Author Archive

Lineup Analysis (5/26/23)

American League

Angels

Taylor Ward and Mickey Moniak are splitting time in left field.

Luis Rengifo, Gio Urshela, Brandon Drury, and Jared Walsh are sharing three infield spots. Read the rest of this entry »


Waiver Wire & FAAB Report (Week 10)

Updated on Sunday morning. Added a few players and moved a few players around.

Note: Starting today (Friday), my Memorial Day weekend is planned out for me and I’m just along for the ride. Here are my weekly waiver wire ranks and I’ll update them when I have time. I’ll post the updates here at the top and in the excerpt to let people know if/when there have been any changes. I’m going to be very bare-bones with no blurbs, but I’ll add more info as I find time.

In the article, I cover the players using CBS’s (about 40% or less initial roster rate) and Yahoo’s ADD/DROP rates. Both hosting sites have the option for daily and weekly waiver wire adds. CBS uses a weekly change while Yahoo looks at the last 24 hours. Yahoo is a great snapshot of right now while CBS ensures hot targets from early in the week aren’t missed. The players are ordered for redraft leagues by my rest-of-season preference grouped by starters, relievers, and hitters. Read the rest of this entry »


What is Too Many Four-Seamers?

The question came up when I examined David Peterson. I wondered if he was getting hit around because he was throwing a ton of subpar fastballs. Today, I’m back-testing the theory.

I had no idea what I was going to find but the results, positive or negative, will help to shape future studies. I examined starters from 2021 and 2022 who threw at least 20 innings (n=201). I limited the time frame to include the STUFFF metrics that have only been around that long. Also, I limited this study to guys who threw their four-seamer more than their sinker. I started with just four-seamers and stayed away from sinkers. The STUFFF metrics are separated based on pitch type so I wanted to stay in one lane.

The narrative behind four-seamers (or any fastball) would be that batters would familiarize themselves with these fastballs. I know that bad fastballs won’t generate as many strikeouts but do they get hit around more, especially if that’s all batters see.

Additionally, I included my pERA values which is only based on if the pitch misses (SwStr%) and the direction it is hit (GB%). These values might seem high but I don’t scale the value based on pitch type and fastballs generate fewer swings-and-misses than non-fastballs. It’s time to start the journey.

First, I grouped the pitchers by how far their ERA estimator was from their actual ERA. Here are the results.

Four-Seamer Fastball Metrics Depending on ERA-FIP
ERA-FIP > 1 Between -1 and 1 < -1
BABIP .322 .286 .241
HR/9 1.5 1.2 1.3
K% 18.7% 21.6% 22.6%
FF% 42.5% 37.8% 34.4%
FF%/(FF%+SI%) 79.1% 78.4% 71.1%
FFv 93.1 93.1 92.9
wFF/C -1.26 -0.21 0.12
Stuff+ 86.4 91.9 94.9
Bot+ 47.6 52.4 50.0
pERA 4.82 4.67 4.68

 

Four-Seamer Fastball Metrics Depending on ERA-xFIP
ERA-xFIP > 1 Between -1 and 1 < -1
BABIP .310 .287 .254
HR/9 1.8 1.2 1.0
K% 18.9% 21.7% 22.9%
FF% 39.4% 38.2% 35.1%
FF%/(FF%+SI%) 77.9% 78.2% 76.9%
FFv 93.0 93.2 92.9
wFF/C -1.57 -0.19 0.76
Stuff+ 87.2 91.3 99.1
Bot+ 48.8 52.2 53.5
pERA 4.88 4.68 4.50

 

Four-Seamer Fastball Metrics Depending on ERA-SIERA
ERA-SIERA > 1 Between -1 and 1 < -1
BABIP .307 .287 .264
HR/9 1.9 1.2 0.9
K% 18.9% 21.8% 21.6%
FF% 39.7% 38.0% 36.6%
FF%/(FF%+SI%) 79.6% 77.5% 79.2%
FFv 92.8 93.2 92.7
wFF/C -1.51 -0.21 0.58
Stuff+ 87.4 92.0 93.4
Bot+ 49.2 52.4 51.7
pERA 4.87 4.67 4.58

 

Four-Seamer Fastball Metrics Depending on ERA-xERA
ERA-xERA > 1 Between -1 and 1 < -1
BABIP .309 .286 .276
HR/9 1.8 1.2 1.3
K% 18.9% 21.9% 19.8%
FF% 41.0% 38.0% 35.7%
FF%/(FF%+SI%) 80.1% 78.8% 70.8%
FFv 92.5 93.2 92.9
wFF/C -1.61 -0.13 -0.39
Stuff+ 85.2 92.6 88.6
Bot+ 47.1 52.7 49.2
pERA 4.83 4.65 4.86

There is a lot to unpack, but the biggest takeaways for me are

  • The pitchers with higher than expected ERA threw more fastballs on average.
  • The pitchers with higher-than-expected ERA generally had worse STUFFF.
  • The pitchers with lower-than-expected ERA mixed in more sinkers.
  • Fastball velocity didn’t matter. It still remains linked to strikeouts.

Here are two more groupings by HR/9 and BABIP.

Average Four-Seamer Fastball Metrics Depending on HR/9
HR/9 > 1.7 Between 0.7 and 1.7 < .0.7
BABIP .294 .285 .293
HR/9 2.2 1.2 .6
K% 18.1% 21.8% 23.8%
FF% 39.7% 37.7% 38.8%
FF%/(FF%+SI%) 79.3% 78.2% 73.8%
FFv 92.466 93.156 93.943
wFF/C -1.72 -.09 .40
Stuff+ 85.7 92.5 92.3
Bot+ 49.3 52.1 53.8
pERA 4.99 4.65 4.49

 

Average Four-Seamer Fastball Metrics Depending on BABIP
BABIP > .317 Between .253 and .317 < .253
BABIP .334 .284 .237
HR/9 1.3 1.3 1.2
K% 20.1% 21.4% 22.9%
FF% 40.5% 37.8% 36.0%
FF%/(FF%+SI%) 75.5% 78.9% 76.9%
pfxvFA 93.212 93.112 92.941
pfxwFA/C -.76 -.32 .50
Stuff+ 85.6 92.1 96.8
Bot+ 51.1 52.1 51.5
pERA 4.75 4.69 4.59

The results are a little messier but the conclusions are close to being the same.

  • The batters who got hit around threw a few more fastballs on average.
  • The pitchers who got hit around had worse STUFFF.
  • Fastball velocity or sinker/four-seam mix didn’t matter to over-or-under-perform batted ball metric.

The two major factors seem to be the usage rate and the STUFFF metrics.

After eyeballing the above tables, it seems like a usage under 40% along with a Stuff+ value under 90 and a Bot Stuff under 50. To see if these benchmarks work, I took the 2023 starters and grouped them.

 

2023 ERA-ERA Estimators for Starters Throwing Lots of Bad Four Seamers
Four-seam traits FIP xFIP SIERA
Usage >40%, BotStuff <50 -0.10 -0.19 -0.03
Everyone else 0.06 0.07 0.04
Usage >40%, Stuff+ <90 -0.12 0.19 0.17
Everyone else 0.06 0.06 0.04

The pitchers I expected to perform worse actually performed better. That’s suboptimal. I did find out what possibly didn’t work but it would be nice if the values were predictive. I ran one last comparison for future reference, here are the pitchers’ stats for if their ERA is above or below their ERA estimators so far this season.

 

2023 Stats for Grouped by ERA-ERA Estimator Above or Below Zero
ERA minus estimator FF% wFA/C BABIP HR/9 botStf FF Stf+ FF
ERA-FIP >0 40.2% -0.53 .320 1.4 47.9 93.6
ERA-FIP <0 42.6% 0.17 .268 1.3 49.7 96.6
ERA-FIP >0 41.2% -0.80 .318 1.6 48.0 92.7
ERA-FIP <0 41.5% 0.47 .270 1.0 49.5 97.6
ERA-SIERA <0 40.7% -0.86 .318 1.6 47.3 92.2
ERA-SIERA >0 42.1% 0.53 .270 1.0 50.3 98.2

The usage doesn’t matter this season but the STUFFF values show some signs worth continued investigation.

That’s enough failure for one article. Here is what I see needs to be done next.

  • Sinkers will be included by weighting the results by usage. David Peterson mixes in some (bad) sinkers so maybe the combination brings more clarity.
  • I’m going to attempt a fastball grade that takes into account the predictive values (STUFFF), pitch results (pERA), and batted ball results (pVAL). From some past work, I wasn’t a huge fan of pVALs but I think they might help show the possible disconnects between shape and results (e.g. ability to hide the ball).

While I didn’t come to any groundbreaking information, I found what not to believe and hopefully, I can improve the future results.


Big Kid Adds (Week 9)

While the NFBC Main Event garners most of the attention, there are a handful of leagues with even a larger entry fee ($2.5K to $15K). They get originally named “High Stakes Leagues” and this year there are nine of them. With so much money on the line, these fantasy managers are going to try to gain any advantage. Most of the time, these managers will be a week or two ahead of everyone else on their adds. Here are the players and some information on the ones added in four or more of these leagues. Read the rest of this entry »


Sunday Night Waiver Wire & FAAB Chat

7:33
Jeff Zimmerman: Welcome and a note about next week, there is a good chance there will be no chat since I’ll be traveling for Memorial Day. I might do a limited one earlier in the day.

7:34
Jeff Zimmerman: Here are the winning bids from the two 15-team Tout Wars leagues.

7:34
Jeff Zimmerman:

7:34
Need for speed: Myles straw was dropped. What do u think about him for speed. He is pretty empty otherwise

7:35
Jeff Zimmerman: He plays and steals bases. For some managers, there will be helpful.

7:35
BoB: What kind of bid are we looking at for Bobby Miller in ME leagues where he’s available now?

Read the rest of this entry »


Waiver Wire & FAAB Report (Week 9)

In the article, I cover the players using CBS’s (about 40% or less initial roster rate) and Yahoo’s ADD/DROP rates. Both hosting sites have the option for daily and weekly waiver wire adds. CBS uses a weekly change while Yahoo looks at the last 24 hours. Yahoo is a great snapshot of right now while CBS ensures hot targets from early in the week aren’t missed. The players are ordered for redraft leagues by my rest-of-season preference grouped by starters, relievers, and hitters. Read the rest of this entry »


Lineup Analysis (5/19/23)

American League

Angels

Taylor Ward (.649 OPS) has sat in two of the last four games.

Astros

Chas McCormick (.762 OPS, 2 HR, 4 SB) has started all four games since coming off the IL.

Jose Altuve is about to come off the IL so Mauricio Dubón (.723 OPS) will need to find a spot in the lineup. Read the rest of this entry »


Strikeout Rate’s Link to WHIP

I’m still in disbelief from a recent finding I made. It started with this comment in a recent article I wrote about STUFF:

How much WHIP changed in the two “Stuff” models was almost too good to be true. In both cases, the walk rate increased as a pitcher’s stuff got better, but the hit suppression was so large that the WHIP declined.

Well I was wrong about the hit suppression. I went back and found no link to BABIP. The difference was because WHIP is on an innings denominator and a strikeout removes the chance for a Hit and Walk. An out comes down to the random chance of a batted ball. I know it’s confusing so here is an example assuming a pitcher with a 9 K/9, 3 BB/9, and .300 BABIP and throws 6 IP/GS. Read the rest of this entry »


Big Kid Adds (Week 8)

While the NFBC Main Event garners most of the attention, there are a handful of leagues with even a larger entry fee ($2.5K to $15K). They get originally named “High Stakes Leagues” and this year there are nine of them. With so much money on the line, these fantasy managers are going to try to gain any advantage. Most of the time, these managers will be a week or two ahead of everyone else on their adds. Here are the players and some information on the ones added in four or more of these leagues. Read the rest of this entry »


Sunday Night Waiver Wire & FAAB Chat

7:31
Jeff Zimmerman: Welcome

7:32
Jeff Zimmerman: Here are the bids from one of the Tout Wars mixed leagues.

7:32
Jeff Zimmerman: CMorel 180
DFletcher 54
CSchmitt 48
LOrtiz 48
MMoniak 44
BMiller 32
MLorenzen 22
MWacha 22
DPeterson 21
CSilseth 20
GSoto 17
TanScott 17
HBrazoban 15
AIbanez 14
SDominguez 13
WPeralta 11
PSmith 11
FFermin 9
RGrossman 7
JOviedo 7
PDeJong 5
JDiaz 1
CStratton 1
KKiermaier 0

7:34
Jeff Zimmerman: And from the other one:

7:34
Jeff Zimmerman: EPerez 310
CMorel 188
CKimbrel 105
DFloro 69
DFletcher 47
NPratto 45
TTaylor 36
LTaveras 35
LOrtiz 27
GCanning 26
HHarvey 25
MWacha 25
BBelt 25
JPCrawford 24
MCastro 22
CSchmitt 22
RGrossman 18
DPeterson 18
CSilseth 12
MMcLain 8
PBattenfield 4
KFarmer 1
MThaiss 0
GSoto 0
JBauers 0
ERosario 0

7:34
Jeff Zimmerman: Sorry for the change of format. I’m not on my normal computer.

Read the rest of this entry »