Hi! Disclaimer: In this post I use raw Statcast data to calculate expected batting average (xBA). Evidently the raw data do not include the sprint speed adjustment that the Statcast folks said they made. That adjustment only shows up on player pages and in the search. This explains why it seemed to me an adjustment had not been made! The xBA values on player pages are much closer than the raw values and look similar to what I have presented below, and it explains my confusion herein regarding the matter.
So, this post reinvents the wheel a bit. Perhaps it can serve as a mini-primer or -tutorial for you. At the very least it can serve as further validation of the work that the folks at Statcast completed and instituted a couple of years ago. Just keep in mind that the original post below remains intact, completely unedited.
Thanks for reading!
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It has always seemed rather obvious to me that Statcast’s expected batting average (xBA) failed to properly account for a hitter’s speed (“sprint speed”). It seemed like fast hitters routinely outperformed their xBAs while slower hitters underperformed. In looking at a Statcast-era leaderboard (2015-21) of differentials between actual and expected batting averages on ground balls, obvious names rise to the top: Delino DeShields, Dee Strange-Gordon, Eduardo Núñez, Billy Hamilton, Jose Altuve, Jonathan Villar, Norichika Aoki, Mallex Smith, Jean Segura, Adam Eaton, Starling Marte… the list of players who have historically outperformed their xBAs by the widest margins are (were) all elite speedsters. At the other end of the spectrum, post-prime sluggers: Justin Smoak, Chris Davis, Logan Morrison, Jay Bruce, Kendrys Morales, etc. etc.
I thought this exact phenomenon, which is not a revelation by now, had once nudged the Statcast team to apply a sprint speed adjustment to xBA. Apparently, this happened sometime between the 2018 and 2019 seasons. Here’s the original snippet, which I very lightly edited for clarity:
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Here is a disputed fact: MLB changed the ball. League brass, on the record, wanted to make the ball livelier but also raise the height of the seams, which would increase drag. The two changes — more bounce, but also more air resistance — would, more or less, offset each other.
The fact is disputed because some of the game’s most intelligent minds — namely, renowned baseball physicists, the very people most capable of determining if the ball is, indeed, different — doubt the ball has changed. It’s imperative I tell you this because they may be right, which would make me (and MLB, for the umpteenth time), well, wrong. Everything that follows assumes the ball has changed. Maybe this meshes with what you’ve witnessed, maybe it doesn’t. This is simply one stupid man’s interpretation of the data available to us thus far.
Early returns suggest MLB accomplished what it set out to accomplish. We can use weighted on-base average on contact (wOBAcon) to describe hitter production on balls in play, aka batted ball events (BBE). The average hitter is slightly less productive in 2021 than in past years, but not egregiously so, as shown below. Also, it’s only April; as the weather warms, so should be the bats. It’s reasonable to expect 2021’s league-wide wOBAcon value to climb a few ticks before year’s end.
Often, when an analyst reviews his or her draft, it is typically ahead of the season about to be played. That makes sense! We are planning for 2021, not 2020. It might behoove us, however, to review how drafts actually went. I’m guilty of doing the former and not the latter! With the dawn of the second annual Razzball RazzSlam best ball tournament upon us, I figured now is as good a time as any to rectify this.
Last year, I entered the inaugural RazzSlam having played, and subsequently bombed, in a couple of best ball drafts on Fantrax. I wanted to dip my toe in, get my feet wet, and other joint-aquatic/podiatric idioms, so I entered a couple of low-stakes leagues, figuring I could wing it. As foreshadowed, I fared poorly. I, in fact, could not wing it.
Having failed somewhat spectacularly for a guy who fancies himself at least somewhat knowledgeable and/or adept at fantasy baseball, I wanted to right my wrong by taking my preparation for RazzSlam seriously. I finished 15th out of 216 participants (18!leagues of 12 teams each), somehow not winning my league but ending up one of the highest 2nd-place finishers above some league winners. I’m proud! Because I expected another unmitigated disaster.
Hence, I figure it might be worth reviewing, with hindsight, one of my rare successful drafts. We played out the season already, so instead of trying to outline tips and tricks up front, it might be easiest to simply show you the draft results, each player’s stats, and the clear takeaways from my strategy — some intentional, some not.
In 2019, Brad Johnson and I published a weekly series in which we, each on a semiweekly basis, identified three or four or five players in the Minor Leagues who (1) had not appeared on previous top-prospect lists and (2) appeared to us to be capable of producing admirably, perhaps significantly, at the big-league level at some point for fantasy purposes.
Because of an actual force majeure (i.e., the COVID-19 pandemic), Peripheral Prospects was rendered temporarily null as the Minor League Baseball season was cancelled. Alas, we published nothing about peripheral prospects. But that does not mean peripheral prospects did not thrive! Peripheral prospects indeed thrived.
I figured it would behoove me to not only review my favorite peripheral prospects from the end of 2019 but also highlight my favorite (existing) peripheral prospects heading into 2021, before a whole new batch of peripheral prospects is anointed. Yesterday, I revisited my 10 favorites from 2019; today, I’ll highlight another 10 eight whose progress I’m eager to monitor in 2021.
Presented in chronological order (and not by favoritism):
I figured it would behoove me to not only review my favorite peripheral prospects from the end of 2019 but also highlight my favorite (existing) peripheral prospects heading into 2021, before a whole new batch of peripheral prospects is anointed. Here, I’ll revisit my 10 favorites from 2019; next time, I’ll highlight another 10 whose progress I’m eager to monitor in 2021.
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:
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.
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:
Maximum exit velocity (max EV) measures a player’s hardest-hit ball, typically measured within a single season and compared against other players. Our Mike Podhorzer has documented its leaders and laggards. Rob Arthur, one of baseball’s best public analysts and whom I admire greatly, wrote intelligently on the importance of max EV as a projection-buster back in 2018. Max Freeze (real name) blends extremely hard hits (114+ mph) with launch angle to look for possible power breakouts ahead of 2020.
It has been established (by Al Melchior and me, in fact) that max EV, while an effective indicator, is not the or even a superior indicator of hitter power.
That’s not to say max EV is useless, by any means. It is altogether a different breed of metric than, say, barrel rate (Barrel%, either per plate appearance [PA] or per batted ball event [BBE]) or average exit velocity (EV), both to which fantasy baseball analysts refer much more often. The latter two, and many others, are rate metrics that need large sample sizes to become reliable — or, in common parlance, to “stabilize.” (More on that here, from our former and beloved Eno Sarris.)
Meanwhile, max EV is not a rate or average but a singular data point. It can happen at any moment in time — including the very first batted ball of a hitter’s season. This makes it an intriguing addition to the ol’ tool belt insofar as it could become “reliable” (not necessarily in the statistical sense) much sooner than would barrels or EV. Potentially, we could use max EV loosely as a leading indicator of where a hitter’s barrel rate, average EV, or even weighted on-base average on contact (wOBAcon) might eventually settle.
So: what are the merits of max EV?