| 7:29 |
: Sorry for the late notice. First, here are this week’s FAAB reports for the two 15-team Tout Wars leagues: |
| 7:29 |
: Auction |
| 7:29 |
: JSoto: 412 |
| 7:29 |
: Draft: |
| 7:29 |
: JSoto: 235 |
| 7:30 |
: I’m buying Lyles where possible. His pitch mix has changed (dumped sinker) for the better. |
After getting some internal and external feedback, I’m going to be posting a league-wide lineup evaluation every Friday (or possibly Saturday). The goal is to help owners find targets for the weekend waiver wire and help keep my weekend free. I will focus on just lineups and stay away from position changes. Also, if nothing is written about a team, nothing has changed in the past week.
Angels
Zack Cozart has replaced Ian Kinsler at the top of the lineup and moves down a few spots when Shohei Ohtani hits.
Astros
Alex Bregman is back at the second spot after hitting .282/.404/.564 over the past 14 days.
Evan Gattis is starting to hit better (.276/.300/.621) over the past two weeks. It might be good time to buy low.
Athletics Read the rest of this entry »
A few days back, I noticed Domingo German’s fastball velocities and spin rates were unique. Since starting, his sinker (or two-seamer) was faster and had more spin than his four-seam fastball. This setup rarely ever occurs. Pitchers try to get as much velocity and spin as possible on their four-seamer with the intention of generating swings-and-misses up in the strike zone. A sinker usually has less of both to help create sink and the resulting groundballs. Throwing a higher spin sinker than a four-seamer historically is not helpful.
Normally, the other Jeff examines one-offs, but I was dumbfounded when I saw German has this feature. Here the two pitches in action.
Sinker
Four-seamer
With our auction calculator update, I going to look at three top-rated batters.
In my recent Launch Angle podcast, I had a tough time deciding if I’d draft Ozzie Albies or Jose Altuve in a new 2018 league. I decided I’d go with Altuve but being that I had to think about it, I needed to see if I buy Albies’s ascension into a possible first or second round talent.
Others and myself had an idea Albies was going to be good but just not hit for so much power. If someone would have told me to predict six and 13 for Albies’s home runs and stolen base totals, I would have swapped them. Last year, he had 15 HR and 29 SB. The season before, it was 6 HR and 30 SB. I figured the speed was safe (and it is) but his power may come later. I was right, he isn’t hitting for more power, he’s utilizing his it better.
In my last article, I did a Quick Look at Freddy Peralta and found his fastball fascinating. He manipulated the pitch to provide the look of different pitches by changing his grip. What I wanted to know is if he could get by with just a good, varying fastball.
First, everyone needs to take a trip over to Peralta’s game page at BrooksBaseball.net and examine the pitch groupings. Usually, different pitches form clusters when examining variations in break, velocity, and spin. Ignoring the possible changeup, he has two groups, fastball and curve. His fastball has an estimated spin which varies from 1300 rpm to 2500 rpm. Its velocity differs from 88 mph to 97 mph. The spin values on Brooks are interpreted based on the ball’s break. The spin rates may be off because Peralta releases really close to home as Jeff Sullivan documented.
• I got swamped last night during my chat on Peralta questions. I just didn’t know much about him so I went and watched his debut.
• It looks weird in that he is nearly falling down on every pitch and just averaging 90 mph on his fastball. It reminds me of high pitchers trying to reach 85-mph.
• Fastball: It sat at 87-95 mph with some nasty glove-side run. It could be considered a cutter at times. He used it like Mariano Rivera did by changing the spin. Partway through the game, MLB Gameday started labeling some of his pitches as cutters. It was not two separate pitches since there was no unique spin-velocity grouping
| 7:32 |
: Time to get started |
| 7:33 |
: Here are the FAAB reports from the 2 Tout Wars 15-team leagues: |
| 7:33 |
: Mixed Auction |
| 7:33 |
: Name: FAAB (out of $1000) |
A week ago, I examined how prospect rankings could add more context to hitter projections. It’s time to take the research a step further by dividing up the prospect list to see if projections can be refined. And they can be.
Initially, I shied away from dividing up the prospect lists because the sample size quickly gets into single digits. I started dissecting the data hoping to keep reasonable sample sizes. I sort of achieved my goal.
I used the same parameters in the last article. I compared a hitter’s Steamer projected OPS (on-base percentage plus slugging percentage) from 2010 to 2017 to the actual results in their debut season. To designate prospects, I used Baseball America’s top-100 which has been compiled since 1990. I collected the average and median change in OPS. The median value helps to smooth out any major outliers.
Rarely is OPS (on-base percentage plus slugging percentage) a fantasy stat. It’s off many people’s radar but it’s widely available and closely mimics a position player’s overall hitting talent. While other stats (e.g. wOBA and wRC+) also give a hitter an overall value, these stats aren’t available at every website. Most sites have their own unique blend but OPS is commonly available. Because of this availability, I’ve been using it as a baseline in recent articles on adjusting projections based on prospect pedigree and when hitters get platooned ($$). Now, it’s time to use OPS to help predict the individual categories.
The process I used for this study was to simply see how much various stats changed when OPS changed a certain amount. For rate stats (e.g. batting average) the conversion is straightforward. For counting categories, I put the stats on a per 600 plate appearance scale. Additionally, I only compared data from 2015 to 2017 during the current “juiced” ball era. I know the process is not close to being 100% precise and that is fine. I’m just trying to create general adjustments and can look to hone the process later. I’m putting in 20% effort to get 80% of the answer.
My focus is on lineup position and the amount of regular play each hitter get. I’m not concentrating on positions played. Also, if a team isn’t listed, I didn’t find any new information.
Note: I highlighted what I consider to be the seven most important findings.
Angels
Astros
