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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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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With most teams planning to limit the innings their starters throw, there are going to be a few middle relievers who bridge the gap to the seventh to ninth-inning guys. Because most starters will not go five innings, these bridge relievers will have the chance to accumulate a few Wins while hopefully providing decent ratios. Here are some targets.

Every season, some middle relievers go off accumulating half dozen Wins and Saves, great ratios, and over 100 strikeouts. They are more valuable than most starters and closers. The deal is that no one has a clue which middle reliever it will be, but whoever rosters them will be loving it. I decided to query a target list.
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Introduction

Last year, I introduced a new (yet simple) pitcher metric. Weighted Plate Discipline Index (wPDI) arises from the core ingredients of plate discipline from the point of view of the pitcher – control, deception, and contact.

wPDI looks at the following basic binary events:

• Was the ball thrown in the strike zone?
• Was the ball swung on?
• Did the batter make contact with the ball?

That’s all.

Weighted Plate Discipline Index (wPDI) does not look at generated bat speed, exit velocity, pitch speed, or quality of contact, etc. wPDI doesn’t even focus on walk rates or strikeout rates, or any other plate appearance result. wPDI focuses solely on the pure components of a pitch. Is the pitch in the zone? Is the batter swinging at pitches in the zone? Is the batter swinging at pitches outside of the zone? Is the hitter contacting the pitch?

That’s all.

In this series of articles, I will be refining and expanding upon what I had started last year. I will look at wPDI’s effectiveness and predictability. Along the way, I shall highlight both pitchers and hitters who catch our eye based on great (and poor) plate discipline performance.

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I had no plans to write or investigate pitcher injuries again. I’ve done it several times in the past with similar results. Since I needed the same information to investigate if an often injured pitcher ages faster as I did with hitters, I had the data available so why not take another stab at projecting pitcher injury risks with a few different inputs.

For a refresher, here are some of my previous findings:

• Previous injuries, age, and workload matter.
• A low Zone% is a precursor to injuries
• High fastball velocity, not pitch type, leads to injuries.
• Shoulder and elbow injuries can linger for a while.

The small data difference is that instead of limiting the IL days to the previous one to three seasons, I’m just using the accumulated days. Also, I tracking the number of times the pitcher went on the IL for an arm, elbow, or shoulder injury. The two different factors are joined by fastball velocity, Zone%, and age to see what leads to injuries the next season.

I took all the data from all the pitchers and ran it through a Trees analysis and got the following chart.

This image sums up pitcher injuries perfectly. The best predictor of future injuries is past injuries. And just because a pitcher had never been on the IL, on average, they will still spend ~18 days on it. It’s just re-enforcing common sense backed up by study after study.

From my previous work, the injury rates between starters and relievers (i.e. starters who can’t stay healthy) are drastically different. For that reason, I ran the analysis splitting out starters (GS/G >= .5) from relievers (GS/G < .5). Here are the two decision trees.

Starters

Relievers

We have some further division taking place fastball velocity on both, a further out 460-day threshold for starters, and age requirement. I’m not a huge fan of the multi-branched tree with many variables. I’m all for keeping it simple. Using the above variables, I cut and diced the data into several possible combinations and came up with the following divisions.

Again, the rates are similar to my previous findings. The only changes are the pitch velocity groupings and that rough over-460 IL day group who average two months on the IL a year. For me, I focus on starters and will designate the starters into three risk groups:

• Low: No IL, low velo.
• Medium: No IL, high velo, and some IL, low velo.
• High: Some IL, high velo, and high IL.

Not all injuries can be avoided, but the injury downside is just another factor to consider when setting each pitcher’s fantasy value.

And what’s a study without the players to consider for the upcoming season. Here are the historic IL days and fastball velocity for any pitcher with 10 starts last season.

• The pitcher with the highest velocity and IL experience is Noah Syndergaard. That 2020 IL stint didn’t take long.
• The oldest starter to never have been on the IL is Tanner Roark at 33.
• Darvish and Morton are going as the 17th and 18th pitchers even though they’ve broken the 460-day threshold.
• Just go and scroll through the starters who have been on the IL and throw over 93-mph, especially over 95. Lots of them have spent considerable time on the IL over their careers. I’m thinking to target “safer” but elite starters if given the opportunity like Corbin, Bieber, Kershaw, Castillo, and Flaherty. There is no way to completely stay away from the injury risk but why not add a pitcher with a 28% chance (Boyd) than someone with a 63% chance (Ryu)

These conclusions were about 80% in line with what I expected with fastball velocity nudging itself in. Next up will be taking this information and seeing if a higher injury rate ages pitchers more than projected.

One of my Launch Angle Podcast partners, Rob Silver, brought up how if there is a universal DH, the NL pitcher will no longer face ineffective bat-wielding pitchers. Simply, pitchers can’t hit. Last season in 4789 PA, National League pitchers hit for a combined .126/.157/.160. Our own Dan Szymborski continued the discussion to see if dominating pitchers hitting was a repeatable trait. I’m going to go a different route to investigate, using Dan’s information, how a pitcher’s projection would change going to an American League team (effectively including a DH) and this number affects a pitcher ranking.

I’m going to start off saying to not take any of the following information as the gospel truth. I’m trying to achieve a better projection that’ll be closer to the final outcome. Each stat and step in the process can be nitpicked along the way. I’m not even sure if the following method is the best way but it’s a way. I’m trying to move the discussion from “The DH will be a try breaker for me when drafting” (quote from a podcast I heard) to actually putting some number behind the possible changes.

Also, I’m not here to argue on why Jacob deGrom started out as the 8th ranked starter and he’s now 9th. I just collect a projection set. Anyone who uses stats to generate their projections will have their own secret sauce. I have my own. I just need a projection framework and live with it. Here is how I set it up.

I downloaded the 2020 ZiPS projections from here at FanGraphs. I used the ZiPS values since Dan created them and I’ll be using the inter-league adjustments provide in his article.

Then I changed each pitcher’s stats using Dan’s 2019 variables.

Variable: Change

• BB%: +4%
• K%: -5%
• AVG: +.007
• RC/G: +13%

I just adjusted the pitcher’s projected stats by the above values and created a hits estimate from the AVG with few assumptions.

NL pitcher innings projections have two offsetting values that could also be in play. The worse results could lead to fewer innings thrown (i.e. early hook) and the pitcher’s value could drop. On the other hand, the times a pitcher nearing his pitch limit will be replaced by a pinch batter will disappear. One of the two factors will likely dominate

Next, I used the 2019 12-team SGP (Standings Gain Points) formula from The Process to create pitcher valuations. The SGP value is the expected jump in the standings if that pitcher’s stats are added to a team’s stats. Here are results from the final top-40 starting pitcher using the SGP formula and ZiPS projections (I’m not sure why the TJS pitchers are still included but I don’t get paid the big bucks to know such things).

The changes are significant once all three factors (WHIP, ERA, strikeouts) are factored in. While the rank changes by just one or two with the top-10 or so arms, the difference becomes significant around pick 20 with moves of four spots. Maybe this change is a tie-breaker for some owners, but if an owner gains an extra ~1 SGP from all nine pitchers, it becomes nine spots in the standings. I think every owner would take those extra spots.

Just eyeballing the differences, it’s ~0.40 increase in ERA and 0.04 bump in WHIP to go with the 5% drop in strikeouts. The near half run increase in ERA will scare off quite a few owners by itself. Other owners will get blow off the possible changes, but in my current opinion, they will be playing catch up if they ignore them.

Again, don’t take my word for it … I’m still coming to grips with Lance Lynn possibly jumping Patrick Corbin. I could be wrong with these calculations but hopefully, some other analysts will step up and perform the calculations. The possible change in production is likely the biggest valuation change with half the pitchers facing legit MLB hitters instead of the irrelevant pitcher.

After working my way through the first eight rounds of the website ADP draft,  some sites leaned heavy pitcher while others were drafting just hitters. Before continuing the draft, I wanted to determine the split difference for each website. The difference wasn’t as big as I thought considering the results seen to this point in the draft.

Just for reference, I used the ADP data collected at FantasyPros from ESPN, Yahoo, NFBC, CBS, FanTrax, and Real Time Sports (RTS). Next, I assumed people were drafting a 12-team league with 23 players (14 hitters, 9 pitchers) which works out to 276 players. Then, I gave each player an auction dollar amount based on their ADP. Finally, I divided them into two groups and added up their auction dollars. Here are the results
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Let’s talk about the best prospect fastballs! These are the 9 pitchers with at least a 70 grade FB – Present (FBP) and 50 or higher FV grade for those with a 70 grade FBP.

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