Let’s do some experimenting. Let’s imagine you drafted a team using only one statistic. What style of baseball are you? Do you love the hitters with speed, bat control, and an eye for tactics? Or, are you more of the home run or strikeout kind of fan? Why can’t you be both, you ask? Well because it’s an experiment and you have to choose one or the other. So…go ahead. Which do you choose? The K% Kings or the Barrel Brothers?

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After just a few games this year, I decided to check in on batter maxEV. If a batter was already setting a new high only a couple of games into the season, I wondered if it was an early sign of a power and HR/FB rate surge over the rest of the year. So let’s now review those hitters who increased their maxEV the most at that point to see if it was indeed a hint of things to come. As a reminder, maxEV may have been recorded on a ground ball, which is far less exciting than one that comes on a fly ball. It doesn’t matter how hard a grounder is hit, as it cannot leave the park! A better look would have been to filter for only fly balls, so I’ll look at it that way next season.

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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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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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If you haven’t studied the managers who won your league by now, you should. If you won your league, and your competition is smart, you’re being studied. People are trying to find out why the Robbie Ray‘s and the Marcus Semien’s of 2021 caught the eye of those who drafted them and are looking to find next season’s doppelgangers. The Dodgers didn’t take long at all to take their pick. We can make all the models, algorithms, spreadsheets, and crystal ball readings we like, but the most tried and true technique is regression. Players that were unbelievably good…will regress to their true-talent level. Players that were unexpectedly bad…will regress to their true-talent level. This is something you can take to the bank. Rather than choose from 15 of the best players we should take in the first round, let’s think of players that we expect to go in the 5th and 6th rounds. Here’s a look at three players that seem likely to do just that, and could fly under the radar in your 2022 draft.

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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 »

Yesterday, I compared my Pod Projections in the stolen base category to Steamer and reviewed the five hitters I forecasted for a meaningfully higher stolen base total. Today, let’s now review the hitters I projected for fewer stolen bases than Steamer over a 650 plate appearance pace. As a reminder, stolen bases were down this year, so theoretically it should have been easier to hit on more of the downside guys.

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Today, I continue my Pod Projections vs Steamer battle reviews, this time moving along to stolen bases. Similarly to the way I compared our home run forecasts, I calculated a PA/SB rate first and then extrapolated that projection over 650 plate appearances, so we’re only comparing stolen base projections and playing time forecasts don’t factor in. We’ll start with the stolen base upside guys. For some context, the league stole the fewer bases per 650 plate appearances since…1971! So hitting on the upside guys is going to be a lot tougher than hitting the downside guys.

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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 »

Yesterday, I reviewed my Pod vs Steamer home run upside list results. Today, let’s now review my Pod vs Steamer home run downside list. As a reminder, the comparisons are AB/HR ratio and the table displays the implied home run totals over 600 at-bats. So the actual HR column isn’t necessarily what the player hit, but what his 600 at-bat pace actually was in order to truly compare home run rate projections without playing time factoring in.

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