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 »

Last week, I reviewed my hitter Pod Projections vs Steamer projections comparisons. Let’s now move along to the starting pitchers and ERA. As a reminder from my original post:

Though Steamer is the best pitching projection system out there, it struggles on pitchers that have shown consistent BABIP and HR/FB rate suppression skills and deficiencies, as what usually works for the majority of pitchers — projecting a heavy dose of regression to the MLB mean — means it misses on those uncommon exceptions. I use Statcast’s xBABIP now for my projections, so I’m not afraid to forecast a mark that strays from the league average. However, I certainly still include some regression as we don’t always have enough batted balls in a pitcher’s history for that mark to stabilize.

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I’ve been slowly working my way through the hitter projections and that journey comes to an end today as I examine how each projected hitter rate stats stand up. Besides batting average, I turn each of the counting stats into a rate by dividing by plate appearances. Finally, I adjust each value to the actual league rates. Again, any combination of projections stick out along with the BAT.

For reference, here are the projections used.

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

To create a list of players to compare for accuracy, I took the NFBC Main Event ADP (players in demand at that time) and selected the hitters in the top-450 drafted players (30-man roster, 15 teams in the Main Event). To determine accuracy, I calculated the Root Mean Square Error (RMSE) for two different sets of values. RMSE is a “measure of how far from the regression line data points are” and the smaller a value the better. Additionally, I included the actual and league average rates for reference. Read the rest of this entry »

After diving into early draft hitter projections, the late draft season hitter projections get their time in the sun. First up is the counting stats that are heavily influenced on accurately guesstimating playing time. As with the early projections, the Bat and the Wisdom of the Crowds stand out with the addition of the Pod projections joining the others near the top.

For the projections, I pulled the following ones from the morning of March 30.

• Steamer (FanGraphs)
• ZIPS
• DepthCharts (FanGraphs)
• The Bat
• The Bat X
• Davenport
• ATC (FanGraphs)
• Pod (Mike Podhorzer)
• Masterball (Todd Zola)
• PECOTA (Baseball Prospectus)
• RotoWire
• Razzball (Steamer)
• ZEILE (Fantasy Pros)*
• Paywall #1

I didn’t run the values on CBS even though I pulled them. They were missing quite a few players and I messed up not pulling the Utility-onlys. Additionally, I pulled the ZEILE projections which are an average of several projections. Read the rest of this entry »

In mid-March, I listed and discussed the 10 hitters my Pod Projections forecasted for more home runs per 600 at-bats than Steamer. All that means is that I calculated the player’s projected AB/HR ratio from both systems and then extrapolated those projections over 600 at-bats, so I’m isolating the home run rate skill and keeping playing time constant. Let’s dive into the results.

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