We now complete our lucky/unlucky trilogy by looking at hitters. For those of you just joining the symposium: we ‘ve had some success identifying players who have, we posit, been unduly favored or disfavored by fortune, and for whom the wheel accordingly figures to make a 180-degree turn. With pitchers, we look for (1) lucky guys who’ve been hit hard (as measured by, duh, Hard-Hit Percentage) but have managed to contain the damage, as measured by Batting Average on Balls in Play and Home Run Percentage, and (2) unlucky guys who’ve done the opposite.

We’ve already reviewed the results of this exercise for both starters and relievers. With hitters, we do essentially the same thing. We find guys who hit the ball hard but didn’t get hits, and guys who didn’t but did. Last year this approach would have pointed you towards Marcell Ozuna and Chris Iannetta and away from Ian Desmond. Of course, it would also have steered you away from Jonathan Schoop, who, in what is not among our finest moments, we suggested would be outperformed by Ben Zobrist and Logan Forsythe. So how do you tell the Schoops from the goats? Beats us. Read the rest of this entry »

Today we continue our comparison of my Pod Projections with Steamer projections. This time I’m going to check in on stolen bases and begin by identifying nine hitters that I’m projecting for a lower PA/SB rate. Stolen bases totals are primarily fueled by three factors — on base percentage (which itself is driven by a variety of components), how often the player attempts a steal once he reaches base, and how successful he is when attempting a theft. With so many moving parts, it’s sometimes very difficult to forecast stolen bases, especially for certain types of players, like the big sluggers who suddenly decide to run (Manny Machado in 2015 and Bryce Harper in 2016).

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Yesterday, I discussed nine hitters that my Pod Projections forecast for a lower AB/HR ratio than Steamer, giving them home run upside, in my opinion. Today, I’ll check in on a group of hitters I see as possessing significant downside compared with Steamer.

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Last year, I transformed my series pitting my Pod Projections against Steamer projections into a categorical comparison. I’m going to continue that this year, but instead of comparing counting stats extrapolated over my plate appearance projection, I’m going to go straight to ratios. As an ardent supporter of ratios over counting stats, I have no idea why I didn’t do this to begin with!

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This offseason, I decided to roll out this series as an extension of my own fantasy draft prep. By sifting through National Fantasy Baseball Championship (NFBC) draft data, I’ve gone position by position through average draft position (ADP) info to identify players who I believe my fellow fantasy owners are overrating or underrating. By doing so, I’m able to better identify potential sleepers, and get an idea for what price I’ll pay for them. So far, I’ve written posts on third basemen, shortstops and second basemen, and today I’ll take a stab at first base.

As you might have guessed from reading the headline above this article, today’s comparison involves Rockies rookie Ryan McMahon and Brewers veteran Eric Thames. I’ll lead off by saying that this one boggles my mind more than any of the other underrated/overrated pieces I’ve written this offseason.

Let’s take a quick look at where these two are being drafted:

Essentially, Thames is a 16th-round pick in 12-team standard leagues, and McMahon is a 30th rounder — or in other words, he’s basically being drafted as an NL-only guy. I wondered if these numbers might be changing as we get closer to the season, so I took a look at Yahoo, which has only even had their fantasy baseball site up for a little over week. However, it’s the same story there, where Thames is 86% owned, compared to just 9% for McMahon. My goal for today is to help close that gap.

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The 2018 Pod Projections are now available! For the first time, the package includes NFBC ADP, along with all historical Pod-developed xMetrics. My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve (thanks Statcast!).

2018 Pod Projections Index:
Shohei Ohtani

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I’ve been prepping for two AL-only leagues, so the today’s thoughts only involve mainly AL hitters but can be extended to either league. While working on my evaluations here are some observations.

Create a plan for the injured X-factors

The two hitters who could carry a fantasy team, if healthy, are Michael Brantley and Miguel Cabrera. If (BIG IF) they get a full season of healthy plate appearances, I’d not be surprised if they were top-20 hitters. Owners aren’t showing a ton of faith since Cabrera’s ADP is at 95 and Brantley’s at 245.

No one knows for sure how they’ll perform so owners need to know before the draft where they feel they can gamble on rostering them. Owners need to make this decision ahead of time, not on the fly in a draft or auction. Come up with a plan you can live with and stick to it. Their evaluations can be adjusted as detailed reports become available from spring training on their health.

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Today marks the end of xBABIP week, after I shared and discussed 11 hitters potentially due for a BABIP surge and 10 hitters at risk of dramatic decline over the last two days. Today I’ll check in on hitters that at first glance, wouldn’t appear to be far off from their xBABIP marks, while the surgers and decliners list were quite a bit more obvious. If you posted a .230 BABIP in 2017, you’re probably going to find yourself on a potential surger list, while a .380 BABIP is likely going to get you onto the decliner list.

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Yesterday, I used my xBABIP equation to identify and discuss 11 hitters who might be in for a BABIP surge this season. Today, I’ll move on to the other side of the ledger — those hitters whose xBABIP marks were significantly below their actual BABIP marks, suggesting serious downside this year.

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A year ago, I introduced the latest and greatest version of my hitter xBABIP equation, this time incorporating shift data. Even though it was leaps ahead of any previous iterations and attempts at an xBABIP equation, it still only resulted in an adjusted R-squared of 0.5377. There’s still a whole lot more work to be done here! I would have liked to spend some time doing more research in the hopes of unveiling a further improved equation before the season begins, but alas, I haven’t had the time.

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