropped out were almost average in aggregate defensively):

FRAA notably doesn’t suck here- although unless you’re a modern-day Wintermute who is forbidden to know OAA, just use OAA of course.  Looking at the residuals from previous-season OAA, UZR is useless, but FRAA and positional adjustment contain a little information, and by a little I mean enough together to get the r^2 up to 0.47.  We’ve discussed positional adjustment already and that makes sense, but FRAA appears to know a little something that OAA doesn’t, and it’s the same story for predicting next-season outs/opp as well.

That’s actually interesting.  If the crew at BP had discovered that and spent time investigating the causes, instead of spending time coming up with ways to bullshit everybody that a metric that treats a ground ball to first as a missed play for the left fielder really does outperform Statcast, we might have all learned something useful.

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.

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.

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.

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):

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.

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

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.