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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06/30/20 

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NOTABLE TRANSACTIONS/INJURIES/RUMORS 

• Opt-outs: Joe Ross, Ryan Zimmerman, Mike Leake, Ian Desmond 
• Matt Kemp to COL on minors deal 
• Drafting prospects  

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We’re just going to freeform this and make it essentially a notes column. I’ll start by saying that not every move is influenced by the 60-game season. In fact, few are. A lot of them are influenced by a change in health or just more studying that led me to make a move.

RISERS

Biggest moves up the rankings since my February 28th list.

Rich Hill +65 to 64 – He was slated to be out until June with an elbow injury so now his full IL stint happened in quarantine and he’ll now be ready from the jump. He’s been great on a per inning basis the last four seasons, but will he stay upright throughout this mini-season?

Michael Kopech +30 to 82 – Returning from TJ, Kopech was expected to miss some time at the outset of the season with a late-May/early-June target. Like Hill, that time has passed and now he’s made the 60-man roster, though his role is unknown. For fantasy purposes, it’d be perfect if Kopech was paired with an opener so if he’s limited to 3-4 innings, it comes in the middle innings and puts him in line for wins.

James Paxton +27 to 21 – 100% injury-related.

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With baseball officially inching closer to an actual season, it is time to update draft ranks for those of you that are like me and still have leagues that still need to draft. I will likely be updating each set of ranks I release once or twice before opening day as news of the schedule, players opting out of the season, and other information could shake things up tremendously. However, here are my current starting pitcher ranks as of now. (Beginning of tiers are highlighted in blue)

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Here they are!

I’ll have a whole column of notes on different guys on Monday. I just wanted to get the ranks out and we can start discussing them in the comments. This 60-game season is going to be so wild!

Previous list for comparison, though comparing a full season list to a 60-game one is tough.

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My initial goal was to determine the amount of variation in pitching stats in a short season. What I found was a stipulation filled mess. It should have been simple. Just take the first two months and compare how the pitchers performed to a full season. The short answer is that they did great because they pitched in cooler weather and were 100% healthy. Instead, should the results from August and Septemeber be used, by that point in the season, many had broken down and the breakouts (e.g. Lucas Giolito) emerged. There is no perfect way to answer my original idea, so I’ll try to provide several possible answers.

To limit the focus, I’m going to implement the following guidelines. It’s a lot and when I was setting them, I was questioning any possible findings. By changing any one of them, the process to find the results and the actual final results differ.

• Assumed a 12-team league and used SGP (Standing Gain Points) equation from The Process.
• I used historic Steamer projections to set the preseason valuation.
• I only examined WHIP and ERA. Most of the hot takes I’ve heard involve not wanting to deal with the possible variation in these rate stats.
• Ignored closers. They are their own beast.
• Focused on the 7 starters for 12 teams.
• Used April to May data and then August to September. Both aren’t ideal but the differences can then be analyzed.
• Anyone who didn’t pitch during the two-month time frame got zeros across the board.
• I just did 2019 and kept the mess to one season.

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