Let’s get right down to the question that all baseball analysis is asking at its core: Which of these two players would you rather have on your team, all else being equal?

It’s a close one, right? That’s largely because I decreed it to be so; these aren’t real players, just stat lines I made up that have the same wOBA. Who would you rather have? They’re extremely different, of course; one gets a ton of value from walks and singles, with some doubles sprinkled in for good measure. You can surmise that the other gets a ton of value from home runs — look at that slugging percentage — but does worse elsewhere.

Oh yeah, a few other caveats. These are underlying talent levels; you might look at Player A and say that the BABIP can’t continue, or Player B and say the HR/FB rate can’t be real, but for our purposes, these are the lines they’ll put up over 1,000 PA, or 10,000, or 1,000,000. This is their real skill level. Given that, in most cases, it doesn’t matter much which one you choose, because they’re about the same. That’s the point of wOBA, after all.

That’s not a very interesting answer, so I decided to go deeper. I constructed a generic American League lineup. I removed intentional walks so that we’re comparing apples to apples. The result looks like this:

I threw that lineup into a lightly modified version of my lineup simulator, a short snippet of code that lets you put in a lineup (based on the probability of each outcome every time they bat) and get an estimate of how many runs they’d score per game. This one comes out to 4.53 runs per contest, which is close enough to the actual AL average for my purposes. Read the rest of this entry »

If you’ve read any of my articles of late, you would know that I am currently fixated on plate discipline. My piece on Jarred Kelenic sparked an article on take value and hitter approach. After that, a discovery that Darin Ruf is succeeding with one of the lowest swing rates in baseball despite not having phenomenal plate discipline on the surface inspired research into zone-swing differential and what it may tell us about a hitter.

Under the plate discipline section of player pages and on our leaderboards, we list both O-Swing% and Z-Swing%. On a handful of occasions, though, writers here have used zone-swing differential. Chet Gutwein defined this stat as D-Swing% in his piece about the NL West, and Justin Choi wrote about it in an article on the Blue Jays’ aggressiveness in early counts. In my most recent piece on Ruf, I cited zone-swing differential to conclude that while his overall swing rate is low, his discipline might not actually be that good, as he’s still swinging at a fair amount of pitches outside the zone, which you can see when you look at his below-average D-Swing rate.

The idea behind D-Swing% is simple: Hitters should be better when they swing at strikes and take balls. This isn’t the only way to succeed at the plate, but you would think that better hitters would have higher D-Swing rates on average. There were a couple comments about D-Swing rate on my Ruf piece, and that inspired me to look into it further. Is this a stat that tells us more about hitters than what we already have with the standalone O-Swing% and Z-Swing% stats?

This exact question was actually explored on the FanGraphs community blog back in 2017, where user Dominikk85 broke hitters into top- and bottom-30 groups by wRC+, ISO, OBP, and BABIP to see if O-Swing%, Z-Swing%, or what they referred to as Z-O-Swing% had the biggest impact in explaining the difference between the groups. They found that being more aggressive in the strike zone “helps the power but seems to slightly hurt the OBP,” but overall, they saw an advantage in using D-Swing% over the individual components.

I ran a similar study, but I wanted to control for more variables — zone rate and contact rate — to isolate the effect of D-Swing%, as many plate discipline metrics are interrelated. You may swing less at pitches in the strike zone if you are seeing more pitches outside, or you may choose to swing at pitches in the strike zone with which you can make contact (preferably hard), which may lower your Z-Swing% but raise your Z-Contact%. Without at least attempting to adjust for some of these variables, we may be missing out on conscious hitter tendencies that may be more the result of the pitches that they are seeing rather than their inherent swing choices. Read the rest of this entry »

I will never not be fascinated by the fact that hitters actually produce negative value when swinging. In my most recent article outlining the struggles of Jarred Kelenic, I briefly discussed this idea. Even in a sea of hitters who are below-average when swinging, Kelenic stands out as being particularly bad when he takes a hack; he’s been worth roughly -6 runs per 100 swings so far this year. And as I noted, the hitters who do the best at limiting the damage on their swings tend to be baseball’s most productive hitters overall. From that research, I found an R-squared of 0.714 between a hitter’s run value when swinging and their seasonal wOBA.

That makes a ton of sense: Hitters who maximize their production on swings — that is, both limiting whiffs and making frequent loud contact — tend to be better hitters overall. But this also got me thinking about the reverse: How does taking pitches influence a hitter’s overall production? From the Kelenic research, I found only a moderate correlation between take value and seasonal wOBA, with an R of 0.422 and an R-squared of 0.178. That’s not to say that better “takers” aren’t better hitters; it just suggests that having extremely high-value takes doesn’t necessarily lend itself to having more success overall. For posterity’s sake, here’s the plot of 2021 hitters’ run value per 100 takes and their seasonal wOBAs. Players on both ends of the wOBA spectrum are highlighted just to demonstrate a few individual examples: Read the rest of this entry »

I wish I could come to you with news about the mass adoption of backup sliders from pitchers across the league. I also wish I could come to you with a shining example of even one player who has perfected the art of the effective mistake. There is no apparent analytical case that comes to mind; it’s largely just a fascination to me — the best hitters in the world swinging through the worst offering a pitcher could imagine throwing. Here’s an example:

Matt Wisler is the perfect guy to have here — someone who throws nothing but sliders making the biggest kind of slider mistake! And yet there are times when it just works. What I want to try to answer is what makes these mistake sliders click without diving into the rabbit hole of pitch sequencing. Are there particular characteristics of movement and velocity that make for better backup sliders?

First, we have to set guidelines on what a backup slider is. You know it when you see it, but it is more broadly defined as something that “hangs” when thrown to a batter of the same handedness. Sliders behave differently depending on whether they’re thrown inside or out, as shown by Eno Sarris on this site a few years back; those away gain almost half a foot of horizontal movement compared to ones thrown inside! Sliders need height to be considered mistakes, but the distinctions in horizontal movement are too vast for an outside-and-up slider to be as bad a mistake as one up but inside. For our purposes, let’s say that a backup slider is anything in the upper third of the strike zone, middle-to-in, in a same handedness matchup. Read the rest of this entry »

There’s this episode of SpongeBob Squarepants that I love, in which Mr. Krabs’ snowballing desire for jellyfish jelly causes SpongeBob to catch more and more jellyfish until none remain. I bring this up because I like to imagine front offices as Mr. Krabs: Over the past few years, they’ve been shifting against more and more hitters, with seemingly no end in sight.

It turns out, however, we might have already reached the peak of infield shifting, at least in terms of volume. Comprehensive shift data dates back to 2016. Since then, here’s the rate of shifts against left- and right-handed batters each season:

This season, we’ve reached a point of stagnation. Teams haven’t budged from the mark they set against lefty hitters in 2020. Moreover, after a steady year-to-year increase, the rate of shifts against righty hitters has actually dropped. What I find more interesting — and ultimately want to dissect — is the latter trend. That teams aren’t looking for new lefties to shift against makes sense, since there’s presumably a limited pool. But righties demonstrate pull-side tendencies, too. If we assume teams are shifting mainly based on pull rates, we’d also expect the number of shifts against righties to keep climbing.

Read the rest of this entry »

The other day, I was listening to an episode of Rates and Barrels, an always informative baseball podcast on The Athletic hosted by Eno Sarris and Derek Van Ripper, and learned something new. The two went over each team’s ‘Location+,’ a metric developed by Max Bay that quantifies pitcher command, with teams like the Brewers, Giants, and Rays recording the highest marks. That’s no surprise; what did surprise me is that the Padres stood out as being uniformly bland, receiving average grades for every pitch type except cutters.

San Diego’s’ pitching staff is underperforming, injured, and recently experienced a change in leadership. But I figured it’s still one of the league’s better ones. Since Location+ is proprietary, I can’t consult the exact numbers, though it did inspire me to look at where Padres pitchers had been locating their pitches. And in doing so, I came to a realization: They might have a four-seam fastball problem.

Pitchers perform differently depending on the count; they’re great when they’re ahead, about average when even, and terrible when behind. Unless a microscopic sample size is involved, this principle applies to pretty much everyone. So when looking at how Padres pitchers have performed by count, these results shouldn’t seem out of the ordinary:

Consider, though, how they compare against the league averages. The Padres are comfortably better than the average pitcher when ahead in the count, but the same can’t be said for other instances. In disadvantageous situations, they seem mediocre at best, and the whole picture is underwhelming. You might have guessed where I’m going with this, but basically, the idea is that four-seam fastballs are to blame. Here are the wOBAs against them by count, along with where the Padres rank league-wide. I’ve also included xwOBA to isolate the effects of batted ball luck:

As the kids say, this ain’t it. A .193 wOBA against four-seamers once ahead in the count is great. But a whopping .500 wOBA after falling behind is… not so great. The gap does narrow with xwOBA as the metric of choice; after all, part of the Padres’ recent struggles are due to good players underperforming, which is naturally fixable. But there’s a significant gap nonetheless, and it does seem tied to how they are locating their fastballs. For the sake of time and sample size, I focused on the team’s starters with 50 or more innings pitched. If we examine where their fastballs have ended up, perhaps we can also analyze why they have been hit hard.

Alright, enough talk. You’re here for the meat and potatoes. First up is Blake Snell, whose fastball locations I categorized by count type and batter handedness, presented from the pitcher’s point of view:

You can see that he likes to live higher up when ahead in the count, which is ideal, since batters are more likely to chase. Otherwise, however, Snell’s fastballs are heading straight down the pipe. Even his higher fastballs are still squarely in the strike zone; with the amount of ride he generates, he can afford to climb the ladder more often, a feat he accomplished in previous seasons. He’s also all over the place, which the wide contours illustrate. The command isn’t quite there, and it shows.

Next is Yu Darvish, the Padres’ other ace. Unlike Snell, his four-seamer isn’t his primary pitch, but it still accounts for around 20% of his repertoire. Another detail to note is the wOBA against his four-seamer by month:

• April: .328
• May: .178
• June: .066
• July: .398
• August: .513

After appearing invincible in June, the four-seamer has spiraled out of control in recent months. Because the downward trend coincides with the crackdown on sticky stuff, though, it’s easy to think Darvish’s heater has become worse. That’s true, but not markedly so. An average spin rate of 2,577 rpm before the June 15 ultimatum is now down to 2,473, and it only cost Darvish about an inch of ride, which isn’t all that significant.

There hasn’t been a change to how he’s locating his heater, either. But maybe there should, because Darvish seems like another pitcher who isn’t capitalizing on the vertical movement he generates:

When ahead in the count, Darvish is hitting the outside corner against lefties and righties alike, but besides that, there’s not much else in terms of location. And like Snell before, the high fastballs aren’t really all that high. The contours are also wide and scattered across the strike zone, which might suggest a lack of strategy. I could be reading too much into it, but even at a glance, those heat maps aren’t very appealing.

Joe Musgrove is similar to Darvish, in that the four-seamer acts as a secondary pitch but is nonetheless an integral part of his arsenal. Without it, his fantastic breaking pitches probably aren’t as attractive. So how does he locate the heater? Here’s a look:

That’s better! Those ahead in the count fastballs, they’re up (sort of), but at least they aren’t centered around the heart of the zone. I also appreciate how Musgrove is seemingly exploring the bottom third of the zone when behind, as a way to sneak in a called strike or two.

In his case, though, the stuff is arguably a greater issue than command. Despite an elite raw spin rate, Musgrove doesn’t actually generate much vertical movement on his heater; in fact, it’s one of the league’s worst relative to his velocity. This is presumably why he has continued to shy away from it, gradually replacing his four-seamers with cutters and more breaking balls. Maybe right now demonstrates the best usage of it; I’m not entirely sure. But among Padres starting pitchers, his fastball woes are the least severe.

Then we move onto the youngsters, Chris Paddack and Ryan Weathers. To avoid beating the same drum for too long, I’ll sum up Paddack with words: He probably can and should live up in the zone more often, but there’s been a snag in his stuff. After a solid rookie campaign, his fastball lost a ton of vertical break in 2020, and as far I can tell, he’s still working toward returning to those 2019 levels. The ERA and dearth of strikeouts this season are concerning, but it’s doubtful he’s this ineffective of a starter moving forward. We’ll give him a pass.

On the other hand, Weathers sticks out like a sore thumb. It’s okay that this is his first season in the big leagues. It’s not okay that every pitch he has — fastball, slider, and changeup — ends up in a terrible spot. He’ll figure it out as he accumulates innings and experience, but for now, here’s a slice of reality for the Padres:

Those aren’t good areas to place a fastball even with superb movement, which unfortunately Weathers has lacked so far.

But let’s put everything we’ve explored into context. What’s an example of good fastball command, and how does that turn out when visualized? Originally, I’d planned a comparison between the Giants’ and Padres’ fastball locations, then scrapped it after realizing how daunting the task would be. There’s a useful remnant, though. Below is a heat map of Johnny Cueto’s four-seamers this season:

It’s the year 2021, and Cueto has a higher whiff rate and a better run value on his four-seamer than Snell. Yes, Cueto uses his less frequently, but consider where they’ve ended up. Ahead in the count, those fastballs are perched right on top of the strike zone, with a tendency to veer away from right-handed hitters. Naturally, they aren’t as high up when the count is even, but remember, that’s where Snell roamed after getting ahead, not even. And even when behind in the count, Cueto has done a solid job of avoiding the bottom third of the zone.

If you buy pitch location as a reason for the Padres’ pitching woes, their unexpected dismissal of Larry Rothschild makes a bit more sense. There’s not much a coach can do about a pitcher’s stuff; no decree will magically add three inches of movement to a slider. Location, however, is within his realm of control. To wit, Mets pitchers in 2018 went from generally avoiding the inner half to thriving there, which then-pitching coach Dave Eiland had emphasized. Over time, perhaps the Padres realized Rothschild’s own philosophy was doing more harm than good.

I’m not 100% sure if location, let alone fastball location, is the main culprit. Heat maps are hardly an exact science; they’re approximations of a pitcher’s command whose gaps are colored in by a model and charted. They also don’t factor in pitch sequencing, another element a pitching coach could influence. So maybe this is all wrong! But two facts remain true: (a) the Padres, in general, haven’t been able to avoid dangerous fastball locations; and (b) their fastballs are either getting smacked or taken for balls. If they do indeed need help, it needs to come fast.

I’ll warn you up front: this article is going to be a loose description of some research I’m working on, plus a copious amount of rambling. I’ve been looking for non-handedness platoon effects a lot recently. Partially, it’s because they’re fun to look at. It’s also because the Giants seem to be using some non-handedness platoons to good effect this year — they’re certainly doing more than just picking left or right based on the opposing pitcher.

I haven’t finished exploring this one yet. So why write an article about it? People like to read articles — but also, I get a lot of good ideas from reading the comments (this being perhaps the only site on the internet where that’s a reasonable sentence) and I could use some inspiration in terms of more things to do here. Without further ado, let’s talk inside/outside splits.

Listen to a game, and you can’t miss it. Announcers will tell you that some players are adept at taking an outside pitch and hitting it the other way, or turning on anything inside and giving fans some souvenirs. I split the plate into thirds, then used those thirds to define three zones: anything on the inside third or off the plate inside is “inside,” anything on the outside third or off the plate outside is “outside,” and the rest is the middle.

Here’s something right off the top: Bryce Harper has destroyed inside pitches this year. He’s seen 441 of them and produced 22 runs above average. That counts good takes as well as solid contact, but his batting line is spectacular, too — .367/.480/.735, good for a .497 wOBA. Give him something he can pull, and it’s all over but the crying.

If you can manage to stay on the outer third, you have a better shot. He’s seen 864 pitches out there (pitchers aren’t dummies) and produced 9.2 runs above average, a far lesser line. That’s still solid — he’s an MVP candidate — but it’s nowhere near the scorched-earth stuff he manages on inside pitches. Read the rest of this entry »

Sinkers (or two-seamers, as they’re also called), are a mixed bag. Maybe it’s just me, but they seem to produce polarizing results. They’re used by the most mediocre of control artists and the league’s best pitchers alike. They’re responsible for some of the slowest as well as the fastest, well, fastballs – just watch teammates Adam Wainwright and Jordan Hicks. When a pitcher lobs a bad sinker, hard contact seems inevitable. But when a good sinker is dangled as bait and the hitter bites, there’s no escaping that darn infield.

Extremes can work. They’re also risky, which is why the average pitcher relies on a four-seam fastball. We know what makes that pitch tick, and it slots into any arsenal. Sinkers are trickier to tame, which helps explain why pitchers have shied away from them in recent years. But as I explored earlier this year, a decline in usage does not equal a decline in relevance. If anything, the emphasis on seam-shifted wake has piqued the sabermetric community’s interest in sinkers.

When I wrote the article I referenced above, I was left with a few unanswered questions. For example:

“That being said, I’m not sure if higher sinker velocity correlates to better results, whether that be in terms of wOBA or Run Value… [a]t a glance, there’s no significant relationship between sinker velocity and xwOBA allowed (r^2 = 0.04).”

Immediately, there’s a flaw within that finding. I’d measured the relationship using pitchers who threw sinkers, not the sinkers themselves. It’s possible a pitcher possesses the makings of a good sinker but struggles with command. This time, I got down to business. I had pitch data from the 2018-19 seasons from an earlier project, so that became my sample. One caveat, though: I only included sinkers that resulted in batted balls. For the most part, the intended purpose of a sinker is to generate soft contact, and I felt including whiffs, fouls, and other results would produce murkier conclusions. Read the rest of this entry »

The playoff race is heating up and for teams still competing for the postseason, the stakes are the highest they’ve been all season. The spotlight shines especially bright on high-leverage relief appearances in this environment. Unfortunately for the Padres, their All-Star closer Mark Melancon took a small step backward in yesterday’s matchup against the Athletics. Melancon entered the ninth with a two-run lead but allowed two runs on three hits and a walk. The A’s went on to win 5-4 in extras. Despite the setback, Melancon has been one of the best closers in the league in 2021, converting 32 of his 37 save opportunities and leading all of baseball in saves. This season, however, has seen a ton of blown leads in late innings. In the past two seasons, save conversion rates have plummeted, diving from a stable range of 66-70% from 2002-18, down to as low as 61.7% so far this season.

At first glance, it’s easy to point to the expansion of the active roster to 26 players and an influx of injuries as the reason for baseball’s poor performance in closing out games. Save opportunities are being distributed much more widely than in the past. The chart above shows that the drop in save conversion rate actually begins in 2019. The days of multiple workhorse closers meeting the 40-save benchmark are gone. Even getting 40 save opportunities has been elusive for all but a handful of pitchers:

Read the rest of this entry »

There is an old adage in baseball that changing a hitter’s eye level pitch-to-pitch will lead to better outcomes for the pitcher. This makes sense on its face: compared to varying pitch heights and forcing a hitter to alter his bat path, throwing two consecutive pitches at the same height should make it easier for a batter to square up the ball. In a New York Times piece by Tyler Kepner, Mike Mussina discussed the importance of varying locations pitch-to-pitch to mess with the hitter’s eye, offering the example of throwing fastballs down and then countering with a pitch up in the zone. Kepner noted that the hitter’s eye would then be trained on a pitch higher in strike zone, affording the pitcher the opportunity to throw a curveball down to induce a groundball, or net a swing-and-miss. David Price has expressed a similar sentiment: “That’s always a big emphasis [for] me, just making sure I’m hitting spots with that fastball – two-seam, four-seam, both sides of the plate, moving it in, up, down.”

In research on the effect of eye level change on college hitters’ performance against fastballs, Higuchi et al. found that quick eye movement as a pitch traverses towards home plate has negative consequences for the hitter. This research was included in Driveline Baseball’s examination of hitters’ gazes when standing at the plate. On these pages in 2015, Jonah Pemstein looked into whether a pitch thrown at a different height than the one that followed it affected how umpires called the pitch at hand. Permstein surmised that this was indeed the case, with umpires less likely to call a pitch a strike at any height if the previous pitch was thrown at a different vertical location.

As I said up top, this all makes intuitive sense. But does it hold up to further scrutiny? The research I cited by Higuchi et al. only included six collegiate hitters and only considered fastballs. While their work was extremely thorough, its scope didn’t consider the hitter population many of us are most interested in (major league hitters) and only included fastballs at a time when pitches are leaning on breaking balls and offspeed pitches more than ever. Pemstein’s research looked at umpires, not hitters; his conclusions give us some confidence that behavior changes when pitchers vary their pitch location, but doesn’t provide insight into the strategy’s ability to flummox batters. I decided to delve into the data myself and see if there was any merit to this fundamental aspect of pitching strategy.

Using Statcast data from the past three seasons, I constructed various pitch sequence parameters to gauge the efficacy of changing the hitter’s eye level. The first parameter involved pitches that were in the strike zone, as defined by the MLB Gameday zone. Pitches in zones 1, 2, and 3 were coded as “up,” zones 7, 8, and 9 as “down,” and 4, 5, and 6 as “middle.” All other zones were considered off the plate. I focused on pitches in the strike zone because we know hitters are more likely to swing at those pitches and generally have success when they do. The in-zone swinging strike rate over this sample was 12.1%, while 28.1% of these pitches were put into play. Batters had a .349 wOBA on pitches inside this strike zone versus a .304 wOBA outside of it. Any degradation in performance on pitches inside the zone would be a real value-add for pitchers. Read the rest of this entry »