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.
After two days discussing individual players with apparent upside and downside given their fly ball pull percentages (FB Pull%), it’s time to really get to know the metric. Let’s begin by looking at the leaguewide trend over the last 10 years.
Yesterday, I began my discussion of another one of my xHR/FB rate equation’s components, FB Pull%, and shared the hitters who posted above average Brls/True FB and Avg FB Dist marks, but below average FB Pull% rates, hinting at upside if a change in approach is made. Today, I’m going to check in on the hitters who posted below average Brls/True FB and Avg FB Dist marks, but above average FB Pull% rates. These hitters are at greater risk of HR/FB rate regression given their heavy reliance on pulling their flies. If that skill erodes or approach is altered, there would be major downside.
One of the components of my new xHR/FB equation is fly ball pull percentage (FB Pull%). Sadly, I have spent significantly more time discussing barrels per true fly ball and average fly ball distance, so I’m going to change that. Let’s talk FB Pull%!
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!). Given the hype and the difficulties of translating performance from a foreign league, it was obvious who the first player for this series should be — Japanese uber-athlete Shohei Ohtani.
