Archive for Meta Analysis

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.

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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).

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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.

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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.

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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:

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Let’s move along in our 2020 review of underlying skill metrics to the plate discipline rates. While the shortened season makes it difficult to evaluate many metrics, and especially counting stats, plate discipline rates don’t take as long to reach a reasonable sample size because it uses a pitch-based denominator which ends up as a larger number than most other rate denominators. From 2015 through 2019, Z-Contact% had a correlation coefficient with K% of about -0.82 when requiring a minimum of only 100 plate appearances. That’s pretty darn strong! So let’s review and discuss the Z-Contact% improvers versus 2019, that recorded at least 100 plate appearances in each season.

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A week ago, I reviewed the average fly ball distance laggards and discussed the surprising names. Today, let’s look at the decliners, those hitters who lost the most average fly ball distance (AFBD) versus 2019. There will likely be some overlap with the laggards list, so I won’t discuss the same names again. Once again, I’ll require a minimum of 10 Statcast fly balls to qualify for the list.

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Last week, I listed and discussed the barrels per true fly ball (Brls/TFB) surgers versus 2019. I apparently totally forgot to review the decliners, instead moving on to average fly ball distance leaders and laggards. So let’s get back to Brls/TFB analysis and check out the biggest decliners.

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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?

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With fewer games for extreme performances to regress back toward player and league averages, it stands to reason that surger and decliner lists are going to be pretty lengthy and more surprising than normal. In addition, the degree in which the metric being analyzed surged or declined is likely to be much greater than in past, full seasons. So let’s remember that when reviewing 2020 numbers and put much less stock into the fact that a player doubled or tripled some rate from 2019, and instead simply consider that the player may have enjoyed skills growth that could potentially carry over into 2021. Today, let’s review the barrels per true fly ball (Brls/TFB) surgers. Many of these same names appeared on the leaders list posted on Tuesday, so I’ll try discussing only those not on that list.

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