I semi-recently had the honor of presenting at PitcherList’s PitchCon online conference to help raise money for Feeding America. My presentation, “ERA Estimators: Past, Present, and Future,” discussed, well, exactly what it sounds like it discussed. Over three posts, I will recap and elaborate upon points made in my presentation.

In the first two parts of this series (1) (2), I reviewed every manner of estimator, from the classics (FIP, xFIP, SIERA) to new-fangled doohickeys (Baseball Prospectus’ DRA, Statcast’s xERA, Connor Kurcon’s pCRA, Dan Richards‘ FRA). Today, we march forward, envisioning a future that may already be upon us.

ERA Estimators, Part III: Future

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I semi-recently had the honor of presenting at PitcherList’s PitchCon online conference, which raised a good chunk of money for Feeding America. My presentation, “ERA Estimators: Past, Present, and Future,” discussed, well, exactly what it sounds like. Over three posts, I will recap and elaborate upon various talking points from the presentation.

However, if this does happen to be your first exposure to ERA estimators or you are familiar with them but don’t necessarily understand their innards, then I hope you find this launching-off point beneficial.

ERA Estimators, Part I: Past

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One might argue that xFIP is slightly misaligned. One might make that argument in blog form, on the website FanGraphs, today, here, now.

One only might argue that xFIP is slightly misaligned because xFIP is commonly understood to serve a purpose distinct from FIP. FIP, aka Fielding Independent Pitching, is calculated as a function of strikeouts, walks, and home runs — that is, outcomes over which fielders bear no influence. The equation that underpins FIP is derived from a linear regression equation intended to resemble ERA, for ease of interpretation. Because it is based exclusively on outcomes, its purpose is more descriptive than predictive. In other word, it finds greater purpose describing what should have happened but not necessarily what will happen.

xFIP, on the other hand, seeks to achieve the inverse. A large swath of evidence exists to suggest home run-to-fly ball rate (HR/FB) for pitchers is incredibly noisy season to season. Sure, certain pitchers might anecdotally buck the norm — apparently, Michael Pineda was born to be a cafeteria lunch lady, serving up meatballs and taters — but, by and large, HR/FB is a fool’s errand to predict. Accordingly, xFIP replaced home runs with expected home runs, by way of multiplying the number of fly balls allowed by a pitcher by the league-average HR/FB, thereby normalizing home run damage, making it, in theory, a better descriptor (and perhaps a better predictor) of pitcher performance over time.

And therein lies the rub, although, if you missed it, you mustn’t be blamed.

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It seems as though each year, fantasy baseball analysts, “professional” and amateur alike, hone in on a new — or, if not new, then relatively untouched — metric or data set for their endlessly eager consumption. In 2015, FanGraphs introduced batted ball data to its leaderboards. In 2016, Statcast data was unveiled, although it arguably didn’t become popular until 2017, and before the 2017 season FanGraphs changed the game with its splits leaderboard. Baseball Prospectus has introduced myriad new metrics, too — DRA in 2015, DRC+ last year, etc. — and we began to lean into pitch-specific performance analysis last year. (The latter-most topic is relevant to what follows here.)

I recently joined Christopher Welsh and Scott Bogman of In This League on their podcast. I thought one of the evening’s questions was particularly topical and prescient (and I paraphrase): What will 2019’s it metric be? The question was asked with pitch values, something I’ve seen garner increasing attention on Twitter, in mind.

You can acquaint yourself with pitch values directly from the man who created them:

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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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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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I like a lot of pitchers who have unperformed this year. With strikeout and walk rates (K%, BB%) of 20.5 percent and 7.0 percent, respectively, Drew Hutchison delivers everything I want from a mid-rotation fantasy starter. With a 5.19 ERA and a 1.47 WHIP, however, he delivers a flaming bag of feces to my doorstep.

The same can be said for Taijuan Walker who, after a terribly rough start to the season, dazzled for seven straight starts before recently tossing three stinkers. With plate discipline ratios better than Hutchison’s and just 22 years old, Walker demonstrates the skill set and ceiling that have earned him consensus top-20 honors on prospect lists from 2012 through 2014. Yet his 5.06 ERA and 1.29 WHIP have left fantasy owners not only disappointed but also reeling.

Hutchison and Walker share a common trait: their ERAs dwarf their fielding independent pitching (FIP) statistics. FIP was designed to demonstrate a pitcher’s true performance in light of the events he can control — that is, events independent of balls put into play at the mercy of the defense supporting him (among other things).

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It took a dominant 11-strikeout, zero-walk performance from Taijuan Walker for the fantasy world to finally take notice of him. I don’t have ownership trend data to exemplify this, but I do have an anecdote: he was available in every league I’m in before the start, and he was owned in every league I’m in shortly after it.

The truth is Walker had demonstrated progress, described here by Eno Sarris, in his prior four starts, notching 27 strikeouts to three walks in 29 innings. Someone who hadn’t been paying attention to Walker probably wouldn’t have noticed: his ERA prior to the recent five-game surge stood at 7.33, and he had completed the sixth inning only twice in nine games. Once a hyped prospect, he looked like a 22-year-old who still needed seasoning to reach his potential.

No longer, as you will probably have to give up an asset of value to acquire Walker from a fellow owner now. The price may not be too steep given his poor ratios (4.94 ERA, 1.39 WHIP), but this is likely the highest they’ll be for the rest of the season.

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Contributing to RotoGraphs’ Bullpen Reports last year brought me much joy: opening infinite Brooks Baseball player cards for sabermetric outcomes and watching glorious GIFs. Oh, Marcus Stroman’s Two-Seamer:

GIF: Marcus Stroman’s Two-Seam Fastball from last week’s outing. It may be our favorite of the year – http://t.co/vyJM8ltUir

— Pitcher List (@ThePitcherList) August 31, 2014

Mmm.

My last Bullpen Report from October looked at possible closers through outcomes and presented BABIP differentials (actual BABIP versus expected BABIP using Inside Edge data).

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