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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In a former life, I had the time and energy to keep up with Peripheral Prospects on a semiweekly basis. That dream hibernated in 2020 when the pandemic killed the minor league season and died for good this year when I simply failed to uphold my end of the bargain.

But I love Peripheral Prospects and the inexact science/exact art of digging up breakout fringe and non-prospects with potential to make waves at the big-league level despite lacking the requisite hype. These breakouts make for feel-good stories, but for fantasy baseball purposes they’re market inefficiencies that can change the trajectories of dynasty (or even redraft) teams.

So, I want to spill at least a little bit of digital ink in honor of my favorite Peripheral Prospect hitters (and pitchers, coming in a separate post). A year-end catch-all post loses the dynamics of the ebb and flow of player performance; I benefit from these slash lines being etched in stone. But that hindsight should make the selections here a bit tighter than might have normally been picking five fresh names every other week for six months.

Anyway, here’s my list of top-8 Peripheral Prospect hitters from 2021 (because 10 was too many—this is rarefied air, y’all). The only rules: (1) They played in the high minors (Double-A or Triple-A) but not the MLB level, and (2) they cannot be featured on any prominent top-100 list. I’m going to rank them loosely from favorite to least-favorite. Let’s go!

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Last week, I reviewed my imaginary cheapie NFBC hitting rosters, including the $14 offense, and the offense composed of hitters not even bought in auctions. Today, let’s now review the incredible $251 offense I built. This exercise was probably even harder, as it’s more difficult to differentiate between top three round values than it is to pick out the best of the worst. Let’s see how this roster ended up performing.

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The following analysis is beyond nerdy.

I’ll try to keep it simple as possible, but no guarantees. This past week, I examined the results of several hitter projection systems. In the comments of the first article, Mays Copeland and Skin Blues brought up a near 15-year-old thread on the InsideTheBook blog between Tom Tango and Nate Silver. Read the rest of this entry »

Yesterday, I reviewed the thrilling $14 offense I built with all hitters that averaged just $1 in auction cost in NFBC leagues as of the beginning of March. Today, I review an even tougher challenge — an offense composed solely of hitters who weren’t purchased at all in the seven auctions that occurred in the month of Feb.

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