Two weeks ago, I wrote about the importance of evaluating expected strikeout rate (xK%) in the context of each pitcher’s respective histories. In other words, xK% on its own can only tell you so much about a pitcher’s chance and magnitude of regression toward the mean.

And last week, I refined the expected walk rate (xBB%) metric for pitchers by adding a proxy for pitch sequencing in the form of percentage of counts that reach 3-0 (“3-0%”). This helped better explain the model’s fit with respect to the data, as pitchers who worked into more 3-0 counts tended to walk more batters. (Who knew?)

The logical next step is to combine the two aforementioned analyses: 1) comparing xBB% to BB% 2) for each pitcher over time. I’ll reiterate a couple of key points. Calculating a pitcher’s xBB% can give us a decent idea of how lucky or unlucky he may have been during a given season. Calculating his xBB% and comparing it to his actual BB% on an annual basis can give us a better idea of truly how he typically performs against his xBB% — that is, if he consistently outperforms his xBB%, perhaps the difference between his xBB% and BB% is not a matter of luck at all but a skill or characteristic not captured by the variables specified in the xBB% equation.
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I have never thought much about, or given much attention to, FanGraphs’ fan projections. It’s primarily a matter of stubbornness: I run my own projections, and I develop my own strategy, so why should I listen to you? At least there’s a trace of rationale behind it: I know how I created my projections (whether or not they’re any good is a topic for another day), but I have no idea how you created yours. Thus, I am more likely to blindly trust a computer-generated projection system such as Steamer instead of random fan projections.

Still, there is a sort of bizarre, secondhand wisdom to fan projections. For every person who is high on a particular player, there could be another person who is equally-and-oppositely down on him. Solicit and aggregate enough fan projections and you could produce a very reasonable prediction of a player’s performance by sheer chance.

Which is why fan projections intrigue me. If, for example, Steamer predicts the most likely outcome from a wide range of possible outcomes for a player, then the fans convey the anticipated outcome for a player. The difference between them, you could say, is what amounts to a market inefficiency (aka a price distortion). The larger the difference, the greater the inefficiency. We see these inefficiencies arise every year — 2014’s most prevalent example is probably Corey Kluber — and they typically manifest because of a lack of information about certain players.

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I expected to follow up my xK% differential post from last week with a complementary xBB% differential post. For those who don’t enjoy surprises, I’ll let you know now that that didn’t happen. In its stead, I bring what I hope is good news — news that will not only influence a future xBB% differential post but also may impact general pitcher analysis henceforth and possibly international diplomacy.

The title of this post, however, is a tad misleading. I think I can say, with some degree of certainty — and I hope to demonstrate, with some degree of competency — that pitch sequencing indeed plays a role in a pitcher’s walk rate, as the devilishly handsome Mike Podhorzer has postulated. What I can’t describe, with any degree of certainty, is the magnitude of the role it plays. In truth, I desperately want to prove Mike wrong: there must be other factors, outside of pitch sequencing (and pitch framing, perhaps), that help explain a pitcher’s walk rate. For example, I have tried incorporating O-Swing% and Zone%, two PITCHf/x metrics provided by FanGraphs that I swore would fill in the cracks, but they offer little in the way of additional explanatory power.

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The baseball community — owners, scouts, fantasy analysts et al. — is slowly learning how Cuban hitters plucked from the Cuban National Series (CNS) perform in Major League Baseball. Unfortunately, the sample size is not increasing very quickly. The common fantasy owner is helplessly resigned to rely on a) scouting reports, and/or b) his or her own eyes, probably via a batting practice video uploaded online. Ideally, a Cuban hitter’s salary would serve as a proxy for what one could expect offensively and defensively from his imported bat and glove, but the market, and the information that defines it, is far from perfect.

The market for Cuban hitters is a pendulum, but rather than coming to rest, it is in full swing: hitters such as Yoenis Cespedes, Yasiel Puig and Jose Abreu, who are all but locks to fulfill the value of their modest contracts and then some, have plumped up the market for international signees. The Diamondbacks’ Yasmany Tomas, therefore, should not be compared to Abreu simply because the average annual values (AAV) of their contracts are almost identical. The dynamics of this particular market are nebulous, changing with every transaction.

But that doesn’t mean we can’t compare Tomas and Abreu statistically. Comparing the CNS and MLB performances of hitters more recently signed out of Cuba can still give us at least a faint idea of how we can expect Tomas to perform. This is my hope, at least. I’ll be the first to admit the analysis that follows is not as rigorous as I wish it could be, as the sample of contemporary, fantasy-relevant Cuban hitters who recently played in the CNS simply lacks breadth.

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I’d like to talk to you about Dellin Betances.

Wait! Wait. No. No, I wouldn’t. I’d like to talk about Mike Podhorzer first. Mike has published a lot of great work covering the fundamentals of the xK% (and xBB%) metric for pitchers (and hitters), so if you are unfamiliar with or falling behind on his work, I recommend you first click here, here or here. But if you’re lazy, the short of it is: xK%, or expected strikeout rate, is an equation birthed from a linear regression that measures how a pitcher’s looking, swinging and foul-ball strike rates as well as overall strike percentage correlates with his strikeout rate. It doesn’t predict future strikeout rates as much as it retrospectively adjusts past strikeout rates; thus, it is a good tool for identifying pitchers who potentially benefited (or suffered) from good (bad) luck in a previous season – say, 2014.

Like many other metrics completely unrelated to xK%, however, there is evidence that certain players consistently out-perform (or under-perform) what their xK% rates predict their actual K% rates should be. (Mike alludes to this trend in his quip about Jeremy Hellickson, a xK% underachiever, in one of the articles linked above.) Similarly to how a power hitter will post consistently higher ratios of home runs to fly balls (HR/FB) than a non-power hitter, or how Mike Trout will probably post some of the highest batting averages on balls in play (babip) in the league for years to come, it appears there is some skill, or perhaps a particular characteristic, inherent to pitchers who consistently best, or fall short of, their xK% rates.

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