07/20/20

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2020 STARTING PITCHER RANKING REVIEW

• Reviewing my latest SP Rankings (here)

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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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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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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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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 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:

That's not that interesting. What I was more interested is its relationship to NEXT YEAR's wOBA.

Not surprisingly, the performance of the 5% hardest batted balls is what indicates talent in the following year, and by a huge amount. If you've been following my barrels thread.. pic.twitter.com/hvLWr0rCSr

— Tangotiger 🍁 (@tangotiger) February 27, 2020

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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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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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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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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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

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 2^nd 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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