On Tuesday, Carlos Santana signed a two-year deal with the Royals, likely to take over as their starting first baseman, with the occasional start at DH I’m sure. The move pushes Hunter Dozier back to third base and essentially signals that the team has given up on Ryan O’Hearn. Let’s find out how the change in home ball park might affect his performance.
Yesterday, I used batter Hard% as the first example of why using averages might be deceiving. Along with an explanation as to why, I listed and discussed 15 hitters whose fly ball Hard% most exceeded their overall Hard%. Let’s now shift to the other side and identify and discuss the hitters whose FB Hard% settled most below overall Hard%. These are the hitters whose home run power was actually worse than overall Hard% suggests.
I started diving into the dividing out the effects of injuries limited ramp time for the short season and didn’t get far. Some league-wide rates didn’t add up. Hitters who head to the IL are outperforming the healthy population.
Note: This analysis is math-heavy. I summarized my findings and questions at the end.
To start with, here are the league-wide OPS values for all nonpitchers as I compare the first month as players might have been ramping up.
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As more and more advanced metrics are publicized and used in analysis and discussion, opportunity for misuse and misinterpretation of these statistics continues to increase. First, remember that a statistic is a fact. The metric in and of itself isn’t wrong or bad, but rather the use and interpretation might be. Some metrics are easier to misinterpret than others. One such metric type is the use of an average, which applies to many metrics. Averages are fine if used properly. For example, we could say a .350 hitter recorded more hits per at-bat than a .300 hitter, but we absolutely cannot say that the .350 hitter is better than the .300 hitter, solely using the difference in batting average as the determining factor.
Calling someone a flop in two-month season requires some serious caveats, air quotes around “flop”, or both. That doesn’t mean there weren’t several disappointing players that deserve a deeper look to see what’s going on. We will investigate some of them today and if you find this exercise worthwhile, we can make it a series!
Victor Robles | WAS – OF – 65 wRC+
Robles had the standard “better in fantasy” season in 2019 as his 17 HR and 28 SB overshadowed his uninspired .255/.326/.419 line. His proponents (of which I am one) acknowledged the poor batted ball profile but felt that it was premature to lock the 23-year old into his 2019 levels. Oh, he wasn’t locked in… turns out he could get worse!
Yesterday, I identified and discussed the hitters whose SwStk% marks improved the most versus 2019. Today, let’s check in on the opposite end of the list. Remember that “decliner” in this context actually means these hitters’ SwStk% marks have increased, so their skill declined, but the metric we’re using to evaluate their skill has risen.
Recently I outlined how the installation of Hawk-Eye as Major League Baseball’s tracking and data collection system has shed light on the issue of untracked batted ball events (BBE) in prior years. The issue was first broached by Connor Kurcon, who uses launch angle for his various research and analytical endeavors, including classified run average (CRA), dynamic hard hit rate (DHH%), and TrueHit percentage.
If you’re too lazy to click through, I’ll recap: Because Hawk-Eye tracks more than 99% of BBE, we can use the distribution of launch angles in 2020 to identify the possible launch angles of untracked BBE in previous years. Most likely, untracked BBE converge on the most extreme angles — think -90° and 90°, but with a margin for error such that some BBE as shallow as -40° (for ground balls) or 50° (for pop-ups) might have still gone untracked.
Absent the information available to us now, Tom Tango and the Statcast team devised a method that would impute exit velocity (EV) and launch angle (LA) values that most closely mimic the untracked BBE’s observed outcome by measure of weighted on-base average on contact (wOBAcon). From my observation, Statcast applied roughly half a dozen different launch angle estimates for this purpose, with two in particular used disproportionately: -21° or -20.7° (for ground balls) and 69° (for pop-ups).
Again, absent the data we now have, this was as good an approach as one could reasonably expect. But now we know untracked BBE cluster around the extremes. An imputation of 69° for pop-ups is reasonable, but -21° for grounders might not be extreme enough.
To correct for this issue in the seasons preceding 2020, I adopted an approach I recommended in my original post.
Let’s continue on with our look at the plate discipline metrics by moving onto swinging strike percentage (SwStk%). With a strong correlation of 0.77 (qualified batters from 2015-2019), SwStk% is a good proxy for strikeout rate and the two will usually move in the same direction. Because SwStk% uses a denominator of total pitches, versus K%’s use of plate appearances as its denominator, it takes far less time for SwStk% to become meaningful. As such, the value of the metric increases in a shortened season like 2020. So let’s review the 2020 batter SwStk% improvers.
Earlier this week, I wrote up a team of guys who could be top 75 players next year. It is a different way to write about breakouts and I want to run it back today with some deeper breakouts. These guys have a real shot at being top 150 players in 2021 which for reference is where guys like Jorge Soler and Yasmani Grandal are being drafted meaning these would be high impact pieces of your team if they panned out.
Max Stassi, C 336 ADP
His awesome 2020 was undercut by only playing 31 games so you may not have noticed that he hit 7 HR and 20 RBI with a .278/.352/.533 line in 105 PA. With Jason Castro gone, the job is clear for Stassi to take on full time and sit just once a week for rest. Stassi has a strong glove reputation and the bat might be catching up.
Yesterday, I identified and discussed the hitters that improved their O-Swing% the most versus 2019. Today, I’ll check in on the other side of the ledger. While I titled this post “decliners”, what that really means is a decline in skill, assuming a lower O-Swing% is representative of greater skill. So these are the hitters whose O-Swing% increased the most versus 2019.
