Archive for reliever

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 »

A couple of weeks ago, I investigated Justin Verlander’s resurgence. I found reasons to validate his hot streak but turned up additional question marks along the way.

One of them was his zone contact rate (Z-Contact%). At 79.7 percent, it would have been the second-lowest of his career by several percentage points (despite not performing “at peak”). However, I realize now, unfortunately, that I must have encountered a glitch in the leaderboards — his Z-Contact% as of August 21 (because the post, despite running the same day as his Aug. 26 start, was published prior to it) was 85.7 percent.

Regardless, it got me thinking what affects a pitcher’s zone contact rate because it correlates very strongly with strikeout rate (R-squared = .594). User DoubleJ speculated about the metric via comment on one of last week’s posts:

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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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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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