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 »

Pitch modeling isn’t anything particularly unique or groundbreaking. It’s the kind of thing Harry Pavlidis and Jonathan Judge (of Baseball Prospectus) and our once-editor Eno Sarris (now of The Athletic) have investigated for years. I won’t claim to break new ground here. I’m just a nerd who likes testing hypotheses for himself.

Last year, I used velocity and movement, courtesy of PITCHf/x, to model swinging strike and ground ball rates for pitchers. That post was not my best work (easy to say in hindsight), primarily because of limitations with the data. The data, from Baseball Prospectus, was aggregated, such that I couldn’t isolate any single pitch thrown by a pitcher. The advent of Statcast has enabled us to do exactly that, providing publicly accessible hyper-granular pitch-level data and changing how the public sphere of sabermetricians nerd out.

Something I have wanted to do for a long time is refresh my previously-linked analysis, but with (1) Statcast data and (2) a different modeling approach — namely, the use of a probit model rather than a multiple regression model. For most of you, this means nothing. It’s gibberish. I don’t intend to wade too deeply into the weeds of the modeling, lest I disorient or alienate. Mostly, I just want to communicate I think it’s an exciting and different way to answer the everlasting question: how does a pitch’s velocity, movement, and spin rate affect its outcome?

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Sorry for the late post, y’all. With Memorial Day, spending time with nieces and nephews, and then getting sick because of said nieces and nephews, it has been a bumpy week.

It’s a good mission statement, Brad. The mission statement in question: Peripheral Prospects seeks to identify obscure future fantasy contributors. That seems good, right? Let’s roll with it.

I missed last week with travel and then subsequent illness. There’s a lot of housekeeping to catch up on!

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Last week, I recommended an improvement for expected fielding independent pitching (xFIP) without dismantling the original FIP framework upon which it was built. FIP describes the relationship between ERA and strikeouts, walks, and home runs allowed; xFIP does the same but attempts to remove the luck component from home runs by multiplying the number of fly balls a pitcher allows by the league-average rate of home runs to fly balls (HR/FB) — the rationale being HR/FB is notoriously fickle to project year to year.

The recommendation: change HR/FB to include line drives (LDs) and exclude infield fly balls (IFFBs, aka pop-ups). It’s worth noting our dark overlord David Appelman once explained how removing pop-ups from aggregate fly balls insignificantly affects xFIP. Additionally, less than 1% of line drives result in home runs. The recommendation, then, seems like the merging of two separate but equally fruitless endeavors, given the facts.

Yet changing the HR/FB component in xFIP to be “HR/(oFB + LD)” substantially improved the metric’s correlation with same-year ERA. Adjusted r², which measure the strength of relationship from 0 to 1, increased from 0.42 to 0.55 using Statcast data (0.44 to 0.53 using FanGraphs data). I hypothesize that, when added to fly balls, line drives (despite resulting in very few home runs) give a more holistic indication of the average contact quality and launch angle a pitcher allows.

Today’s recommendation: account for start/relief splits.

Although I thought of this independently, the idea itself is far from an original one. Read the rest of this entry »

One might argue that xFIP is slightly misaligned. One might make that argument in blog form, on the website FanGraphs, today, here, now.

One only might argue that xFIP is slightly misaligned because xFIP is commonly understood to serve a purpose distinct from FIP. FIP, aka Fielding Independent Pitching, is calculated as a function of strikeouts, walks, and home runs — that is, outcomes over which fielders bear no influence. The equation that underpins FIP is derived from a linear regression equation intended to resemble ERA, for ease of interpretation. Because it is based exclusively on outcomes, its purpose is more descriptive than predictive. In other word, it finds greater purpose describing what should have happened but not necessarily what will happen.

xFIP, on the other hand, seeks to achieve the inverse. A large swath of evidence exists to suggest home run-to-fly ball rate (HR/FB) for pitchers is incredibly noisy season to season. Sure, certain pitchers might anecdotally buck the norm — apparently, Michael Pineda was born to be a cafeteria lunch lady, serving up meatballs and taters — but, by and large, HR/FB is a fool’s errand to predict. Accordingly, xFIP replaced home runs with expected home runs, by way of multiplying the number of fly balls allowed by a pitcher by the league-average HR/FB, thereby normalizing home run damage, making it, in theory, a better descriptor (and perhaps a better predictor) of pitcher performance over time.

And therein lies the rub, although, if you missed it, you mustn’t be blamed.

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Behold! Another installment of Peripheral Prospects, the low-price, off-brand fantasy baseball version of Fringe Five. Brad Johnson and I have brought something on the order of five minor leaguers per week who make us feel — like, really feel. These players tend to be unloved and unheralded but very much deserving of love and, uh, herald, not unlike the two authors of this series.

Some quick housekeeping, per usual:

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Another week, another installment of Peripheral Prospects. Through six weeks — three from me, three from esteemed colleague Brad Johnson — we have featured 26 different players with whom one or both of us express(es) some degree of infatuation.

The aim is simple: identify players who (1) have little to no hype as a prospect, having missed all major top-100 lists; (2) have not exhausted their rookie eligibility yet; and (3) are reasonably close to the majors, such that you’re not dreaming on talent that projects dubiously from the low minors. The rules are not hard-and-fast; it would be a shame to not mention certain players who we think exceed the credit they’ve been given. Accordingly, we will relax the constraints every now and then to make accommodations.

Before I dive into my weekly five, some quick housekeeping, per usual:
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This is the Fantasy Fringe Five, but is no longer named as such. This is the last time you’ll see Brad or me use that alliterative phrase to describe this series. And that’s it! Keepin’ it cryptic. This is now, until or unless we come up with something better, Peripheral Prospects. (We welcome recommendations! Leave a comment.)

That said, we might start steering this ship more deliberately in our own direction. In the spirit of Carson Cistulli’s series after which this is now loosely modeled, we will sometimes follow its self-imposed restrictions and other times elect to deviate from them — most probably, the one underlined below:

In light of same, eligibility for The Fringe Five will require (for the present, at least) the following:
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Consider this the low-hanging fruit Fantasy Fringe Five (FFF). I’ll endeavor to make future installments more fringy. However, given the circumstances — namely, that the season started and folks are already thinking about future waiver wire pursuits — I wanted to highlight some MLB-ready fringe prospects for your dynasty (and potential redraft) consumption. One (or more?) of these guys check the box of almost too good to not be top prospects, which, uh, I guess is the entire premise of this series.

But first, some housekeeping:

Very Important News

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Another year, another set of bold predictions, and another introduction to The Process^TM. I did well last year, hitting on Matt Chapman and Miles Mikolas out-earning their teammates Matt Olson and Luke Weaver despite enormous divides in National Fantasy Baseball Championship (NFBC) average draft position (ADP) as well as Madison Bumgarner being worse than a not-top-20 starting pitcher (with an asterisk for his late start in 2019). I might’ve hit more bold predictions last year than in my previous three seasons combined.

Bold predictions can but don’t have to be a frivolous exercise. As fun as it is to slap a 40-homer prediction on Franmil Reyes (…should I do that?), I don’t find it particularly illuminating unless it’s supported by evidence (…which exists for Reyes?!). You can make bold predictions without being outrageously bold — it’s exactly what I intended to accomplish last year simply by leveraging what I observed to be extreme market inefficiencies at play. I stuck my neck out for Chapman and Mikolas and Bumgarner, but not as far as folks might think. There was enough evidence in their (and, where applicable, in their teammates’) bodies of work for me to make objectively bold predictions on the basis of draft price or market consensus without them feeling particularly bold to me.

While endeavoring to go 6-for-10 this year just to match last year’s hit rate would be absurd, I do think I can hit another three, at least, in 2019 if I pick my spots correctly. So, here goes: my 10 bold predictions for 2019.

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