Yesterday we dropped the first piece of our 2021 draft season coverage, which discussed the Athletics’ backstop Sean Murphy. Two of our writers discussed why they find themselves higher or lower than consensus on the young slugger. You can check that article out here.
Today, we are going to take a slightly different look at the state of the catching position. One of the biggest things we’ve worked on this offseason is our in house stat platform, The Data Monster. One of the most popular questions we have gotten about the tool is about what each tab is telling us. To help alleviate those issues we will be using the Data Monster to discuss what certain hitters excelled at in 2020 at each position. These articles will drop each week on Tuesdays and Thursdays and we cover a new position in each article. That begins today, with Catchers.
Catcher has long been a fantasy wasteland and each individual manager takes a different approach to solving this issue. Some like to spend up for J.T Realmuto, while others like to wait it out and fill it with boring but safe options. Regardless of your individual approach, we all need to roster at least one and in some leagues like NFBC, we need to start two. Regardless, let’s look at some hitters the Data Monster likes and dislikes at the catcher spot.
The first set of statistics for hitters that you’ll see when you load the Data Monster is the Whiffs board. This tab in its simplest form is an attempt to understand the underlying aspects that make up a hitter’s whiff rate. For our purposes, whiffs are similar to what many others call swinging strikes. This means how often give a given hitter swing and miss on all pitches thrown to him. The first column on this sheet is the actual Whiff rate, the second column is what I call xWhiff. This uses an underlying model that factors in the count, pitch type, and location to determine the likelihood a given pitch generated a swing and miss.
This is essentially a measure of how pitchers attack a given hitter, a higher xWhiff means they are actively locating to spots where there is a higher likelihood of generating a swing and a miss. In one season samples, this likely does not carry a ton of interest for us as fantasy owners but it’s useful to look at for a trend. One interesting trend is Oman Narvaez.
For most of his early career Narvaez was seen as a good AVG play due to his low K rates. However, that skyrocketed in 2020. For the majority of his career, Narvaez saw a league average xWhiff rate and actually made more contact than the locations would suggest (In_Whiff). However, in 2020, pitchers seemed to pitch him more aggressively for Whiffs and his whiff rate jumped significantly. So assuming the contact ability that he’s displayed before the 2020 returns, but the changes in attack plan pitchers made remain, his K rate should remain elevated from his early career averages but should decline from 2020. I think Narvaez is not as “safe” of a play as he once seemed but he’s a guy I will be willing to roster in 2-catcher leagues.
However, the main value I look at on the Whiffs board is the final column, In_Whiff. This is essentially a measure of how much more or less likely a given hitter is to swing and miss. This value takes into account the location and pitch types but also the opposing pitcher faced. This means we give a hitter more credit for making contact against Edwin Diaz than we would against Eric Fedde. The leader among catchers in this metric is the Dodgers’ Will Smith. The young backstop looks like a star at the position due to his power upside in a dominant lineup but the extreme contact skill was a bit unexpected. This was a 98th percentile result in 2020, but his 2019 sample also graded out extremely well with a 77th percentile value. The contact ability coupled with power makes him a bit of a unicorn at the catcher position.
On the flip side, Jorge Alfaro had the worst In_Whiff in all of baseball in 2020. The Marlins backstop had similar issues in each of the previous two seasons. He also had 1st percentile results in 2018 and 2019. There is immense power potential in his bat but unless he can make some more contact he is doomed to mediocrity. However, for fantasy purposes, his 15-20 homer power and 5 or so steals make him a viable option for teams that want to wait on catchers.
I will combine these next two groups as in my opinion they do not tell us a ton about a hitter for fantasy purposes. These are essentially my versions of O-Swing and Z-Swing. I am able to determine the expected swing rate of every pitch and see how a hitter compares to that expectation. Typically, swinging at pitches in the zone at a greater rate is a good thing for a hitter, but there are better ways to understand the swing decisions and what they mean for production.
However, at the top of the list for swinging in the zone, we see free swingers, Jorge Alfaro and Salvador Perez. Both guys do not walk very often and while that does not mean much for those of us in AVG leagues, this is reason to downgrade both in those that use OBP. Among the more selective hitters within the zone, we have Yasmani Grandal, which makes sense as he is an OBP league stalwart.
As for the hitters who chase out of the zone the most, we see a trend with old friend Jorge Alfaro. He pretty much swings at everything in sight and that could be a huge reason for the insane whiff rates as well. On the flip side, Chance Sisco, was one of the best hitters in all of baseball at laying off pitches outside of the zone. He left the zone about 10% less often than the pitches he saw would have expected. He is an interesting late-round option who could grow into the power he’s shown flashes of due to a strong eye at the plate.
The fourth option among the types of stats, the Data Monster has is wOBA. Much like all of the other tabs, this looks at all balls in play and assigns an expected wOBACon value to the pitch based on location, count, and pitch type. Then based on the expected result (xLwOBA) and the actual result we are able to extract the influence over that result from both the pitcher and hitter.
This is the In_wOBA column on the tab. I like to think of this as a measure of contact quality. How much damage more than the average hitter did he do? It’s probably the best measure of the hitter’s overall ability. The leader in 2020 among all catchers in this metric, Sal Perez.
For his entire career, Perez has always been above average by this metric. However, 2020 was an entirely new stratosphere for the Royals’ catcher. As we have seen before Perez is an extremely free swinger and looking at his ability to improve the quality of contact it’s easy to see why. He whiffs a little bit more than average but as a whole, Perez makes enough contact and does a lot of damage when he does make contact allowing him to appear near the top of the power leaderboard for catchers every season. I am not a huge fan of his price but the skills are hard to come by at the position.
If you drop the pitches seen minimums to 100, Sam Huff finds himself near the top of the board as well not too far behind Perez. I think he should get a chance early in the season to take over for the Rangers and he could be a great source of late power at the position.
On the flip side, while he was not the worst catcher, Gary Sanchez was the worst catcher of interest by this measurement. As a whole Gary was not a fundamentally different hitter in 2020. He struck out more than ever before, but in terms of Exit Velocity and Launch Angle he was barely any different as a hitter. However, his BABIP plummeted. In_wOBA also saw a massive drop in his results and saw him go from typically ranking in the top 10% by this metric to the bottom 20%. I think since there does not appear to be anything massively different driving that drop we can chalk this up to a fluke of sorts. With that lineup and Gary’s power, he should bounceback in 2021 and return to posting elite power numbers with a poor average.
The last stat grouping is the Plate Discipline numbers. This was what I was eluding to before when discussing the flaws of looking at in zone and out of zone swing rates. I have created a custom metric I call SAE or swings above expected. This looks at the xLwOBA (location-based expected wOBA) of the pitches a given hitter decided to swing at versus the xLwOBA of every pitch he saw. This essentially shows us how much more of an advantageous position a hitter put himself into based solely on the pitches he swung at. The metric is scaled so that means a value of 110 means the pitches the hitter chose to swing at had an xLwOBA that was 10% better than all of the pitches he actually saw. This metric has a correlation of 0.40 with actual wOBA so it does a good job of measuring hitter skill.
Among catchers, the leader in this stat was Padres’ backstop, Austin Nola. Nola burst onto the scene in 2020 actually netting a decent trade return for the Mariners at the deadline last season. One of the best waiver adds of the season he hit for a great AVG while showing enough pop to make him a threat among catchers. Overall, Nola does not grade out well by In_wOBA meaning he does not improve significantly on expected results like a guy like Perez does. However, his success seemingly comes from choosing the correct pitches to swing at. He swings at extremely high-value pitches. I typically love to target hitters like these because any small improvement in contact quality can lead to a huge breakout.
On the negative side of things, Giants’ catcher of the future Joey Bart was one of the laggards in this metric. his to me is a big concern as it suggests that Bart may have some pitch recognition issues and until he corrects them he will struggle to improve his walk rates. While Bart did post an above-average In_wOBA, the poor plate discipline and swing and miss issues he showed in 2020 could be a signal that he needs more time in the minors. It appears that will be the case but Bart was a guy I was interested in as a post-hype play or sorts but the concerns may be moving me off that position.
Overall, we hope this was a fun and informative way to previewing the catcher position and this gives you a better understanding of how to evaluate hitters using the Data Monster. I’ll be returning Thursday with a look at first base and hopefully, we will be able to uncover a few sleepers along the way. As always if you have any questions or concerns about anything related to the Data Monster feel free to reach out to me on Twitter @pmamminofantasy.