Today, I finish up reviewing my 2020 Forecast BABIP lists with the BABIP decliners. Once again, I used my xBABIP equation to identify the hitters who most outperformed their xBABIP marks in 2019. Now let’s see how they performed over the short 2020 season.
Yesterday I continued my review of 2020 preseason articles by recapping my HR/FB rate surgers list. Today, I’ll review the flip side, the hitters that appeared on my HR/FB rate decliners list. The list was compiled using my xHR/FB rate, and then I identified the fantasy relevant hitters with the most significant overperformance. Let’s see how they ended up doing over the shortened season.
With hot stove transactions summarized and still no updates on when the season will begin, I’m going to continue to review my 2020 preseason articles. Obviously, it’s pretty silly to review my calls based on a 60 game season, but it’s still fun to look back on and hey, maybe we can learn something. Today’s review is my HR/FB rate surgers. The list was compiled using my xHR/FB rate, and then I identified the fantasy relevant hitters with the most significant underperformance. Let’s see how they ended up doing over the shortened season.
Edit (5/13/24): Hi! Alex here, writing to you from the future. MLBAM just published swing data (it’ll be exciting, I promise you), and one of the marquee metrics for swings on Statcast is called a “Blast.” It has nothing to do with the Blasts herein. As far as I’m concerned, Statcast is the ledger of record, so I will brainstorm new terminology for this so it doesn’t create confusion… although changing the name of my metric might create confusion for the four people or whatever who use it. Anyway, just a PSA for y’all. These are not the same blasts!
I’ve heard (read) a lot of hullabaloo about “not all barrels are equal.” Hullabaloo or not, it’s true; although barrels capture exit velocity (EV) and launch angle (LA) combinations that produce optimal wOBAcon (weighted on-base average on contact) results, the Statcast metric is defined broadly enough to include absolute blasts alongside somewhat-pedestrian hard hits within the same grouping.
The algorithm used to classify barrels is not publicly available (edit: an anonymous tipster alerted me that it, indeed, is available! I think I reverse-engineered it correctly just by sight…), but one can reverse-engineer it easily enough. Here’s a plot of all barrels since the start of the 2017 season.
Given the scatterplot, the formula is most likely as follows:
if EV < 97.5 mph, then barrel = no
if LA > 25.5° and LA < 30.5°, then barrel = yes
if LA < 25.5° and (25.5 – LA) < (EV – 97.5), then barrel = yes
if LA > 30.5° and ((LA – 30.5) * 2) < ((EV – 97.5) * 3), then barrel = yes
if EV > 97.5 mph but none of these apply, then barrel = no
“Not all barrels are equal” takes on its meaning once you convert the above scatterplot to a heatmap. I set the low end of the color legend artificially high to show the contrast between barrels that are relatively productive versus those that are massively productive:
I started diving into the dividing out the effects of injuries limited ramp time for the short season and didn’t get far. Some league-wide rates didn’t add up. Hitters who head to the IL are outperforming the healthy population.
Note: This analysis is math-heavy. I summarized my findings and questions at the end.
To start with, here are the league-wide OPS values for all nonpitchers as I compare the first month as players might have been ramping up.
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Yesterday, I listed and discussed the starting pitchers whose ERAs underperformed their SIERA marks by the most significant margins. I then reviewed the pitchers’ BABIP, LOB%, and HR/FB marks and identified which of the three metrics were driving the SIERA underperformance and what the chances for improvement in 2021 are. Let’s now shift over to the SIERA overperformers. Which of the three “luck” metrics drove such overperformance this season and can that last through next year? Let’s discuss the fantasy relevant names.
Of the three ERA estimators available on FanGraphs (SIERA, xFIP, FIP), SIERA is the best at predicting future ERA, even though it was designed as a backwards-looking metric, like the other two. If you’re still using xFIP or FIP in your pitcher analysis, then stop, and immediately switch to SIERA. In a short 60 game season, focusing on SIERA, rather than ERA, is even more important when forecasting a pitcher’s future performance.
The underlying skills that drive SIERA stabilize more quickly and the metric isn’t influenced by the gyrations of the three “luck metrics” — BABIP, LOB%, and HR/FB — which don’t have enough time to settle around the pitcher’s true talent level. ERA is heavily influenced by how a pitcher performs in those three metrics, but there’s far too much randomness involved to place significant weight on them, even over a full 162 game season. Remember though, even SIERA isn’t perfect because there are pitchers who consistently underperform or outperform due to some skill or lack thereof that has been a challenge to identify.
So let’s review the pitchers who underperformed their SIERA marks most this season (minimum 40 innings pitched). I’ll identify which of the three luck metrics fueled that underperformance and discuss whether there’s a chance the pitcher underperforms again in 2021 or reverts closer to his SIERA (I’ll only discuss the fantasy relevant names).
Recently I outlined how the installation of Hawk-Eye as Major League Baseball’s tracking and data collection system has shed light on the issue of untracked batted ball events (BBE) in prior years. The issue was first broached by Connor Kurcon, who uses launch angle for his various research and analytical endeavors, including classified run average (CRA), dynamic hard hit rate (DHH%), and TrueHit percentage.
If you’re too lazy to click through, I’ll recap: Because Hawk-Eye tracks more than 99% of BBE, we can use the distribution of launch angles in 2020 to identify the possible launch angles of untracked BBE in previous years. Most likely, untracked BBE converge on the most extreme angles — think -90° and 90°, but with a margin for error such that some BBE as shallow as -40° (for ground balls) or 50° (for pop-ups) might have still gone untracked.
Absent the information available to us now, Tom Tango and the Statcast team devised a method that would impute exit velocity (EV) and launch angle (LA) values that most closely mimic the untracked BBE’s observed outcome by measure of weighted on-base average on contact (wOBAcon). From my observation, Statcast applied roughly half a dozen different launch angle estimates for this purpose, with two in particular used disproportionately: -21° or -20.7° (for ground balls) and 69° (for pop-ups).
Again, absent the data we now have, this was as good an approach as one could reasonably expect. But now we know untracked BBE cluster around the extremes. An imputation of 69° for pop-ups is reasonable, but -21° for grounders might not be extreme enough.
To correct for this issue in the seasons preceding 2020, I adopted an approach I recommended in my original post.
Let’s continue on with our look at the plate discipline metrics by moving onto swinging strike percentage (SwStk%). With a strong correlation of 0.77 (qualified batters from 2015-2019), SwStk% is a good proxy for strikeout rate and the two will usually move in the same direction. Because SwStk% uses a denominator of total pitches, versus K%’s use of plate appearances as its denominator, it takes far less time for SwStk% to become meaningful. As such, the value of the metric increases in a shortened season like 2020. So let’s review the 2020 batter SwStk% improvers.
As the de facto purveyor of launch angle tightness (or launch angle consistency, both terms that I use interchangeably), it is important I relay to you significant developments related to launch angles in general in 2020. Let the record show I am merely the messenger and Connor Kurcon, whose name graces these pages (or at least my pages) quite often these days, is forever my muse.
In 2020, Major League Baseball instituted its new pitch-tracking (and also ball- and player-tracking) system, Hawk-Eye. You can read about its merits here, among them being its alleged ability to “more comprehensively [track] the full flight of the ball”:
Furthermore, if the ball leaves the field of view of all 12 cameras (as can happen on high pop-ups and fly balls), the system can then reacquire the ball later in its trajectory as gravity pulls it back into the view of one or more cameras.
Hawk-Eye was expected to track more than 99% of all BBE, a significant upgrade from the previous system. Many approached the claim with skepticism. Turns out, the claim may be legit.
Kurcon noticed Hawk-Eye all but ruined the year-to-year consistency of launch angle tightness. Consistency is now inconsistent! Specifically, launch angle consistency values (calculated as the standard deviation of launch angle) have nearly universally grown larger in 2020. For the purposes of launch angle consistency, higher is worse, so it gives the appearance (if you’re looking at players individually and not at the larger picture) that a lot of players cratered a bit during the spring season. And it’s not only because of the shortened season, although the season’s length does contribute partly to the discrepancy:
