Experiment alert! Prepare yourself to digest a very simple linear model that looks at plate discipline data. I’ll do some explaining of the model along the way, but here are a few points to cleanse your already superb palate before sampling the charcuterie:

1. I’ve limited to players with at least 60 PAs because it is a good point of stabilization for hitter K%.
2. I’m using 2017-2019 as a training set and then deploying my model on 2021 data to look for differences between model predictions and actuals.
3. My model only tells us what should be expected from a hitter who accumulates at least 60 plate appearances in a season based on what other players have done in the same situation from 2017-2019. 2020 is excluded. The predictions of this model should not be confused with expectations.
4. Hasn’t this been done before? Probably.

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

Guest: Trevor May, New York Mets

Interview

• Highlight of Career
• Game planning
  • Is about pitching to your strengths, or exploiting your opponents weaknesses?
  • Game planning team by team
  • Helping other teammates with game planning
  • Having a “default” game plan
  • Using MLB the Show
  • Learning from other teammates
• Health
  • Recovering from TJS
  • Switching from starter to reliever
  • Keeping healthy all season long
  • Extended rest between outings – good or bad?
• Injury Guru Trivia of the Week
• Analytics
  • Trevor’s own stat – Command Quality Ratio (CQR)
  • Trevor’s CQR and Ariel’s wPDI
  • Horizontal movement vs. vertical movement on pitches
  • Pitch velocity, spin rate, spin efficiency
  • Hanging out with the analytics staff
• Mailbag
  • Who do you NOT want to face with the bases loaded?
  • Why #65?
  • Day games vs. night games
  • Favorite inning to pitch in
  • Love of cats
  • Favorite podcast

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Cedric Mullins was one of the best breakouts of 2021 with a 30 HR/30 SB season that came out of nowhere. But what comes next? There have been 11 seasons of at least 30/30 since 2011 featuring 10 players and Ryan Braun doing it back-to-back in 2011-12. We actually saw four in 2011 and then two in 2012 before a drought that José Ramírez and Mookie Betts ended in 2018.

Here are all 11:

I wanted to look at what these players did before and after their magical season to see if it might help us with our Mullins projection.

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I’m finally done crunching numbers (part 1, 2, 3, 4, 5, 6, 7, and 8) with late-season pitcher rate stats being the last to go. It’s so monotonous and painstaking, but I’ve learned a few things going through it all.  The Average rises to the top … again with several other projections popping to the top.

First, here are the projections analyzed.

• Steamer (FanGraphs)
• ZIPS
• DepthCharts (FanGraphs)
• THE BAT
• Davenport
• ATC (FanGraphs)
• ZIELE (Fantasy Pros)*
• Pod (Mike Podhorzer)
• Masterball (Todd Zola)
• PECOTA (Baseball Prospectus)
• RotoWire
• Razzball (Steamer)
• Paywall #1
• Average of the above projections

To create a list of players to compare for accuracy, I took the NFBC ADP (players in demand at that time) and selected all the pitchers in the top-450 drafted players (30-man roster, 15 teams in the Main Event) in ten or more leagues. Then I removed all the pitchers who never threw an inning. Read the rest of this entry »

Today, I share the final review of my pre-season Pod Projections posts. This time, we shift to a starting pitcher, Zach Plesac, whose original writeup is here. Plesac did post a sub-4.00 ERA during his 2019 debut, but it wasn’t backed by his skills, as he handily outperformed his ugly 5.13 SIERA. During the short 2020, he enjoyed a true breakout as his strikeout rate surged thanks to pitch mix changes. Let’s see how he did for an encore and how it compared to the projections.

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Let’s continue reviewing the Pod Projections I shared early in the year. Today, I’ll review my Trent Grisham forecast. You can find the original writeup here. Grisham enjoyed somewhat of a fantasy breakout during the short 2020 season, as he went 10/10 over 252 plate appearances, putting him on a 20+/20+ pace over a full season. We fantasy owners salivate over that power/speed potential. Let’s see how he followed up and compare it to my projections and the rest of the forecasts.

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I’m on the home stretch with most of the comparisons (part 1, 2, 3, 4, 5, 6, and 7) already done. Today, the counting stats for the late-season pitcher projections taking center stage. The boys over at Razzball dominated most of the results with the aggregators coming in near the top … again (might be a theme).

First, here are the projections analyzed.

• Steamer (FanGraphs)
• ZIPS
• DepthCharts (FanGraphs)
• THE BAT
• Davenport
• ATC (FanGraphs)
• ZIELE (Fantasy Pros)*
• Pod (Mike Podhorzer)
• Masterball (Todd Zola)
• PECOTA (Baseball Prospectus)
• RotoWire
• Razzball (Steamer)
• Paywall #1
• Average of the above projections

To create a list of players to compare for accuracy, I took the NFBC ADP (players in demand at that time) and selected all the pitchers in the top-450 drafted players (30-man roster, 15 teams in the Main Event) in ten or more leagues. Then I removed all the pitchers who never threw an inning. Read the rest of this entry »

As you probably already know, I manually project player performance each and every year, and make the forecasts available on my Pod Projections page. It’s a seriously time-consuming task, but the manual process gives me some advantages versus a computer system, so I continue to create them. Early in the year, I share a couple of my Pod Projections, the individual forecasted metrics, and an explanation of the process I follow to arrive at each number. This year, the first projection I shared was that of Ha-seong Kim, who had just signed a four year contract with the Padres after spending seven seasons in the KBO (Korean Baseball Organization). Projecting veteran baseball players is challenging enough, so you can imagine the added layer of difficulty when working on a forecast for a player coming over from a foreign league. Let’s find out how Kim performed compared to my projection and the two that were published in early January.

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I completed the counting stat analysis on early March pitcher counting stats after finishing the hitter projection comparisons (part 1, 2, 3, 4, and 5).  it is time for the pitchers to take center stage. For the first article, I’ll measure the accuracy of counting stats from early March. The results were mixed this time with the aggregators having a decent showing.

First, here are the projections analyzed.

• Steamer (FanGraphs)
• ZIPS
• DepthCharts (FanGraphs)
• THE BAT
• Davenport
• ATC (FanGraphs)
• Pod (Mike Podhorzer)
• Masterball (Todd Zola)
• PECOTA (Baseball Prospectus)
• RotoWire
• Razzball (Steamer)
• Paywall #1
• Average of the above projections

To create a list of players to compare for accuracy, I took the TGFBI ADP (players in demand at that time) and selected all the pitchers in the top-450 drafted players (30-man roster, 15 teams in the Main Event) in ten or more leagues. Then I removed all the pitchers who never threw an inning. Read the rest of this entry »

Now that the analysis hitter projection comparisons (part 1, 2, 3, 4, and 5) are done, it is time for the pitchers to take center stage. For the first article, I’ll measure the accuracy of counting stats from early March. Razzball had a near clean sweep as it only missed on Saves.

First, here are the projections analyzed.

• Steamer (FanGraphs)
• ZIPS
• DepthCharts (FanGraphs)
• The Bat
• Davenport
• ATC (FanGraphs)
• Pod (Mike Podhorzer)
• Masterball (Todd Zola)
• PECOTA (Baseball Prospectus)
• RotoWire
• Razzball (Steamer)
• Paywall #1
• Average of the above projections

To create a list of players to compare for accuracy, I took the TGFBI ADP (players in demand at that time) and selected all the pitchers in the top-450 drafted players (30-man roster, 15 teams in the Main Event) in ten or more leagues. Then I removed all the pitchers who never threw an inning. Read the rest of this entry »