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 »
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 »
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 »
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.
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 |
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 |
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 |
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
Here are two more groupings by HR/9 and BABIP.
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 |
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 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.
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.
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.
While I didn’t come to any groundbreaking information, I found what not to believe and hopefully, I can improve the future results.
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 »
7:33 |
: 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.
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7:34 |
: Here are the winning bids from the two 15-team Tout Wars leagues.
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7:34 |
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7:34 |
: Myles straw was dropped. What do u think about him for speed. He is pretty empty otherwise
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7:35 |
: He plays and steals bases. For some managers, there will be helpful.
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7:35 |
: What kind of bid are we looking at for Bobby Miller in ME leagues where he’s available now?
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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 »
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 »
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 »
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 »
7:31 |
: Welcome
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7:32 |
: Here are the bids from one of the Tout Wars mixed leagues.
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7:32 |
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 |
: And from the other one:
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7:34 |
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 |
: Sorry for the change of format. I’m not on my normal computer.
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