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.
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?
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.
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.
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.
First, sorry about no drop list article last week… more on that later. Second, today’s experiment stems from HappyFunBall’s comment from a couple of weeks ago.
I was well into the analysis last week and found I was not answering the right question and had the wrong data. The drop comparisons need to be a little forward-looking, not backward. The data collected from the sites was after the NFBC FAAB ran. The other sites I used allow daily or quick-draw pickups, so some of the news from the Monday was getting baked in. I wanted the rates to be taken from similar timeframes.
My original question focused on which players I should analyze so owners can gain an edge. The edge could be knowing who to drop because they are no longer fantasy-viable. Also, the hope is to find some players whose owners gave up too early on. The hope is that one of the other platforms can provide more insight than the NFBC Main Event.
Several issues exist with this study. First, it’s just one week and a busy week at that with the trade deadline. The increased activity may help to offset the lack of moves since it’s later in the season. I’m not 100% sure but I may run the same analysis again but I need to do it before the head-to-head playoff start and main teams get abandoned.
Finally, the table is a mess. A big one. What Happy Fun Ball and I hoped for didn’t materialize but a few interesting observations can be made.
Nearly every article I write, I do it because I find the content useful. It’s an article I wish others would write but don’t. Because no one was writing about lineups is why I started writing one. Last year I tried writing both the AL and NL versions rotating weeks but the two-week gap between leagues was too long. Too many changes happened. This year Al Melchior took NL teams and I went with the AL ones. Because of the extra research, I’ve noticed my mixed-league teams lean AL.
My pickups are all AL leaning probably from writing the AL lineup analysis. I have to trust others for what is going on in the NL.
— Jeff Zimmerman (@jeffwzimmerman) July 22, 2019
It’s not just my lineup article providing self-centered content. My closers rankings and FAAB projections help me make decisions and I hope they help others.
On Monday and Tuesday, I identified and discussed a slew of hitters who were underperforming or overperforming their xBABIP marks, a backwards-looking metric I developed to try cutting through the noise to determine what a hitter’s BABIP should be given various underlying skills I deemed important. One of those skills was speed. Obviously, a faster hitter has a better chance to beat out an infield grounder for a hit than a slower batter. The problem is, how do I know how fast a hitter is once he makes contact and exits the box en route to first base?
Yesterday, I identified and discussed 12 hitters whose xBABIP marks were significantly higher than their actual marks, suggesting potential dramatic upside for the rest of the season…assuming they maintained those underlying skills. Now let’s check in on the opposite group of hitters — those whose BABIPs have significantly exceeded their xBABIP marks, suggesting the potential for serious downside the rest of the way if they are unable to improve those underlying skills.
I’ve had the question written on my whiteboard for ever: are foul balls good or bad? It’s a glass-half-empty, glass-half-full conundrum. The former group might think a foul ball is simply a barely-missed opportunity at in-play contact. The latter group might view that same event as a positive — that the poor quality of contact on a foul ball is indicative of an ability to induce poor contact quality in general, and it’s not inherently different from a swinging strike.
In my heart of hearts, it makes more sense to me that a foul ball is closer to in-play contact than not. Considering the diameter of both a bat and a ball, and the nearly physically impossible feat of connecting the two in motion, a foul tip has a margin of error of mere inches, whereas a swinging strike, fully sans contact, can have a margin of error measured in feet. Yes, it seems like getting a piece of the ball suggests, from the pitcher standpoint, makes the glass appear more half-empty than otherwise.
I wanted to finally tackle the subject, but I didn’t really know how. I first looked at the outcome of the pitch directly following foul and non-foul pitches, but it was a bit noisy (although, to be fair, I may have missed clear patterns in that noise). I imagine the effects spawning from a foul ball are not exclusive to the next pitch; rather, they may manifest two or three or even four pitches deeper into the plate appearance. In other words, a pitch-sequencing analysis might be prohibitively difficult, at least for someone like me who lacks the brainpower or mental stamina to pull it off.
Instead, I opted for something a little easier yet arguably just as telling. Read the rest of this entry »
