I think by now, we’re all familiar with the Statcast metric barrels. If not, Statcast defines it as thus:

The Barrel classification is assigned to batted-ball events whose comparable hit types (in terms of exit velocity and launch angle) have led to a minimum .500 batting average and 1.500 slugging percentage since Statcast was implemented Major League wide in 2015.

To be Barreled, a batted ball requires an exit velocity of at least 98 mph. At that speed, balls struck with a launch angle between 26-30 degrees always garner Barreled classification. For every mph over 98, the range of launch angles expands.

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Some previous research, including my own, has pointed to changes in Sprint Speed being a sign a player’s talent has changed. I decided to investigate changes from 2019 to 2020 to find who could be struggling and why. Many hitters are experiencing a drop and just a few are up.

In some way, the following information should be picked apart for a longer research article with some miraculous/groundbreaking/made-up claim that batters came to this season out of shape and that is why offensive production is down. I don’t care. All I want to know is who are the hitters who could be breaking out or down. Figuring out why is for the offseason. It’s now time to wins leagues.
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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 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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Note: About 95% of this article was finished before the news that MLB is going forward with a 60 game season.  I finished it knowing that more imporant work needs to be done. This series now comes to an abrupt end and I will return to the series once the season is over one way or the other.

I’m continuing my quest to predict pitcher injuries and their effects as best as possible. I started grinding through the process last week and found through some additional work that injuries from just the past two seasons drag down production. Today, I’m going to go over some other possible other injury causes and provide updated injury ranks.

While I’ve done quite a bit of my own work on pitcher injuries, I decided to scour the web come up with some new ideas. Here are some possible ideas ranked by how I’d like to investigate them.
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With so much sabermetric work already completed, I’m creating a ton of work for myself to see if a hitter’s athleticism influences how they age? Additionally, do these “athletes” age better? I tried to jump the gun a few nights ago with an ill-fated Twitter thread where I thought about reverse-engineering the stats. Instead, I’m going to put a value on a hitter’s athleticism using some readily available metrics.

I began my search by using some advice from Bill James who commented on my Tweet.

Let's assert as Point A that "athleticism" consists of four things: Speed, strength, agility and quickness. "Strength" is the most troublesome, because you can have strength in many different body parts, and people who are strong in one area may be relatively weak in others.

— Bill James Online (@billjamesonline) June 7, 2020

He just rattled the traits off and since he’s likely forgotten more about baseball than I’ve ever learned, I’ll just focus on them. I’m guessing he’s already investigated the subject.
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First off I hate that the season could be so short and it’s total B.S. that the players and owners are still negotiating a season. They should have started once the season was shutdown.

Also, I can understand if an owner or league just wants to sit out this season. This is especially true since other sports who have their sh…stuff together will be playing meaningful games soon.

With those two caveats out of the way, I am interested to see how a short season plays out since none of us have a playbook for it. The owners’ 48-game schedule is even more intriguing if Manfred decides to immediately implement it. The major impact for fantasy owners will be the games per week pending on where he sets the season start date. Here is how those 48-games could get divided up.

If it’s six games a week, that doesn’t change many player valuations. Anything less than that, it gets interesting. With around 5 or fewer games per week, everything will have a playoff feel. Aces will be thrown at every opportunity and suspect starters will have shorter leashes, if they’re starting at all.

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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.

Predicting hitter injuries has been a fool’s errand for me. Besides players with chronic injuries (e.g. Albert Pujols and Ryan Braun), others and myself have made little headway in the field. With few guidelines, many fantasy analysts and owners handle hitter injuries differently. Previously, I focused on a hitter’s recent injury history. This time I attempted a different approach and used the hitter’s career IL days. In the end, I found a useful and easy to remember injury threshold.

For the study, I examined hitters from the 2010 to 2018 seasons. I have IL data going back to 2002, so I hoped the preceding eight years of data would get most of the hitter’s 2010 career total. Additionally, I needed the next season (e.g. 2019) to compare results. Additionally, I set a minimum hitting threshold (100 PA) to include at least some semi-regulars. I know I may miss a hitter who is out the whole season, but the two-week callups were diluting the results. In all, I ended up with a sample of 2365 player seasons.
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Note: This article was written in two parts, with the first half being before the draft and the second half after completion.

Prep

Tonight, I’m participating in a 1999 retro with several other industry analysts. The premise is simple: the owner drafts a 5×5 roto team based on the 1999 season’s final player stats. That’s where the simplicity ends.

I knew these drafts were going on but as I was busy with other projects, I hadn’t dived into them. Then Fred Zinkie contacted me to do an idiot check on his valuations for the draft that happened last Wednesday. I’m a fan of using SGP (standings gain points) for my normal evaluations, but none were available for 1999. Instead, I had to break out a copy of The Process and create the player valuations based on the Z-score method. (Z-score looks at how much a player’s real or predicted stats are above or below the league average for the draftable player pool. It takes several passes to get the correct valuations since the final player pool is unknown. The z-score method is helpful when no league history exists.)

After I was done, we noticed two differences. Fred used a modified SGP for the season and was valuing stolen bases a little more. We aren’t sure of the cause, but he gave catcher and middle infield a little more of a bump. I think the difference was from the different stolen base values. Read the rest of this entry »