Author Archive

Madison Bumgarner’s Fastball is (Still) Broken

If something about Madison Bumgarner’s first eight starts of 2018 have seemed odd to you, it’s because they have been. No matter the fielding independent pitching statistic to which you subscribe — FIP, xFIP, SIERA (although, frankly, it should be SIERA) — Bumgarner’s 2018 has not inspired confidence. Despite a dazzling (and quintessentially Bumgarnerian) 2.90 ERA, his baserunner suppression skills (i.e. strikeouts and walks) have lagged this year, and the various FIPs all portend severe bumps in the road. Granted, Bumgarner has outperformed his FIPs the last three years and throughout his career. I’m here to argue not that we should dismiss our concerns because of this but, instead, that such overperformance has insulated us from what should be potentially serious concerns about MadBum’s long-term health and success.

The problems with Bumgarner’s 2018 season — or at least the peripherals that underpin his 2018 season — thus far stem back not to his broken finger but, rather, something both farther back and much more dire. You may or may not recall Bumgarner fell off a dirt bike last year and injured his throwing shoulder. He returned from that injury almost exactly a year ago and promptly underwhelmed us. Sure, he posted a 3.43 ERA through September and has a 3.23 ERA in the calendar year since his return. It’s not vintage Bumgarner, but it’s not awful. But the peripherals, oh, the peripherals: his strikeout rate (K%) has caved dramatically, falling more than 6 percentage points (27.1% from April 2015 through April 2017; 20.9% from July 2017 onward).

It’s his fastball. Bumgarner’s fastball, once elite (relative to other four-seamers), is broken, and it has been broken for a year.

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Midseason Review of Alex Chamberlain’s Bold Predictions

You don’t care about this part! You care about the predictions.

I originally wrote my bold predictions for 2018 here; they arrived late and incomplete, but they arrived in some capacity, and that’s all we can ask for at this point.

I make bold predictions not for the sake of being bold but, rather, (1) using earnest judgments of player abilities and market inefficiencies and (2) to create teachable moments. My better bold predictions include prescient forecasts for Jose Ramirez and Austin Barnes; my worse ones have typically revolved around Giancarlo Stanton and Chris Davis in some capacity.

Let’s see how everything’s going so far.

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Hard%, xwOBA, and the De-Juiced Ball

The league-wide hard-hit rate (Hard%) is up. Like, way up, at its highest level by far in the 17 years Baseball Info Solutions has measured and tracked the statistic.

Yet league-wide home runs are down, and way down, too, not in the whole history of the game but at least in the context of the recent Juiced Ball EraTM. Hard-hit rates and power, as measured by home runs or isolated power (ISO), increased steadily and in tandem from 2015 through 2017. You’d expect, then, that if the ball were still juiced in 2018, the league’s highest hard-hit rate ever might produce the highest league ISO ever.

No such luck, though; 2018’s .161 ISO falls a full 10 points short of last year and a tick short of 2016. Which is odd, see, because batters are hitting the ball harder than ever. Since 2015, when sabermetricians first noticed the ball was juiced…

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Diagnosing Jon Gray

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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Modeling SwStr% and GB% Using Velocity and Movement

This year, I’ve been caught up on pitching. I investigated the nuance inherent to swinging strikes, indirectly made a case for completely abandoning the sinker with this piece comparing pitch type outcomes, and (maybe) identified the keys to unlocking pitcher BABIP and HR/FB.

Here, I’ve modeled swinging strike and ground ball rates using only pitch velocity movement. Surely, this work can be improved; my quantitative tool set, while fairly robust compared to the layman, is meager compared to the professional or even hobbyist statistician. Regardless, I think it’s pretty cool, and I hope it adds to the conversation constructively.

Mostly, this serves to satiate my own curiosity. Unfortunately, it may be denser than I expected — few answers are ever quite as simple as you hope them to be, I guess.

Existing Research

I linked to several of my own pieces above. Dan Lependorf wrote about estimating ground ball rates in 2013 at the Hardball Times, although its conclusions have an anecdotal slant. (It thinks about velocity and movement but doesn’t take the requisite steps to bridge the logic.)
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Buying Low on Hitters Using xwOBA

There are, like, a dozen articles of this nature written daily — that is, “buy-low” candidates using some kind of xMetric, likely derived from Statcast. That’s fine. I’m not hating on it. This was my modus operandi when I first started writing at RotoGraphs, and it’s how I really started to understand the cyclicality of player performance and the differences between descriptive and predictive metrics.

Speaking of which, I have no desire to rehash the “what xwOBA should really represent” discussion that consumed the sabermetric sphere a week or two ago. (Although, for reference, I’ll link you to Baseball Prospectus, MLBAM’s Tom Tango, and FanGraphs’ Craig Edwards.) Primarily, I want to provide some facts about xwOBA followed by some non-facts about how I use xwOBA to keep my biases in check.

There are two important tenets to xwOBAism. At the player level, wOBA does not always converge on xwOBA…

  1. in a given season.
  2. over the course of a career.

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You Wrote Off Kyle Schwarber Too Soon

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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The Keys to Pitcher BABIP and HR/FB, Perhaps

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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Addition by Subtraction: Fixing Dylan Bundy Long-Term

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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ERA Minus SIERA Laggards: Gonzales, Archer, Gray

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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