In a fairly surprising turn of events, the Rockies demoted Jon Gray Saturday. Gray has arguably been baseball’s most enigmatic pitcher this year, posting a career-worst 5.77 ERA supported by career-best peripherals — e.g., a 13.4% swinging strike rate (SwStr%) underpinning a 28.9% strikeout rate (K%), and fielding independent metrics of 2.78 xFIP, 3.08 FIP, and 3.15 SIERA. Given our most basic sabermetric understandings of baseball, Gray should be a very good pitcher, even if he pitches half his starts at hitters’ paradise Coors Field.

I have written about how a common-breed Rockies pitcher’s peripherals might be penalized for calling Coors Field home (Gray inspired this bit of research as well). FIP metrics generally underestimate ERA by anywhere from 0.8 to 1.3 runs for home starts (compared to 0.0 to 0.2 runs for road starts), suggesting that Rockies pitchers may underperform (a) their FIPs by 0.35 runs or (b) their SIERAs by 0.65 runs — given error bars, maybe more.

Still, that doesn’t explain why Gray’s ERA is nearly 6 right now. I shed light on the ridiculousness of the move; his strand rate (LOB%) is suppressed and his batting average on balls in play (BABIP) is elevated, even compared to his uniquely bad baselines. I’m not sure there’s much more to it.

Nick Mariano of RotoBaller noted here that Gray’s fastball has been incredibly hittable since his debut and especially this year. Despite my thoughts on the inevitability of regression in Gray’s favor, I wanted to pursue Mariano’s train of thought a little further. Gray’s fastball is bad, but how bad? And why?

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Well, maybe not you, specifically. The royal you. The editorial.

Kyle Schwarber has had himself a pretty dang good season so far. It’s exactly what I needed. Having just inherited my first ottoneu team — a relatively downtrodden 9th-place team (of 12) — and hardly knowing the rules, I took a gamble and traded a $12 Jon Gray for a $6 Patrick Corbin, a $7 Willie Calhoun, a $3 Jake Junis, and a $20 Schwarber. I liked every piece of the trade (although I, now regretfully, cut Junis during spring training, not really understanding the dynamics of the draft, my finances, or of ottoneu generally). But acquiring Schwarber at his relatively exorbitant price given his 2017 season was a risky proposition, especially after a summer of these headlines:

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Some good pitchers, despite being good pitchers, throw bad pitches. And there are bad pitchers, too, who throw good pitches. Both are true, and one could make an argument a Venn Diagram of the two groups may overlap significantly, and that overlapping area is the group of pitchers toeing the line between breaking out and being unusable for fantasy purposes.

It stands to reason, then, that good and bad pitchers could benefit from easing off or completely abandoning their bad pitches. It’s one thing to evaluate a pitch based on its underlying metrics — its swinging strike rate (SwStr%), its ground ball rate (GB%), its velocity, and so on. It’s another thing to evaluate the pitch objectively by looking at its weighted on-base average (wOBA) allowed, which, I hope, in an adequately large sample, can indicate a pitch’s quality regardless of its peripherals. In theory, the larger the sample size, the greater the probability a pitch’s outcomes will converge with its inputs, such that the caveat “regardless of its peripherals” doesn’t actually mean anything. Given enough pitches thrown, the aforementioned underlying metrics will adequately inform the wOBA allowed.

Using PITCHf/x data from the last two years, I looked for (1) good pitchers who throws pitches that allow (2a) extremely bad wOBAs with (2b) unusually low BABIPs. Incurring high wOBAs on low BABIPs is less than ideal; if BABIP is subject to high variance and generally converges on the league average, then a bad pitch being “lucky” by BABIP suggests things will only get worse.

This post was going to be about several pitchers, each with their own problematic pitches, but I became too passionate about this single case. This is about Dylan Bundy, his abhorrently bad four-seamer, his fantastic slider, and how much his pitch selection is suffocating his potential. Ultimately, it’s about adding by subtracting.

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FanGraphs hosts a statistic for pitchers called ERA Minus FIP (“E-F”), which is as advertised. FIP being a (somewhat) adequate measure of pitcher over-/under-performance, one could look to E-F to identify pitchers who may, as they say, be due for regression. FIP’s correlation with ERA, however, is weaker than that of xFIP due to the former’s inability to account for the volatility inherent to home run-to-fly ball ratios (HR/FBs). To take it a step further, xFIP’s correlation with ERA is weaker than that of SIERA due to the former’s inability to account for a pitcher’s ground ball rate (GB%) and how it interacts with his strikeout and walk rates (K%, BB%).

Alas, I often use SIERA, rather than xFIP or FIP, to identify pitchers who may be ripe for regression. ERA Minus SIERA (“E-S,” henceforth) is not the be-all, end-all by any means, and I would never consider making a roster decision based exclusively on that metric. Player evaluation is a holistic endeavor, which you likely know yet I still intend to demonstrate. Three names stood out to me — four, if you include Luis Castillo, but I covered him a week and a half ago — as interesting E-S targets, but I came away from this feeling good about only one of them.

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Last week, I reintroduced my Small Sample Normalization Services (SSNS), analyzing strong starts by Dylan Bundy, Jose Berrios, and Patrick Corbin in the context of other small samples within their respective careers or recent histories. This time, I discuss three more odd starts among starting pitchers and their implications.

Chase Anderson, MIL SP

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Apologies for publishing this more than a week after Opening Day. Life gets in the way sometimes.

I had grand plans to make a prediction for each defensive position (two for pitchers — one starter, one reliever). Turns out I won’t even make the standard 10 predictions. Again, apologies. As opposed to waiting any longer, I’m moving forward with my favorites and letting the others decay on the cutting room floor.

Here’s how my post, had it been published on time, would’ve started:

For me, making bold predictions is not about being bold just to be bold. It’s about abiding by The Process^TM — albeit sometimes by grasping at sabermetric straws — and using it to extract value where the market insists there is none (i.e., identify the market’s largest inefficiencies). Works vice versa, too. It also takes a little bit of balance to not make the predictions too bold so I don’t stand a chance to get any correct, but I also don’t want them to be too easily attainable, either.

In the past, The Process^TM has led me to prophetic predictions about Jose Ramirez and Austin Barnes. It has also led me to humiliating defeats, like predicting a Giancarlo Stanton bust preceding one of the more memorable seasons in recent history. Such is the nature of bold predictions; you must wear your victories loudly and proudly, but also own your mistakes. Above all, bold predictions should be teachable moments, not pissing contests.

FYI: I concocted these predictions prior to Opening Day, first week of the season be damned.

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On Monday, CBS Sports’ Chris Towers took to Twitter to put a bit of a scare into Aaron Judge’s hype-men and -women, posting the following set of images:

*whispers* less than most people are pretending pic.twitter.com/fUsNuMfRTn

— ADP Boy (@CTowersCBS) March 12, 2018

Frankly, I loved it. This word is overused, but, alas: it certainly “triggered” some of his followers. Folks were quick to defend Judge, shielding him from the likenesses he shares with Chris Davis that might, in some version of the near future, manifest in a similar player trajectory. Granted, much of praise for Judge was warranted; an additional 8 percentage points of walks elevates his floor a bit higher than Davis’. Fact of the matter, though, is Judge’s power has few modern comps — namely, Davis, Giancarlo Stanton, Ryan Howard. And I’ve compared Stanton to Howard more than once, so the four of them share the same curiously constructed boat.

What caught my attention, though, aside from the similarities between Judge and Davis that are far more striking than most are willing to admit, is, perhaps surprisingly, the doubles column. In Davis’ monster 2014 campaign, he hit 17 more extra-base hits than Judge did in 2017. Seventeen! That’s no small number. And it got me thinking: a ratio of 52 home runs to 24 doubles is actually kind of alarming. They (whoever “they” are) say a season with lots of doubles but fewer home runs than expected portends more power in the following season. Testing that wisdom, conventional or not, is outside the scope of this post. What I’d rather test is the inverse: does a season with lots of home runs but few doubles (or, generally, other extra-base hits) portend less power?

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Normally I don’t write about bad players. It’s more of a truism than anything: writers like to analyze the breakout or peak-performance potential of top prospects or, alternatively, red flags associated with the game’s premier talents. Rarely do we write about objectively bad players.

Through 120 Major League innings (and change), Robert Stephenson has been an objectively bad starting pitcher, having compiled a 5.10 ERA, an anemic 1.63 strikeout-to-walk ratio (K/BB), and 0.1 WAR. A former 1st-round pick and a consensus top-100 prospect for four consecutive years, Stephenson quickly fell from grace after a catastrophic small-sample debut in 2016. Entering his age-25 season, though, he still has plenty of time to turn things around.

That’s the beauty of baseball: an objectively bad player can become an objectively good one, sometimes overnight. 2017 was a banner year for post-hype prospects, all of whom seemed, at one point or another, destined for eternal mediocrity and former-prospect bustitude. I think Stephenson can become an objectively good pitcher, but it’ll take work.

Here’s a top-10 list, presented ordinally and without the statistic by which I’ve ordered it, of pitchers who accomplished something in 2017, from a list of hundreds of other data points:

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In case you missed the announcement in Paul Sporer’s latest post:

FanGraphs now has NFBC ADP data!

NFBC ADP data used to be hosted at Stats, Inc. Prior to last week, 2018 data had only been available to NFBC contestants.

Anticipated FAQs:

Where can I find the data?

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I wrote a feature for a forthcoming fantasy baseball magazine about players with the potential to make or break your season. Due to space constraints, some of the copy lay on the cutting room floor as the magazine shipped to print. Rather than let it go to waste, I figured someone may enjoy reading my leftover snippets for players with volatile outlooks for the 2018 seasons. (The rest you’ll find on physical and digital bookshelves sometime in spring.)

I’ll present each blurb as is and, afterward, provide links to relevant work I’ve written related to that player as well as any final thoughts I couldn’t originally fit into my word count limits. It’s worth noting the target audience includes fantasy baseball enthusiasts of all skill levels, some of which invariably fall short of those of typical RotoGraphs frequenters. Alas, I made my best effort to conduct worthwhile analysis without getting overly technical.

Ordered roughly by expected average draft position (ADP).

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