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.
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!
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
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.
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.
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.
The introductory section below is going to be similar to previous LABR recaps since little has changed and there’s no sense in rewording things.
The clearest sign of a new baseball season is the annual super early LABR Mixed draft. Last Tuesday, 15 of us fantasy nerds virtually gathered to speculate where the swath of still-free agents will sign and hope our early picks don’t suffer spring training injuries. Though I’m certainly not a fan of February drafts, at least it provides me the needed motivation to finish my first run of Pod Projections that drive my player values. Without the forecasts and valuation spreadsheet, I’d be drafting blind, and that’s no blueprint for a Yoo-hoo shower.
On Tuesday, I hopped over to the pitcher side of the ledger to discuss nine fantasy relevant starting pitchers with strikeout rate upside this season. I used my xK% equation and compared what the formula spit out to what the pitcher’s actual strikeout was. Today, I’m going to share the ten pitchers who most outperformed their xK% marks.
I’ve spent nearly the entire off-season discussing hitters, as Statcast and xHR/FB rate took over my life. Let’s move on to pitchers for now, and begin with another of my xMetrics, xK%. I updated the metric’s coefficients last season and it’s probably the best xEquation out there given its sky high adjusted R-squared.
We all know a park’s dimensions, foul territory, hitter’s backdrop, atmospheric effects, etc. play a significant role in shaping our projections and on a player’s performance. Collectively, we know these effects as park factors. We are probably most aware of a park’s home run park factor. I’m sure that for many parks, you have a perception in your mind as to its home run friendliness. The data might say otherwise, but at least you think you know, unlike, say, triples, which I’m sure most haven’t a clue which parks are best for boosting the three-bagger. Unfortunately, while the idea of park factors is sound, they are extremely problematic to rely on.
