Archive for SP

It’s not quite sh*tposting, but it’s close: I post a cryptic poll on Twitter and just let it do its thing. It is, to frame it in this week’s Internet meme jargon, my “beige flag,” my desire to sow chaos by dripping a drop of blood into shark-infested waters.

Here’s my most-recent artistic masterpiece:

miller

— Alex "Oxlade" Chamberlain (@DolphHauldhagen) June 11, 2023

It seemed like Mason, Bryce, and Bobby all tied or set some kind of record this season, each of them one-upping his predecessor from the the prior week or month or whatever it was. It’s all happening so fast, these Millers.

The poll went exactly how I expected: Read the rest of this entry »

Yesterday, I published 15 starting pitcher blurbs, just because. It involved a long prologue behind the what, when, and why of the matter. In short, I spent much of the offseason secluding my digital self from outside noise (fantasy baseball articles, Twitter opinions, etc.), forcing myself to develop opinions on as many relevant players as possible. I isolated my brain from outside analysis and biases, leaving me with only my own. I wanted to document my thoughts, both to have a point of reference during drafts and to have a record of those thoughts for accountability’s sake. If nothing else, be accountable to yourself.

At the end of yesterday’s article, I solicited recommendations from readers for more pitchers to feature. That, my friends, was an enormous mistake. Although I alleged I would pick names at random, instead I combed through 85 comments (and counting) and tallied up the most combined recommendations and up-votes. Those are the ones I’ll present here.

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This offseason I set out on a personal journey. Sentences like that usually precede stories of enlightenment, and I suppose this story features some enlightenment of its own. We won’t know how much enlightenment until after the 2022 season concludes.

I’m not asked this question often, but I have been asked it often enough to have a stock answer for it. It’s some variation of, “What’s the best thing someone can do to improve as a fantasy baseball player?”, to which my answer is some variation of, “Develop an opinion about every player—well, not every player, but you know what I mean.”

This is something that, when I had more time on my hands, I used to do. But in 2020 I became a work-from-home/stay-at-home father navigating a pandemic, and I burned out. Although I managed sporadic success and positive returns on investment in the 2020 and 2021 seasons, I was lucky to escape unscathed—I was all but flying blind. I was, and still am, fortunate to have years and years of watching baseball and playing fantasy baseball to have built an encyclopedic knowledge about most players.

It’s easy, however, to miss the big changes—the breakouts, the fall-offs, the rookies. It’s easy to dismiss them, and easier yet to enter a draft room and ignore them all together. Indeed, you can build a winning team without deeply investigating these types of players, instead relying on existing knowledge about existing players. You can do it, but it’s difficult, and it’s foolish.

So, I dedicated myself to the task of developing opinions about nearly every player.

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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 »

At the beginning of last season, I contextualized the swinging strike rate (SwStr%) (and refreshed those numbers after the season concluded). I had seen other analysts call certain pitches “above-average,” “below-average,” “elite,” etc. using the league-average whiff rate as a baseline. This is neither a criticism nor a judgment, as I absolutely did this before I had my statistically-driven epiphany. But understanding the average four-seamer’s or slider’s or cutter’s whiff rate lends additional context to any assertion one might make about the “elite-ness” of a pitch.

More recently, I wanted to convert discrete outcomes by pitch type into fielding independent pitching (FIP) statistics — namely, FIP and xFIP (expected FIP, which substitutes a pitcher’s rate of home runs per fly ball for the league-average rate). Let me warn you now: the results are very imperfect. It took some brute force on my part to get there, but I got there. I would wager that the the extreme (lowest and highest) values are probably a bit exaggerated. Regardless, it’s an interesting table to ingest:

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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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(Not to be confused with Jeff Zimmerman’s delightful Quick Looks.)

In terms of fantasy topics to discuss, I’ve been pretty unmotivated for the last month. I took to Twitter to solicit some ideas. Rather than letting myself procrastinate and become unmotivated about these interesting topics, I figured I’d knock a few out at once with some quick takes.

The re-emergence of Ben Zobrist

Or, conversely, the caving-in of the rest of the Cubs’ offense.

Sure, there have been bright spots: Javier Baez makes for a nice down-ballot MVP candidate, Kyle Schwarber is not a liability, and Jason Heyward is a non-zero with the bat for the first time since moving to Chicago. But for everyone else? Not so much.

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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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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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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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