Archive for Starting Pitchers

The Sleeper and the Bust Episode: 832 – SP Ranking Review

07/20/20

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2020 STARTING PITCHER RANKING REVIEW

  • Reviewing my latest SP Rankings (here)

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Waiver Wire Targets: Preseason

Note: If you read this on Saturday evening, I’m likely to add a few names as I do some more research and more news rolls in.

Projecting this season’s FAAB is going to be a nightmare. In past seasons, the process seemed fruitless at times but it’s going to be even more of a mess this season. Most leagues are giving teams the same amount of FAAB to cover a third of the season that will lead to some high dollar desperate bidding. Additionally, when a league was drafted matters. For instance, I have two leagues running FAAB tomorrow. The one from early March I need to clean up (e.g. one had Trey Mancini) and the other I drafted last so I may gamble on some different bullpen arms.

In this article, I’m going to at least cover the players in demand using CBS’s (40% or less ownership) and Yahoo’s ADD/DROP rates. Both hosting sites have the option for daily and weekly waiver wire adds. CBS used a weekly change while Yahoo looks at the last 24 hours. Yahoo is a great snapshot of right now while CBS ensures hot targets from early in the week aren’t missed.

Additionally, I’m going to add anyone else I fill is appropriate.

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Updated Starting Pitcher Rankings

We’re a week away from baseball. I unloaded the chamber and dropped 181 names here (and probably still missed SOMEONE). As with the last update, I’ll have a podcast or column out on Monday covering specifics, but for today here are the rankings with the comparison to the last list.

Let’s talk about these rankings the comments!

(Blue indicates a new tier starting)

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Five Starting Pitchers to Buy Cheap

I think we all know that this is going to be a truly wacky real and fantasy baseball season. New MLB rules and only 60 games instead of 162 means a chance to adjust our strategies to try to gain an edge. Ratio categories are always volatile, even over a 162 game season, so in just 60 games, there gap between the league leaders and laggards is going to be even wider than usual. And the names of those leaders and laggards could be more surprising than ever before just because randomness and pure luck are going to play a larger role.

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ERA Estimators, Pt. III: Future

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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Launch Angle, Pitch Location, and What Pitchers Can(not) Control

I spend a lot of time bothering Connor Kurcon. He’s a smart dude with a certain intuition about baseball and a certain ability to apply that intuition to produce tangible results that invariably reflect his hypotheses. He devised Predictive Classified Run Average (pCRA), an ERA estimator that outperforms the big three (FIP, xFIP, and SIERA). He also created a dynamic hard-hit rate which, to me, was astoundingly clever and a superior accomplishment to pCRA (although maybe he disagrees).

Anyway, like I said, I bother him a lot, he tolerates me, we bounce ideas off each other. The journey starts there, with my incessant annoyance of him, but also it starts here, with this Tom Tango axiom: exit velocity (EV) is the primary predictive element of hitter performance (as measured by weighted on-base average on contact, aka wOBAcon) — significantly more so than launch angle (LA). Some of the inner machinations of Tango’s mind:

I won’t speak for Kurcon, but I think this finding helped guide his work on the dynamic hard-hit rate. I also think it inspired his foray into replicating this effort for pitchers or, at the very least, his attempts to determine the most predictive element of pitcher performance. Which leads us to this tweet that (spoiler alert) is actually not stupid at all:

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ERA Estimators, Pt. II: Present

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. Over three posts, I will recap and elaborate upon various talking points from the presentation.

If the previous post was an elementary look at the “big three” estimators (FIP, xFIP, and SIERA), I hope this one is a little more illuminating.

ERA Estimators, Part II: Present

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ERA Estimators, Pt. I: Past

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.

I hoped to make this content accessible to all levels of (fantasy) baseball fandom. With that in mind, the content throughout, but especially in this first post, may feel a bit remedial to the common FanGraphs/RotoGraphs reader. Nor do I claim this content to be necessarily original or expansive; the array of articles comparing and arguing the merits of the “big three” ERA estimators (FIP, xFIP, SIERA) and more is broad. You can find a wealth of information in FanGraphs’ glossary already, if not elsewhere.

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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National League Schedule Analysis

I usually don’t worry about schedule specific details during a regular season since so much can change in a month or two. This season is only going to last a couple of months, so it has some importance. I dug through all of the National League teams trying to find some stretches to stream players. I didn’t find a bunch of one to two-week stretches but I did come to some overarching themes.

I tried to digest as much of the information as possible and I’m sure I’ve missed something obvious. I started the analysis hoping to find a list of week-by-week targets to stream and came away with a new perspective.
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Weighted Plate Discipline Index (wPDI): 2019 Review

In my previous article, I gave an update on my Weighted Plate Discipline Index (wPDI) metric. wPDI arises from the core ingredients of plate discipline – looking only at zone rates, swing rates and contact rates.

An important distinction regarding wPDI, is that its sample size is quite a bit larger than other statistics. Many other stats are based on innings pitched, or even per plate appearance. The denominator of wPDI is pitches. While batter outcomes such as strikeouts and walks stabilize fairly quickly, wPDI can work even faster.

Let’s now take a look at the 2019 leaderboards for wPDI, to see if we can find some undervalued players.

Starting Pitchers

Starting Pitcher 2019 wPDI Leaderboard
Name IP wPDI
Blake Snell 107.0 .380
Chris Sale 147.3 .379
Gerrit Cole 212.3 .374
Justin Verlander 223.0 .373
Stephen Strasburg 209.0 .370
Zac Gallen 80.0 .365
Mike Clevinger 126.0 .362
Yu Darvish 178.7 .359
Max Scherzer 172.3 .358
Kenta Maeda 153.7 .357
Charlie Morton 194.7 .357
Lucas Giolito 176.7 .356
Patrick Corbin 202.0 .355
Luis Castillo 190.7 .355
Aaron Nola 202.3 .355
Kevin Gausman 102.3 .353
Jacob deGrom 204.0 .353
Collin McHugh 74.7 .353
Shane Bieber 214.3 .352
Jose Berrios 200.3 .352
Kyle Gibson 160.0 .350
Andrew Heaney 95.3 .350
Chris Archer 119.7 .350
Dylan Bundy 161.7 .348
Felix Pena 96.3 .348
Zack Greinke 208.7 .348
Robbie Ray 174.3 .348
Matthew Boyd 185.3 .347
Domingo German 143.0 .347
Joshua James 61.3 .347
Hyun-Jin Ryu 류현진 182.7 .347
Carlos Carrasco 80.0 .346
Jack Flaherty 196.3 .346
Dinelson Lamet 73.0 .346
Sam Gaviglio 95.7 .346
Jose Urquidy 41.0 .344
Tommy Milone 111.7 .343
Rich Hill 58.7 .343
Griffin Canning 90.3 .342
Kyle Hendricks 177.0 .342
James Paxton 150.7 .342
Sonny Gray 175.3 .340
Eduardo Rodriguez 203.3 .340
Frankie Montas 96.0 .340
Walker Buehler 182.3 .340
Freddy Peralta 85.0 .340
German Marquez 174.0 .339
Brendan McKay 49.0 .339
Francisco Liriano 70.0 .339
Trevor Bauer 213.0 .338
Miles Mikolas 184.0 .337
Alex Young 83.3 .337
Carlos Martinez 48.3 .336
Chris Paddack 140.7 .336
Ross Stripling 90.7 .335
Mike Minor 208.3 .335
Clay Buchholz 59.0 .335
Michael Pineda 146.0 .333
Noah Syndergaard 197.7 .333
Masahiro Tanaka 182.0 .333
Austin Voth 43.7 .333
Joe Musgrove 170.3 .333
Trevor Richards 135.3 .332
Gio Gonzalez 87.3 .332
Thomas Pannone 73.0 .332
Clayton Kershaw 178.3 .332
Tony Gonsolin 40.0 .331
Jake Odorizzi 159.0 .331
Caleb Smith 153.3 .331
Mike Soroka 174.7 .331
Max Fried 165.7 .330
John Gant 66.3 .330
Madison Bumgarner 207.7 .330
Minimum 40 IP

Above is the 2019 wPDI leaderboard for starting pitchers.

Blake Snell lead all starting pitchers in wPDI in 2019. The key to Snell’s success was his “out of the zone” plate discipline. In particular, Snell’s Outcome A (out of the zone, swung on and missed) was the 2nd highest of all qualified pitchers in baseball. In 2019, Blake produced a K% rate of 33.3%, the highest of his career. He logged a whopping 147 strikeouts in just 107 innings pitched. Both FIP and xFIP (3.32 & 3.31 respectively) agree that his 4.29 ERA last year was somewhat unlucky.

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