For this series, I want to focus on pitchers going after pick 300 in the NFBC ADP. While I’ve moved well past pick 300, three names just dropped below after 300 so I will go back to examine them before moving further down the list.

Starting and Relievers, Part 1 & 2($$):

• Alzolay, Quantrill, J. Suarez, Thompson, R. Suarez, Thompson, C Hernandez
• Javier, Lamet, Rasmussen, Steele, M. Perez, Cortes, Espino, Stripling, Long, Houck

Deep League Starting Pitchers

• Kaprielian, Taillon, Carrasco, and Ober
• Bauer, Plesac, Kikuchi, Gonsolin, and Greinke
• Matz, Megill, Sanchez, and Walker
• Lauer, Hernandez, Heaney, and Flexen
• Pivetta, Strasburg, Paddack, and Singer Read the rest of this entry »

Almost done with the first set of rankings as I still have OF and RP coming after the SPs today. My rankings with the individual comments will come after the new year, but these will lay the groundwork and show you where I’m at to start. These are for 5×5 15-teamers with a 20-game eligibility threshold.

Let’s discuss in the comments!

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Here is the first run of my top-150 starting pitcher rankings for 2022. I create these by turning my personal projections into dollar values and using those, along with non-projection factors, to rank accordingly.

These will change, as my projections do, over the course of the offseason, with many being driven by adjustments in playing time projections more so than changes in talent rates. ADP information is taken from NFBC, previous values are calculated with FanGraphs auction calculator. Read the rest of this entry »

Yesterday, I reviewed the pitchers who had most underperformed their Statcast xHR totals through May 24. Today, let’s now review the pitchers who had most overperformed their xHR totals, meaning they allowed fewer home runs than Statcast calculated that they “should have”. Did these pitchers allow a higher HR/FB rate over the rest of the season? Let’s find out.

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We’re finally winding down the in-season metric reviews, and today, we return to Statcast’s xHR calculation, but this time for pitchers. So we’ll be reviewing the pitchers that had allowed significantly more home runs than expected according to Statcast through May 24 and find out how each performed over the rest of the season. Like I did for my hitter review, this is essentially comparing HR/FB rates through the first two months with the rest of the season, since actual fly balls are used in the calculation and Statcast is calculating how many of those should have left the park versus what actually did. Let’s find out if these pitchers did enjoy major HR/FB rate improvement over the rest of the way.

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The latest episode of the Beat the Shift Podcast – a baseball podcast for fantasy baseball players.

Guest: Jeremy Hefner, New York Mets

Interview

• Highlight of career
• Game preparation
  • Preparing players for the mental & physical aspects of the game
  • Is game planning more about having players pitch to their strengths, or about exploiting weaknesses of your opponents ?
  • In-game management
    • 3rd time through the order
  • Use of an “opener”
  • How “first-in” and “next-in” pitchers coming out of the bullpen are decided

• Health
  • Keeping pitchers healthy throughout the season
  • Extended rest between outings – good or bad?
• Analytics
  • How best to use analytics as a coach
    • Objective vs. subjective view
  • Analytics vs. Gut
    • Pulling Blake Snell in the World Series
    • Trusting analytics, and when to go with your gut
  • How are stats/analytics used to help players on the major league level?
    • Which are the key stats/indicators that you look at?
• Injury Guru’s Trivia of the Week
• Rule changes in baseball
  • Universal DH
  • 3 batter minimum rule for pitchers
  • Extra inning runner on 2nd base rule
• Position players pitching in baseball
  • Effectiveness of Ariel pitching in the major leagues

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xFIP is an underlying metric that can be used as a predictive ERA estimator. By definition of Fangraphs: xFIP is a regressed version of Fielding Independent Pitching (FIP), developed by Dave Studeman from The Hardball Times. It’s calculated in the same way as FIP, except it replaces a pitcher’s home run total with an estimate of how many home runs they should have allowed given the number of fly balls they surrendered while assuming a league average home run to fly ball percentage (between 9 and 10% depending on the year).

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Another dive into pitchers going after pick 300 in the NFBC. Here are the arms I’ve already examined.

Starting and Relievers, Part 1 & 2($$):

• Alzolay, Quantrill, J. Suarez, Thompson, R. Suarez, Thompson, C Hernandez
• Javier, Lamet, Rasmussen, Steele, M. Perez, Cortes, Espino, Stripling, Long, Houck

Deep League Starting Pitchers

• Kaprielian, Taillon, Carrasco, and Ober
• Bauer, Plesac, Kikuchi, Gonsolin, and Greinke
• Matz, Megill, Sanchez, and Walker
• Lauer, Hernandez, Heaney, and Flexen

Read the rest of this entry »

Another dive into pitchers going after pick 300 in the NFBC. Here are some arms I’ve already examined.

Starting and Relievers, Part 1 & 2($$):

• Alzolay, Quantrill, J. Suarez, Thompson, R. Suarez, Thompson, C Hernandez
• Javier, Lamet, Rasmussen, Steele, M. Perez, Cortes, Espino, Stripling, Long, Houck

Deep League Starting Pitchers

• Kaprielian, Taillon, Carrasco, and Ober
• Bauer, Plesac, Kikuchi, Gonsolin, and Greinke
• Matz, Megill, Sanchez, and Walker

Read the rest of this entry »

Yesterday, I reviewed the early season starting pitcher SIERA underperformers and discovered that the majority did indeed improve their ERA marks over the rest of the season, some significantly so. Now let’s flip over to the list of SIERA overperformers. Were these pitchers able to sustain their magic or did the force of regression prove too powerful?

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