I don’t know for sure where I read or heard it, but an analyst mentioned they would never go near Luis Severino this season in drafts because of last season’s major shoulder injury. This claim is something I can investigate on the surface to see if anything sticks. Besides the numbers for shoulder injuries, I included the pitchers with elbow injuries.

For the analysis, I took the players who were on the IL for a shoulder or elbow injury in year one and then compared their next season projection and results. I had matching data going back to 2010 but didn’t use 2018 IL data because for some reason I didn’t clean up the last year’s data. Read the rest of this entry »

• Here is a loaded Tweet:

Dave Martinez revealed Friday the clearest picture yet of the Nats’ plans this year. They’re betting on Carter Kieboom at 3B and…
• Starlin Castro everyday 2B
• He thinks Zim reunion will happen
• Lineup shuffling could include Trea Turner hitting 3rdhttps://t.co/ziJg1M5s6d

— Sam Fortier (@Sam4TR) January 17, 2020

Starting with Turner, I think this gives him a bump in value. His Run-RBI mix will be closer to 1:1. Additionally, I compared all hitters projected for 30+ steals and how often they attempted steals from the first and third lineup spots in the same season. The drop was between one and two stolen bases. It’s a change but nothing to get too worked up about. The like 50 extra RBIs is a much bigger deal.

Also, with Starlin Castro at second and Carter Kieboom at third, Asdrúbal Cabrera’s fantasy value tanks.
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Since I’m starting drafts, I decided I needed projections for seven of the players signing from Asia, either new to the MLB or returning. I could just pull a ranking out of my ass, but I figured I should at least start with a projection before inserting my own biases. For the following projections, I averaged the ZiPS and Clay Davenport projections and then add my own playing time adjustment.

Pitchers

Pierce Johnson
From the NRB
Signed with the Padres

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I’m finally able to mine a few useful bits of information with players, coaches, and owners talking at Fan Fests and caravans.

• Maikel Franco played through a hand bruise last season. When I collected information on players who played through an injury, he didn’t come up. The injury happened in early August and initially, his production suffered (.572 OPS). It bounced back in September (.703 OPS) hopefully meaning it’s not major.

• Brandon Nimmo is another hitter I missed who played through an injury.

Dragging down the above numbers is Nimmo’s performance last April and May, when he played through a bulging disk in his neck.

• Also, while investigating all the hitters who played through an injury, I found this nugget on Matt Carpenter from 2017.

Carpenter sat out the Cardinals’ final three games and underwent a follow-up MRI to the one he had a month ago. The exam showed the same thing now that the doctors knew then, which is that Carpenter is dealing with inflammation and not a structural issue.

Since then, he has missed a considerable amount of time with back injuries. I just can’t pay for any kind of rebound with what seems to be a chronic injury.

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Sprint Speed values have been publicly available for a few seasons. While I see it mentioned for this or that, I don’t know how predictive it is or if should I care about it at all. After analyzing the data, Sprint Speed might need to be ignored in favor of Time-to-First. The stopwatch still rules.

The key, in my opinion, is if the ability to run fast can be predictive in any way. No one that I know of is playing in a Sprint Score league, so the speed with have a secondary effect. If a player is running slower, do their stolen bases drop? How about how many infield hits they can leg out? Generally, how will the players change in speed affect their stolen bases and batting average.

One factor to keep in mind is that the aging curve for stolen bases is just a drop with all humans reaching their peak sprinting speed in their early 20’s.  There are going to be a lot of negative speed values coming up but that’s just aging pulling players down.

A second factor to remember is that teams are not allowing hitters to run as much. In 2015, there were over 2500 stolen bases league-wide. Last season, the value was under 2300 for a 9% decline. Again, more negative numbers.

Sprint Speed was first introduced in 2015 at Baseball Savant (links to Time-to-First values) and it is widely cited. Sprint Speed is not the only measured speed metric available. For one fewer season, Baseball Savant has each hitter’s run times to first base which have been the traditional measure of a player’s speed and it’s still used in scouting players. With the two metrics, it’s table time to what conclusions can be drawn.

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Yesterday, I noticed someone slid into my DM’s and I got my hopes up but instead I got this:

Maybe. Many of the Astros players didn’t think it helped.

Some Astros players told my investigators that they did not believe the sign-stealing scheme was effective, and it was more distracting than useful to hitters.

We just don’t know for sure of the effects of cheating so I guess I better take a stab and find out.

To start with, I went to the projection sources to find out how the projections weigh each year’s results. The weighted averages, along with some aging adjustments and regression, create the final projections. ZiPS is up first.

Dan Szymborski uses individual weightings for each component (strikeouts, doubles, etc) but at the end, the weighting is close to 8-5-4-2 where ‘8’ is the last season. According to the commissioner’s report, the Astros “only” cheated at home in 2017 so only 2 units (half of four, the third value) of the weighting will be boosted. The percentage of the projection’s input from the cheating is 10.5% (2/[8+5+4+2] or 2/19).

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How to correctly value catchers has always been up for debate. As a position, they are by far the worst hitters in the league. Last season, catchers posted a .713 OPS while second basemen were next with a .745 OPS. Most player calculations give catchers a valuation boost to attempt to even them out with the other hitters. This theoretical boost always exceeds the actual cost paid by draft picks or auction dollars. I assumed some experts knew more than me (safe bet) and the catcher projections have a wider range of outcomes. Owners don’t want to pay for these gambles. By examining historical values, I found the complete opposite. Catcher projections are the least volatile.

I’m not going to rehash why catchers need a positional adjustment. I’ve written a whole section on it in The Process. For anyone new to the subject or just wants a refresher, Ariel Cohen wrote an in-depth article on the subject.
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I was wrong. About seven years ago, I wrote on how hitters may overperform their projections since they played through an injury. The injury hampered their production in the season in question, lowered the future projection, and created a buying opportunity. For years, I believed this steadily until last season when I re-ran the numbers and found “jack squat”.

Earlier this week, I examined some of this past season’s hitters who fought through the pain and felt a deeper analysis was needed. I dove in and the results were backwards. I found no bounceback should be expected from hitters who played through injuries, but there is more. For those hitters who play through the discomfort, their future production will take a major hit.

The key to uncovering the following results was getting a usable dataset which is easier said than done. Many of the injuries I’m using for the analysis aren’t well documented, if at all. Real men play baseball and they play hurt because that is what real men do and most importantly, they don’t complain about. Besides the machismo, a player has every right to keep his medical data to himself so vagueness thrives. Simply, there is no good available data. Even with the hurdles, I dug into each of the hitters who were reported to have played through an injury the past three seasons (2017, 2018, 2019).

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I’ve had a semi-fixation on hitters playing through injuries and how the diminished production could hamper the next season’s projection. At first, I found some correlation. Then, I didn’t. One possible answer to there being no bounceback is that the injury becomes chronic and the hitter never improves. Or the dataset could be too small.

I want to dive further into the subject, but the information around injuries is sketchy at best. Most of the time, there are no usable details. The lack of an answer means that I should stop coming back to the subject but I’m stubborn.

Very.

I’m going to go through this past season’s hitters. The dive has a couple of goals. One is to create a better dataset for future reference. The second is to understand why some hitters may have struggled when creating a profile. And just maybe, I’ll find out if I can put to rest the notion that hitters who played through injuries are under projected.
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