Pitchers mostly don’t throw sweepers to opposite-handed batters. Starters especially don’t throw sweepers to opposite-handed batters. To put a number on it, 227 starters threw 250 or more pitches to opposite-handed hitters in 2023. Only 18 of that group chose sweepers even 10% of the time. Everyone knows the math: It’s the kind of pitch that simply doesn’t work when opponents get a clean look at it.

Want further proof? When pitchers have deigned to throw this suboptimal pitch, they’ve gotten punished for it. Per Baseball Savant, starters threw 4,734 oppo sweepers and accumulated 43.6 runs of negative value relative to average for their troubles. In other words, it’s generally a poor option. It’s not quite “break glass in case of emergency,” but it’s not far off. Starters rely on changeups, splitters, vertical breaking balls, or cutters to get by; anything to avoid throwing sweepers.

Okay, now that I gave you that setup, here’s the deal: It’s not universally true. Two Cy Young contenders and a top prospect have bucked the trend, throwing sweepers with relative abandon and getting away with it. What gives? Let’s look at each in turn. Read the rest of this entry »

Last year, I took a long look at the predictive power of rookie exit velocity. One of the things I learned was that for rookies with at least 200 balls in play, wRC+ was less predictive of their future performance than max exit velocity. That blew my mind. Knowing just one measurement, the velocity of a player’s hardest-hit ball, was more useful than knowing about their overall performance through their entire rookie season. Exit velocity matters a lot, as does how you interpret the data.

Since the rollout of Statcast in 2015, we’ve been introduced to three general ways of thinking about exit velocity, along with half a dozen individual variations. Depending on the context, we might read about a player’s average exit velocity, their maximum exit velocity, their hard-hit rate, or any number of exit velocity percentiles. For a while now, I’ve been wondering which one of these methods is most useful. Could there be one exit velocity metric to rule them all?

I have to imagine that at some point in the last several years, the R&D department of each major league team has asked itself that exact same question. In each big league city, someone much smarter than I am did the math and wrote up the results in a report that now rests comfortably in a proprietary database with a catchy name. The rest of us just have to make do with rumors and innuendo suggesting that teams most often value something akin to 90th-percentile exit velocity. To my knowledge, no one in the public sphere has made a comprehensive survey, and I wanted to look into the matter for myself. Read the rest of this entry »

It’s been almost two years since I authored a visual primer on vertical approach angle (VAA) and almost three years since I first discussed VAA here at FanGraphs. The topic directly adjacent to VAA — horizontal approach angle (HAA) — has been marinating in my mind (and in my drafts) ever since.

In an auspicious turn of fate, while revisiting this draft, our beloved Eno Sarris reached out to ask if I had ever published anything about HAA. He had questions about Brandon Pfaadt — specifically, Pfaadt’s sweeper. Much was made about Pfaadt’s success this postseason, which can be attributed, in no small part, to his sweeper and how he weaponized it. (More on that later.) I was spurred into action. Thanks, Eno! Read the rest of this entry »

On October 18, Rawlings and MLB announced this year’s Gold Glove finalists. Conspicuously absent from the list were two electric young Royals: Bobby Witt Jr. and Maikel Garcia. The pair took to social media to voice their thoughts on the selections, with Garcia labelling the process “a joke,” team captain Salvador Perez backing him up, and Witt perhaps summarizing our collective thoughts most concisely with a simple thinking face emoji:

? https://t.co/WnoqeHe9qv

— Bobby Witt Jr (@BwittJr) October 18, 2023

What led to Witt and Garcia’s exclusions? Let’s review the Gold Glove criteria. The SABR Defensive Index, or SDI, is a proprietary blend of fielding metrics that comprises about 25% of the selection process, with the rest depending on the votes of the manager and six other coaches per team. These seven votes per team can only be allocated to qualified players within the same league as the team, but not players on the team. So, for example, Royals coaches can only vote for non-Royal American League qualified players. Read the rest of this entry »

This postseason, pitchers have allowed a .311 wOBA and a 3.74 ERA, down from .318 and 4.33 during the regular season. That part’s not terribly surprising. Since the start of the Wild Card era in 1995, the league’s postseason ERA is 3.85, nearly half a run below the regular season ERA of 4.29. The thing that caught my eye was that this year’s .311 wOBA is 21 points lower than its .332 xwOBA. In fact, for as long as we’ve been calculating xwOBA, wOBA has underperformed it in the playoffs:

This year’s gap is the largest, but it’s hardly an outlier. There’s a big gap between ERA and FIP during the postseason. Pitchers have outperformed their FIP 24 times in the last 29 postseasons. Over that period, they’ve run an ERA of 3.85 and a FIP of 4.15. They’re performing better overall, but they’re also outpitching their FIP to the tune of .3 runs per game. I started thinking about the causes that might explain these discrepancies, and I realized that our new postseason leaderboards would allow us to break them down in some cool new ways. Read the rest of this entry »

Ah, the playoffs. The smell of fall in the air, the sight of towel waving and packed stadiums across the country, and the endless stream of pontification on social media. Are the Rays just not built for the postseason due to a lack of star power? Have the Dodgers been playoff slouches because they’re too dependent on their stars? Do the Astros know something about how Martín Maldonado manages a pitching staff that we don’t? Do we know more about how to manage a pitching staff than John Schneider? The list goes on.

Especially with the new opportunities to weigh in given the expanded playoff structure, it’s been harder than ever to hone in on ideas worth pondering, let alone hypotheses that are falsifiable. But the other day, a xweet from MLB Network researcher Jessica Brand caught my eye:

Of the 145 pitchers with 50.0+ innings pitched in the playoffs, their average ERA changes by -0.32. Genuinely surprised it's to their favor too!

Worried about outliers? Median further cements this point, at -0.36.

44 of 145 go up, 100 go down, 1 stays the same: Catfish Hunter. https://t.co/AcqKFqyJyA

— Jessica Brand (@JessicaDBrand) October 9, 2023

Thanks to our handy new postseason leaderboards, this was indeed an interesting assertion that I could test. I limited my sample to hurlers who not only tossed at least 50 frames in the playoffs, but who also managed 500 innings in the regular season. There were 142 pitchers who met these criteria, and they averaged an ERA three tenths of a run lower in the playoffs. Per a paired-samples t-test, this result was statistically significant. Read the rest of this entry »

This all started because I wanted to write about Isaac Paredes. He’s my kind of player, excellent despite all sorts of warning signs that what he’s doing shouldn’t be working. Advanced metrics and in-person scouting assessments are both quite negative on Paredes, and yet he’s batting .255/.354/.503, good for a 140 wRC+, in mid-September. He’s been one of the most valuable players on one of the best teams in baseball. It’s so weird!

But lo and behold, the exact thing I wanted to write about has already been written. Curse you, Esteban Rivera! Well, not actually, of course. Esteban’s writing is great, and it’s also of particular interest to me because he’s so observant of hitting mechanics. But I can’t exactly write an article about how Paredes’ pull-happy tendencies have helped him keep regression at bay when there’s a better article talking about just that already on the site. Read the rest of this entry »

A couple of weeks ago, I saw Jonah Heim take a called strike that he felt should have been a ball. As a catcher, Heim knew better than to argue. Instead, he performed the delicate dance of the catcher who wants to make a point without showing up the umpire. I’m not sure if the clip below is the exact pitch I saw, but it’s certainly representative of the conundrum a catcher faces when he doesn’t like the strike zone.

You can see Heim duck his head and furtively say something to home plate umpire Doug Eddings. I like to imagine that whatever he says begins with, “I beg your pardon, good sir.” He doesn’t make a show of his displeasure. He asks something, Eddings nods his head yes, and everyone moves on with their lives. Still, Heim thinks he’s seen ball two, and it’s hard to blame him. Even the person operating the score bug got fooled.

For some reason, that little moment has been rattling around in my head. I tend to think too much about the relationship between umpires and catchers. It doesn’t seem possible that they could spend every night doing what they do in the proximity that they do it in without developing a bond. Read the rest of this entry »

In August, the Phillies hit 59 home runs, which is the highest total for a month in franchise history and tied for the third-highest total in any single month by any team in history. It was a remarkable performance, but perhaps not a particularly surprising one given how this roster was constructed; by design, Rob Thomson’s charges are large, strong, and (of late) increasingly shirtless. They were born to mash.

Last week, I had the good fortune to be present at Citizens Bank Park as five of those 59 home runs took flight in a single evening, off the bats of five different Phillies. This was one of those close, muggy summer nights that define the mid-Atlantic summer; with a pleasant, gentle breeze blowing out to left field, the ball was roaring out of the park. It wasn’t just the Phillies; the Angels dingered three times themselves. Two of those came off the bat of Luis Rengifo, hardly a man whose public stomps and chants are included in the Home Run Derby every year.

But as the Phillies laid 12 runs on their opponents, the play that stuck in my mind was the opposite of a home run. In the sixth inning, the Phillies batted around and scored six runs to turn a 4–2 deficit into an 8–4 lead; one of those came on a squeeze bunt by Johan Rojas. It was a lovely push bunt by a speedy right-handed hitter, the baseball equivalent of spreading room temperature compound butter on a slice of crusty bread. “Man, we should see that more often,” I thought to myself. Read the rest of this entry »

Some of the most frustrating arguments involving baseball statistics revolve around the use of expected stats. Perhaps the most frequently cited of these metrics are Statcast’s xStats, which use Statcast data for hitters to estimate the batting average, on-base percentage, slugging percentage, and wOBA you’d “expect” a hitter to achieve. Investigating how predictive xStats are compared to their corresponding actual stats has been a common research exercise over the last few years. While it depends on the exact dataset used, xStats by themselves generally aren’t much better than the actual stats at predicting the next year’s actual stats. But that doesn’t mean we should simply discard expected stats when trying to evaluate players.

While I’m not going spend too much time talking about how predictive xStats are versus the actual ones, I do want to briefly touch on some of the existing work on the subject. Jonathan Judge at Baseball Prospectus examined many of the expected metrics back in 2018. He also spoke with MLBAM’s Tom Tango about the nature of expected stats and their usage:

Earlier this week, we reached out to BAM with our findings, asking if they had any comment.

MLBAM Senior Database Architect of Stats Tom Tango promptly responded, asking that we ensure we had the most recent version of the data, due to some recent changes being made. We refreshed our data sets, found some small changes, and retested. The results were the same.

Tango then stressed that the expected metrics were only ever intended to be descriptive, that they were not designed to be predictive, and that if they had been intended to be predictive, they could have been designed differently or other metrics could be used.

Read the rest of this entry »