Two years ago, I introduced you to the latest iteration of my hitter xBABIP equation, this time incorporating the effects of defensive shifts. Let’s find out which fantasy relevant hitters most underperformed their xBABIP marks in 2019, suggesting the potential for dramatic upside in 2020…if the hitter maintains those underlying skills.
In what turned out to be quite soap operatic, the Red Sox and Dodgers finally complete a trade…though not exactly the original one that was tentatively accepted last week, which also included the Twins. The biggest name to move is of course Mookie Betts, who makes what was already an excellent Dodgers offense into one that is now laughably good. But for those who have already spent a first round pick on Betts, plan to keep him, or are wondering how to adjust his value after the move, the big question now is how might the switch in home park affect his performance. Let’s consult the park factors (2018) and find out!
Last Tuesday, just four days into February, I participated in the earliest LABR draft ever. We always draft in February, but had to do so a week earlier than usual this year due to owner availability. 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 (now available!) 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. This actually marked my return to the league, as I was on vacation last year when the draft was scheduled.
Today, I move on from xHR/FB rate and its components to my xBABIP equation. Every year, there are some seemingly obvious candidate to regress (like the guy who just BABIP’d .390) or improve (like the guy who just BABIP’d .210). But what about the guys hovering within range of the league average of .298? Just because their BABIP marks were close to average doesn’t mean their skills supported such a mark. So let’s discuss hitters who posted a seemingly sustainable BABIP, but xBABIP suggests the mark was either way over their head or actually below what their skills suggested.
Yesterday, I used my xHR/FB rate equation to identify and discuss 7 hitters who could enjoy a HR/FB rate spike in 2020, assuming they maintain the component skills they displayed in 2019. Now let’s turn to the other side, those hitters who dramatically outperformed their xHR/FB rates and could be in for regression this year, unless they improve their underlying skills, or paid off Lady Luck once again to ensure fortune is on their side. As you might expect given the home run explosion, there were more overperformers than underperformers, so I had to cut it off at 6% over xHR/FB rate.
Finally, it’s time for the main event! After weeks discussing the various xHR/FB rate components and reviewing 2019 performance, we now set our sights toward 2020. Today, I will identify and discuss a handful of fantasy relevant names that underperformed their xHR/FB rates most significantly. Remember that this doesn’t automatically mean we should be projecting a higher HR/FB rate this season. But perhaps rather than take the hitter’s actual HR/FB rate at face value, we should substitute our xHR/FB rate mark when reviewing his historical marks and making a 2020 projection.
Last week, I identified and discussed a slew of hitters whose xHR/FB rates validated their surprisingly strong HR/FB rates. Today, let’s look at some of the disappointing hitters in HR/FB rate that were validated by a dramatic decline in xHR/FB rate. Remember, an appearance here doesn’t mean these hitters should be projected to repeat their disappointing 2019 marks, as this is merely a backward-looking analysis. We need to account for additional factors when making a projection. But all else equal, the guy with an xHR/FB rate validating his disappointing HR/FB rate should be expected to have a worse chance of rebounding than the guy who suffered a disappointing actual mark, but underperformed his xHR/FB rate.
Over the past couple of weeks, I’ve been sharing various hitter lists focused on the components of my xHR/FB rate equation. Today, I want to share yet another list. This time, we’re not going to look at the individual components, but rather the xHR/FB rate itself. Just like every other year, there were breakouts and busts. Some of them lucked their way into surprising HR/FB rates, while others seemingly deserved such results. The opposite is true as well, with some batters suffering from poor luck, which dragged down their HR/FB rates, while others suffered a real decline in power.
After signing free agent Kole Calhoun, the Diamondbacks apparently weren’t done adding outfielders. On Monday, the team acquired Starling Marte for a pair of prospects. The knee-jerk reaction is that this must be a positive for Marte’s contribution on offense given the perception of PNC Park as an extremely pitcher friendly environment. Do the park factors confirm this sentiment?
Yesterday, I identified and discussed a slew of surprising names among the average fly ball distance (AFBD) leaders. Today, I’ll check in with the laggards. Which hitters finished closer to the bottom of the barrel than the top that we never would have expected?
