A couple of weekends ago at BaseballHQ‘s First Pitch Arizona conference, The Athletic’s Eno Sarris and I talked about hitter metrics most descriptive and/or predictive of power. In Eno’s presentation, he included a quip from analyst Hareeb al-Saq:

“Knowing barrels on top of average EV [exit velocity] tells you a lot. Knowing average EV on top of barrels tells you a little.”

Eno was surprised by this finding — that barrel rate is a more beneficial metric than average EV, or even EV on a certain type of batted ball event (BBE), such as fly balls and line drives. Incidentally, this is something Al Melchior and I researched last year for which we reached the same conclusion: barrels, whether as a percentage of batted ball events or plate appearances, correlate more strongly than average, maximum, or fly ball/line drive EVs did to common power metrics such as home runs per fly ball (HR/FB), isolated power (ISO), or hard-hit rate (Hard%).

However, it made more sense to Eno when I articulated that calculating barrel rate is simply the act of isolating a hitter’s most-optimal batted ball events. In other words, the inclusion of launch angle (LA) adds another explanatory dimension to EV. In my head, it’s like having two separate circles — one for EV, the other for LA, each containing every individual batted ball outcome from the season — and overlapping them. The overlapped portion of the Venn diagram signifies barrels, and it changes in size depending on the quality of the batted ball events.

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Note: For my next few articles, I’m going to examine the hitters and pitchers who underperformed and overperformed in 2019. Each article may spawn off others since some areas may need to be explored in more detail. After performing horribly in 2019, I need to take take a hard look at why I missed last season and what I can do to improve.

I’m going to start with the one player class every owner hopes to hit on, breakout hitters. A couple of these cheaply acquired star hitters can help carry a team. It could be a prospect turned uber-prospect (e.g. Pete Alonso) or just a hitter displaying new skills (e.g. Ketel Marte). I’m going to dig into the reason these breakouts were not draft-day targets and look for any common themes.

To get the test subjects, I ran our auction calculator for end-of-season production and then compared the auction dollars to the values created from their ADP. I didn’t want to just use the difference in ranks because the gap from #1 to #15 could be over $10 but the difference between #250 to #300 might just be $1. Using this method, I found 62 hitters who outperformed their value by $10 or more.
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After doing an overview of the juiced ball and a focus on pitchers, I started down a path to find out which hitters would be most affected by the juiced ball. I didn’t know it was going to be overrun with thorn bushes and misleading signs. The process was nearly impossible for the simple fact that the league’s hitting profile changed. Besides even considering the ball, batters were hitting more flyball, hitting the ball harder, and pulling it over short corner fences. I tried to find one answer but ended finding another.

One key to this analysis is that I wanted to keep it simple. I didn’t want to pump the data into some neural network for a more “correct” answer where I’d not sure of the factors in play and how each one was weighted. I wanted some clarity.

One set of factors I initially used was the StatCast information but I didn’t use it for the final analysis because it didn’t add any accuracy. Groundball rate is almost a perfect proxy for Launch Angle. Home run per batted ball is basically a Barrel. Also, StatCast data has only been available since 2015 when the juiced ball started. There is no baseline data for the deadened ball period.
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… but not by much for teams I just owned …

My 2019 fantasy season did not live up to my standards with most of the struggles being self-inflicted. Here are some overarching themes I spotted with each league plus some additional points at the end.

Horrible FAAB management

I ran out of money in almost every league and spent too much FAAB on worthless assets. Looking back over the leagues, the root cause was chasing week-only plays. From my work writing “The Process”, I found out how valuable it is to grind out each week. Additionally, I ran the weekly FAAB projections here so I knew around what it would take to get each. Initially, I got the players and but dropped them a week or two later for better options with little to show for the FAAB spent

I need to set a FAAB limit for chasing week-only plays and just accept it’s fine to miss out on a few players. A week’s advantage is worth the same in week 1 or the final week. The rest of my FAAB can be used for chasing long term improvements. Some players may straddle the long-term and weekly play so the FAAB may come from both the weekly and long term pools. I need to have a plan and stick to it.
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I’ve screwed up. A lot. A few years back, I created a pitching metric called pERA which took each individual pitch’s results (swinging-strike and groundball rate) and combined them into one metric. The problem was that I wrote the article in 2016 and used a formula I created back in 2015 with 2002-2015 data. The juiced ball arrived and I never adjusted the formula for the change. Oopsy.

I felt a little sick when it finally dawned on me that the formula needed updating and ma initial findings are available in this Twitter thread.

I've invested too much time this morning looking into GB%, ERA, and ERA estimators. Two conclusions:

1. Happy Fun Ball has everything messed up. Don't assume any previous assumptions are true.
2. Groundball pitchers are ruling the day.

1/6

— Jeff Zimmerman (@jeffwzimmerman) August 16, 2019

It finally dawned on me that the formula I was using to evaluate pitchers was off. With strikeouts and walks being equal, groundball pitchers outperform flyball pitchers. With a deadened ball, a high flyball rate meant most flyballs would go for easy outs. Not any more. Now those flyballs go for home runs. It’s time for a little math to show the change.
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To begin my 2020 preseason, the elephant needs to be addressed, “What to do about projecting with MLB’s juiced baseball (i.e. Happy Fun Ball).

Is Freddy Galvis going to continue to jack 20+ home runs or will he maxing out at dozen or so? Every projection can’t be a Choose Your Own Adventure story. If the ball is still juiced, he’ll do X, if not juiced, he’ll do Y. In my analysis, I’ll pick a lane.

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Back in June, I tried to tackle the age-old question: are foul balls good or bad? I tried to determine the “worth” of a foul ball by grouping plate appearances by their number of foul balls (from zero to four-or-more) and looking at two outcome metrics: strikeout rate (K%) and weighted on-base average (wOBA). Unfortunately, my endeavor turned up mostly duds. There are some interesting nuggets – a pitcher’s wOBA allowed improves by nearly 30 points in two-strike counts if he allows at least one foul ball – but most other splits were meaningless. Similar attempts to quantify the effect of a foul ball on the subsequent pitch were similarly fruitless.

I stepped back from the research to let it breathe. Intuitively, I knew there should be value here – I just wasn’t sure how it would present itself. Then, one day (specifically, June 27), inspiration struck in the form of Bryse Wilson’s third career start, during which he incurred nine swinging strikes but also 20 (twenty!) foul balls on 56 four-seam fastballs, amounting to a 16% swinging strike rate but also an absurd 36% foul ball rate (Foul%). The coincidence of many whiffs and also many fouls struck me as fascinating and extremely relevant to my previous research. It encouraged me to reframe the question at hand:

How does foul ball rate correlate with other measurements of success by pitch type?

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If there were ever a baseball question that keeps me up it night, it’s this: how do the physical properties of pitches affect batted ball outcomes? Many researchers have tackled the subject with varying degrees of success and elucidation. My attempts have focused primarily on a pitch’s ability to generate swinging strikes and ground balls, the first of which used pitcher-level PITCHf/x data while the more recent of which used individual pitch-level Statcast data.

While modeling whiffs and grounders is interesting (and important, too), something strikes me as much more compelling and confounding: the relationship, if any, between a pitch’s physical properties and its batted ball outcomes, whether described as exit velocity, launch angle, or total base-run value allowed, as measured by weighted on-base average (wOBA) or even expected wOBA (xwOBA).

The ability to prove “contact management” as a legitimate and shared pitcher skill has long eluded the Sabermetric community. Assumptions of a league-average batting average on balls in play (BABIP) and, for xFIP, home runs per fly ball (HR/FB) pervade the common ERA estimators (FIP, xFIP, SIERA) we use to gauge talent and assign value. Those assumptions regarding BABIP and HR/FB imply a pitcher’s inability to control them — and there isn’t much evidence to suggest otherwise.

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With a month and a half left in the season, you must keep focus on your placement in the various categories. Forget about overall fantasy value and worry about how each player on your roster will help you gain (or prevent you from losing) points in each category. If you find yourself in need of speed, here are six names that are likely available in many leagues that have been running over the past month.

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This is, and is not, a Mike Tauchman post. My relentlessly Tauchman-centric brand has been, in the words of beloved pal Sammy Reid, “hotter than the sun’s ass.” Tauchman has become the folk hero Yankees fans didn’t know they needed. I also have become insufferable to everyone within digital arm’s length of my Twitter account.

When I reviewed my bold predictions in July, I lamented Tauchman’s bad-luck strikeout rate (K%). By measure of “deserved” strikeout rate (I regressed the components of every hitter’s plate discipline against their strikeout rates to derive a “deserved” rate), Tauchman had been one of Major League Baseball’s unluckiest hitters.

Despite his recent torrid streak, Tauchman still emerges as one of 2019’s unluckiest hitters. That is why this is, in a sense, still a Tauchman post. But it’s also an Everyone Else post, in that I’m eager to unearth baseball’s luckiest and unluckiest hitters this year.

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