Author Archive

Mining the News (1/17/20)

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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Should I Care About Sprint Speed?

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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Should Projections for Astros Hitters be Tempered?

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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Are Catchers a More Volatile Than Other Position Players?

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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Playing Through an Injury Hurts Future Performance

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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Projection Altering Hitter Injuries

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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Mining the News (1/2/20)

It’s time to empty my notes and start clean for the new year. A “Mining the News” almost came out before the holiday break, so some notes are dated but still applicable.

Nomar Mazara owners shouldn’t be counting on fulltime at-bats from him next season since he’ll likely be on the strong side of a platoon.

In 574 plate appearances against southpaws, Mazara features a below pedestrian line of .231/.272/.361 to go with 15 homers, 19 double and 68 RBIs. Manager Rick Renteria expressed hope in getting Mazara going against left-handers, but as it stands now, Mazara could get the bulk of playing time vs. righties with someone such as switching-hitting Leury García facing lefties.

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Keuchel & Ryu Look to Disappoint

I going to examine how both Hyun-Jin Ryu‘s and Dallas Keuchel’s fantasy value changed since both signed over the last few days.

Hyun-Jin Ryu signs with the Toronto Blue Jays

Ryu finally pieced together a great season by staying healthy and throwing more innings (182) than any time since 2013 (192). While his strikeout rate was acceptable (23%), he dominated (2.32 ERA) by walking almost no one (3%) and in the juiced ball era, he limited home runs (0.8 HR/9) with a surge in groundball rate (50%).

I hate this move for Ryu’s value with every aspect being a downgrade from the Dodgers. He moves to the AL where he’ll face a DH more often. He goes to the hyper-competitive AL East. He transitions from a pitcher’s park to one that is neutral overall but gives up more home runs than average. Finally, he goes from a nearly average defense to one in the bottom third.

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Gonzalez and Teheran: BABIP Masters

Most times it’s easier to get an idea of how a veteran pitcher will perform because of years of examinable data. With Gio Gonzalez and Julio Teheran, they have tons of data and each has beat their ERA estimators for years with no obvious reason why. They’ve beaten luck for years. Both are free with Teheran going with an average draft pick of 352 and Gonzalez with pick 632, so cost isn’t an issue. So is either one worth a roster spot at no cost?

Gio Gonzalez signed by Chicago White Sox

Gonzalez posted a reasonable 3.50 ERA with a 1.29 WHIP in 87 innings of work with the Brewers. He’s been able to walk the fine line walking too many hitters (3.8 BB/9 in 2019 and his career) yet limiting hard contact (0.9 HR/9 and .277 BABIP for 2019, 0.8 and .293 BABIP for his career). While he used to generate a decent number of groundballs (54% in 2015), he’s no longer on either end of the batted ball spectrum. His pitches have some batted ball split with the change and curve over 50% GB% and four-seamer at 26% GB%. The combination is nothing special to explain the difference.
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Projection Busting Research Updated

Over the years, I’ve been working on how to fine-tune my player evaluation process. The following are six datasets that I’ve found useful I’ll not go into detail on any of them since I provide a link to the original article. The following is basically a referenceable data dump.

Note: I know there is a lot of content and when questions arise, make sure the area in question is obvious in the comment. Also, I’ll only answer questions here and not in the original articles.

Voit/Muncy All-Stars (link)

These are older AAA hitters who have shown signs of a breakout.

Voit/Muncy All-Stars
Name Position Age Team PA BB% K% GB% ISO
Adam Engel OF 27 White Sox 277 8% 22% 43% .194
Addison Russell SS 25 Cubs 119 12% 21% 38% .281
Andy Ibanez 2B/3B 26 Rangers 529 10% 17% 37% .197
Austin Dean OF 25 Marlins 282 10% 18% 39% .298
Billy McKinney OF 24 Blue Jays 154 14% 16% 35% .217
Breyvic Valera 2B 27 Yankees 348 10% 10% 34% .200
Bryan Reynolds OF 24 Pirates 57 12% 19% 38% .367
Cavan Biggio 2B 24 Blue Jays 174 20% 16% 30% .203
Chance Sisco C 24 Orioles 196 10% 22% 42% .238
Chas McCormick OF 24 Astros 225 12% 15% 37% .204
Cheslor Cuthbert 3B 26 Royals 219 8% 21% 39% .218
Connor Joe 1B/3B 26 Dodgers 446 16% 18% 42% .203
Cristhian Adames SS 27 Giants 165 12% 19% 42% .234
Daniel Pinero 3B/SS 25 Tigers 110 16% 23% 32% .220
DJ Stewart OF 25 Orioles 277 14% 18% 41% .257
Donnie Dewees OF 25 Cubs 419 10% 15% 41% .207
Esteban Quiroz 2B/SS 27 Padres 366 14% 22% 38% .268
Harrison Bader OF 25 Cardinals 75 11% 21% 26% .381
Jason Vosler 3B 25 Padres 426 11% 24% 37% .232
Jaylin Davis OF 24 Giants 117 12% 24% 40% .353
Jeimer Candelario 3B 25 Tigers 178 12% 20% 42% .268
Johan Camargo SS 25 Braves 64 8% 19% 35% .207
Jonah Heim C 24 Athletics 119 9% 15% 34% .198
Jose Rojas 3B 26 Angels 578 10% 23% 31% .283
Josh VanMeter 2B/3B 24 Reds 211 11% 18% 38% .320
Kevin Cron 1B 26 Diamondbacks 377 16% 20% 26% .446
Mark Payton OF 27 Athletics 447 10% 17% 35% .319
Matt Thaiss 1B 24 Angels 372 16% 17% 42% .203
Michael Brosseau 3B 25 Rays 315 11% 18% 40% .263
Michael Perez C 26 Rays 216 13% 24% 36% .250
Mike Ford 1B 26 Yankees 349 13% 16% 40% .303
Nick Dini C 25 Royals 213 10% 14% 33% .269
Nick Tanielu 2B/3B 26 Astros 503 9% 17% 36% .225
Oscar Mercado SS/OF 24 Indians 140 11% 23% 40% .202
P.J. Higgins C 26 Cubs 140 12% 21% 40% .231
Phillip Ervin OF 26 Reds 172 11% 20% 31% .193
Roberto Pena C 27 Angels 155 11% 19% 32% .196
Ronald Guzman 1B 24 Rangers 135 13% 23% 39% .197
Rowdy Tellez 1B 24 Blue Jays 109 13% 23% 34% .323
Ryan McBroom 1B 27 Yankees 482 12% 21% 38% .259
Ryan O’Hearn 1B 25 Royals 149 11% 21% 39% .302
Taylor Jones 1B 25 Astros 531 13% 21% 37% .210
Taylor Ward C/3B 25 Angels 512 16% 20% 38% .278
Ty France 1B/3B 24 Padres 348 9% 15% 31% .372
Will Smith C 24 Dodgers 270 15% 18% 28% .335
Willie Calhoun 2B/OF 24 Rangers 172 19% 14% 33% .232
Yermin Mercedes C 26 White Sox 220 11% 19% 28% .337

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