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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Long has the relationship between pitcher performance and batted ball metrics been dubious. The Sabermetric community has a solid understanding of why, fundamentally, a pitcher is good or bad. Strikeouts are good. Walks are bad. Hits by pitch are also bad. Home runs allowed are especially bad. So on, so forth. And by no means are batted ball metrics useless. It’s how we know ground balls allowed are superior to fly balls allowed, for example.

The community had hoped, however, that more granular batted ball metrics would help us better explain some of the more nuanced elements of pitcher performance, including those related to luck, such as batting average on balls in play (BABIP) and the percentage of home runs per fly ball (HR/FB). Since their introduction to the public sphere in 2015, and even with the inclusion of more granular Statcast data in 2016, any relationships that might exist between the physics and outcomes for batted balls during an individual pitcher’s season are still poorly explained. The following table depicts the correlations between pitcher BABIP and various batted ball metrics, sorted by the strength of the relationship (all qualified seasons, 2007-17, n = 898):

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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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Every time an analyst uses the caveat “small sample size, but,” an angel gets its wings. And then that angel takes flight and also analyzes a small sample size.

I preach patience when it comes to the first few weeks of a Major League Baseball season, and I try to practice it, too, regarding both early-season breakouts and duds. Aside from transactions related to the disabled list, I have yet to drop any player I drafted who wasn’t legitimately dead weight (like my decaying shares of Melky Cabrera and John Lackey) or, in ottoneu, a roster burden, such as a hapless $7 share of a helpless Alex Cobb.

That said, I can’t simply wait until mid-May or whatever to make meaningful analyses of players. But I also can’t make knee-jerk reactions about 30 innings or 90 plate appearances. I try to reconcile this cognitive dissonance by engaging in what I called last year Small Sample Normalization Services (SSNS). The intent: first, to attempt to find similarly long and (un)productive streaks in a player’s past; second, to evaluate how similar or comparable those streaks actually are; and, last, to slap an appropriate level of excitement or panic to the performance in question. If we can’t say with absolute certainty that we’re watching a player do something sustainable, then maybe it helps to know if he had done something similar in the past. If not, what befell him afterward? And if so, how should we move forward with him?

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As a Twitter dork, I’m exposed to a lot of discussion about swinging strike rates (SwStr%), so much so that it almost feels like it has supplanted xFIP (or other comparable metrics) as a catch-all way to evaluate pitchers. Dude has a 12.5% whiff rate! Sweet. It’s not for naught — swinging strike rate bears a strong correlation to strikeout rate (K%), which comprises substantial portions of the regression equations that underpin the aforementioned xFIP and its counterparts. Swinging strike rate’s correlation to the following metrics (using data from the last five years of 714 pitchers who threw at least 100 innings in a given season):

• K%: r = 0.83
• SIERA*: r = 0.61
• xFIP*: r = 0.55
• FIP: r = 0.50
• ERA: r = 0.40

(*See footnote.)

It also correlates strongly year over year (among 392 player-seasons during the same timeframe in which the pitcher threw 100 innings in the current and subsequent seasons):

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A few weeks ago, esteemed analyst Ryan Bloomfield of BaseballHQ Tweeted indirectly about late-round lottery picks:

From 2014-17, we've had six players post first-round value with a 195+ ADP:

2014: Brantley, Rendon, DGordon
2015: Keuchel
2016: Villar
2017: Judge

Recent history says 1-2 will do it this year. Who ya got?

— Ryan Bloomfield (@RyanBHQ) March 21, 2018

As a measure of quality control — only because I noticed Corey Kluber omitted from 2014 — I compared his list to my historical average draft position (ADP) data from the National Fantasy Baseball Championship (NFBC) to compile the following unofficial blended list of players drafted outside the top 195 players by ADP* who achieved first-round value:

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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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