Archive for starting pitcher

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 »

Coming into the 2020 season when anyone would mention the Cleveland Indians rotation you would have automatically thought of Mike Clevinger, Shane Bieber, Carlos Carrasco, and maybe even Aaron Civale. No one thought anything of Zach Plesac. Yet here we are two and a half months later and Zach Plesac had an ADP of pick 79 in the “2 early mocks.” Plesac balled out in this shortened season pitching 55.1 innings with a 2.28 ERA and 24.8% K-BB%.

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SwStr% is a simple metric that is calculated by taking swings and misses and dividing it by total pitches. Why is SwStr% important? Simply put, if a pitcher can produce a bunch of swings and misses it means his strikeout rate should be high. The more strikeouts the better, because if you look at an elite pitcher in baseball you will see a high strikeout rate. It is well known that SwStr% correlates well with a pitchers strikeout rate. Want to know if a player’s K% is over or underperforming? Check out their SwStr%. The rule of thumb (although it isn’t exact) is to double a pitchers SwStr% and their K% should be around that number. Keep in mind some pitchers will be outliers if they consistently rely on called strikes, like Aaron Nola.

Let’s take a look at the SwStr% leaders so far this season.
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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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(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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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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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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I’m already thinking about 2018. It’s not that my teams are doing poorly; they’re fine, for the most part. It’s that the economist nerd in me, when thinking abut fantasy baseball, most often evaluates the disparities between perceived and actual values, and how long, if ever, it takes for the market (aka fantasy owners) to come to equilibrium, to use economic parlance.

For example: you may or may not be aware that Kevin Gausman, despite his atrocious start to the season, has been magnificent the last five weeks. In seven starts from July 2 onward, he’s posted a 3.24 ERA (supported peripherally by a 2.81 xFIP and 3.40 FIP) with 11.4 strikeouts and 2.6 walks per nine innings. The strikeout rate is fueled by a 15% swinging strike rate (SwStr%), which have come consistently, ascending into double-digit percentages in all seven starts (and in eight of his last nine). His strikeout-to-walk differential (K-BB%) by month: 2.0%, 8.8%, 9.2%, 23.4%, and, in one August start, 28.0%.

Meanwhile, he’s inducing ground balls almost half the time (49.5% GB). You could say he’s due for batting average on balls in play (BABIP) regression, and he probably still is. His BABIP constantly hovering above .349 does not inspire confidence, but few pitchers have ever been BABIP’d so hard in a single season — I discussed this phenomenon in regard to Robbie Ray. All said, while there’s no guarantee his BABIP regresses before October, Gausman still shows the promise we once expected of him — perhaps more — and it’s going largely unnoticed because of his downright repulsive first half. (He’s baseball’s #12 starter the last month.)

Such is the gist of this post, in which I’ll briefly touch upon players I anticipate to have average draft positions (ADPs) in 2018 that will lend themselves to relatively low-risk, high-reward opportunities in standard mixed leagues. Whether such expectations become reality is another story; that’s why I’m relying on ownership levels as a proxy for perceived value. All ownership levels likely retain some amount of draft day inertia, for better or for worse — in other words, leftover ownership (or lack thereof) in abandoned leagues — so take it all with a grain of salt.

Please note this is, by no means, an exhaustive list — just the first few players who come to mind, mostly because I’ve paid close attention to them all season.

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