The Rays have been really good this year, but their bullpen has not. And this has come despite them largely sticking to their strategy of deploying relievers with a wide variety of different release points: two sidearm righties in Kevin Kelly and Ryan Thompson, one sidearm lefty in Jake Diekman, an over-the-top righty in Pete Fairbanks, and an over-the-top lefty in Jalen Beeks. This strategy has succeeded in the past: from 2020 to ’22, Rays relievers have never posted an ERA higher than 3.37; their collective ERA of 3.31 in that span is second only to the Dodgers (whose bullpen also happens to be struggling this year); and their FIP of 3.71 ranks third.

This year, things are different. Even with some modest improvements of late, Tampa Bay’s 12th-ranked 3.83 ERA belies ugly peripherals, including a 4.43 FIP that ranks sixth-worst. The rotation has been ravaged by injuries, forcing the team to turn to the bullpen earlier in games; Rays relievers have tossed 1.26 innings per appearance on average this season, the fourth-highest in the league. But some of this is due to the usage of followers, which is nothing new; perhaps what’s behind the drop-off is simply too much of an emphasis on forcing opposing hitters to deal with different looks.

The Blue Jays, perhaps unintentionally, have taken this lesson to heart. Having signed one of the best splitter-throwers in Kevin Gausman prior to 2022, they traded for another this past offseason in Erik Swanson. Despite the league-wide increase in splitter usage due to its potential as a platoon-neutral offering for pronating and sweeper-throwing hurlers, only 2.2% of all pitches thrown so far this year have been splitters. Given the uniqueness of the pitch, the Jays should have Gausman and Swanson throw on different days to maximize the surprise factor, right? Just as the Rays might save two similar sidearmers for different days? Read the rest of this entry »

Whether there is a luddite rebellion, a scouting counter-revolution, or some other attempt at rolling back the technological advances the game has seen in recent years, last week I detailed why it makes sense for pitchers to adjust to new technology right now. Sure, hurlers could wait for a tech nullification or, in its absence, a new kind of tech to level the playing field, but as things stand, the scales are tipping.

Throughout the pitch-tracking era, hurlers have stood to benefit more than hitters from analytics thanks to highly customizable pitching plans. In recent years, motion-capture systems have helped optimize pitchers’ mechanics in addition to their repertoires. But this year, a significant number of teams have unearthed another use of limb-tracking software: in-game pitch tipping. Essentially, machine learning identifies subtle differences in muscle activation in real-time, typically due to different grips across pitch types, while the pitcher is still holding the ball in his glove. After a quick relay system, that information reaches the field, and the batter can then look to the dugout or a base coach for some indication of what’s coming.

To counteract this, I suggested pitchers mix up their wrist action and finger pressure right before release; by the time the KinaTrax systems pick up what’s going on, it’ll be too late. Sadly, I don’t have access to metrics like breadth of wrist action and finger pressure relative to grip, so I decided to come up with my own way of identifying pitchers who are already doing what I would recommend. I theorized that spin axis could be a proxy for grip, since the point about which a thrown baseball spins is heavily reliant on how the pitcher holds it. The direction and magnitude of movement is also closely related to spin axis, but there isn’t a one-to-one correspondence, so I hypothesized that pitchers with wide variability in movement despite minimal spin axis variability could be adding some extra mustard after separation. Read the rest of this entry »

The context behind the phrase “pitch tipping” has grown richer every year. Sure, the basic principle still holds: a pitcher is “tipping” when they’re providing some indication of their upcoming offerings. It’s just that opponents can glean such “tips” through a continuously expanding network of avenues. Previously, the only [clears throat] legal way to do so was when a second-base runner or base coach picked up on a catcher’s signs, or a starting pitcher’s tendency to wind up differently for a fastball or a breaking ball. Then, with the advent of PITCHf/x and later Trackman and Hawkeye, analysts and machine learning algorithms could search for tips to cue their hitters — when Pitcher A throws from a higher release point, there’s usually a fastball coming; when he shortens up his stride, there will probably be a breaking ball.

Next, the Trajekt pitching robots made it so that not only could coaches convey these cues to their hitters, but they could demonstrate how to use them to their advantage in real time. Integrating near-perfect trajectory replication with video of each pitcher’s windup, a pitcher facsimile completes their follow through at a mobile slot — adjustable in three dimensions for different release points and extensions — from which a batting practice baseball is launched. Still, pitchers can make in-game adjustments and at least avoid falling prey to the Trajekt machine for one start at a time, and the use of PitchCom makes it harder for runners and coaches to become privy to signs in-game. Maybe all of that can at least spare the pitcher an inning?

Now, I’m not so sure. Last week, Sports Illustrated’s Tom Verducci described team executives and coaches who are spending more time combatting their hurlers’ tipping than ever before. That’s because of markerless motion capture systems installed in as many as 15 big league ballparks. There are supposedly safeguards against using these KinaTrax systems for sign stealing, safeguards that dovetail with PitchCom’s effects, but the cameras go far beyond their intended purpose of preventing injury and sharpening up mechanics. Verducci describes an example, relayed to him from a front office executive: pitch grip influences which forearm muscles activate and how much they activate, even while the ball is still in the pitcher’s glove; once analysts or machine learning algorithms match each flexion pattern to a particular pitch type, that information goes straight to the dugout, and then to the hitters. Read the rest of this entry »

Last week, in his update on pitch mix, my colleague Davy Andrews told a tale as old as time (or at least 2008, when the pitch-tracking era began): fastball usage is on the decline, largely to the benefit of sliders (and sweepers). But this year, there’s a new wrinkle: cutter usage is on the rise, too.

Per Statcast, pitchers are turning to cutters more than they have in any season since 2008. That usage is still at just 7.6%, but the year-over-year jump of 0.7% is the second highest on record. Plus, as Davy and the folks over at Prospects Live put it, a lot of the cutter’s value lies purely in its mere existence; it doesn’t have to be used a ton to be a worthwhile offering. That’s because, out of a pitcher’s hand, the cutter bears resemblance to both a fastball and a slider. When pitchers are struggling to tunnel their heater and slide-piece, the problem might be that there’s simply too large a movement and/or velocity gap between the two offerings — one that a cutter can bridge. As long as the hitter has to think not only about the dissimilar fastball and slider, but also a cutter, it can make a big difference, even if the cutter doesn’t show up all that often.

Along those lines, horizontal movement gaps between fastballs and sliders have only grown in the age of the sweeper. The new “riding slider” also tends to have larger platoon splits than other similar breakers, which is where the cutter can come in handy yet again due to its relative platoon neutrality. Read the rest of this entry »

Offense is up this year. That’s partly the result of more home runs due to a baseball with less drag. It’s also due in part to the bevy of new rules; while the shift ban hasn’t quite returned us to the golden age of groundball singles, it has at least increased BABIP over recent years, and the bigger bases and pickoff rules have revamped the running game.

However, one major rule change with an as-of-yet undetermined impact on offense remains: the pitch clock. As my colleague Ben Clemens pointed out in the article on rule changes linked above, the impact of clock violations has been minimal. While the clock has likely contributed to the barrage of stolen bases, as the pitcher has less time to divvy up their attention between the hitter and the runner, it’s difficult to separate its effects from those of the disengagement limits. One fear that has been batted around is that the decrease in time between pitches is putting more stress on pitchers’ arms; having to rear back and deliver a pitch every 15 seconds without the opportunity to catch your breath whenever you need to can tire muscles out quicker and lead to a mechanical breakdown. But while the injury data is inconclusive so far, there’s another measurable area in which the impact of throwing pitch after pitch with little respite could show up: long plate appearances. Read the rest of this entry »

On Wednesday night, an eight-run seventh-inning outburst against the Yankees catalyzed another Orioles win, bringing their record to 32–17, second best in the majors. Among the highlights in the frame: Adam Frazier brought home three with a shot off the foul pole, and Gunnar Henderson plated two with a pinch-hit double. Understandably lost in the scrum was the first hit in the rally, a 109.6 mph scorcher off the bat of Austin Hays that snuck through the infield.

With two hits in four at-bats Wednesday, Hays brought his season slash up to .308/.351/.484 and his wRC+ to 131. The excitement that has come along with the Baby Birds — namely Henderson, Adley Rutschman, and Grayson Rodriguez — has overshadowed what looks to be a breakout season for the left fielder despite his own former prospect status. It’s easy to forget that back in 2017, Hays homered 32 times as a 21-year-old between High-A and Double-A, earning a call-up without a single Triple-A appearance. He was the first 2016 draftee to make the majors. Read the rest of this entry »

After a doubleheader sweep against the Guardians Sunday, the Mets have stretched their winning streak to five and stand two games above .500 for the first time since May 3. In the double-bill, co-aces Justin Verlander and Max Scherzer combined for 14 innings while allowing just one run. Granted, they mowed down the league’s worst offense by wRC+, but for fans, the pair’s dominance was a sight for sore eyes: Verlander missed all of April with a shoulder issue and Scherzer has had to work around a sticky-stuff suspension, a neck issue, and general ineffectiveness this year. Given these obstacles, it’s reasonable to wonder whether Sunday represented the turning point fans were hoping for or a mere blip in a season of decline for the veteran hurlers. To protect against the latter, the Mets will have to take a hard look at their starting pitching depth.

Coming into the season, we ranked the Mets starting staff as the second-best in the league, right behind the Yankees. While the Bombers have had to weather some injuries of their own, their starters have still managed to post an above-average WAR, FIP, and ERA. Head across the city to Queens, though, and you’ll find the second, third, and sixth-worst staff by WAR, FIP, and ERA, respectively. Clearly, the problems aren’t limited to their top two starters.

Due to ailments of their own, the Mets have gotten just 18.2 frames out of José Quintana and Carlos Carrasco combined. But one reason we liked the Amazins’ staff so much to begin the season was their enviable depth — their sixth, seventh, and eighth starters in David Peterson, Tylor Megill, and Joey Lucchesi easily could have started the season as part of the top-five somewhere else. The three of them have all been healthy, and along with Kodai Senga and the (inconsistent) contributions of Scherzer, that alone should have made for a serviceable starting five through April. Read the rest of this entry »

Paul Goldschmidt’s 2022 was a year for the ages, literally: the Cardinals’ first baseman defied senescence to post a 7.1 WAR and 177 wRC+, numbers which respectively tied for the 25th-best season among hitters 34 and older and the 15th-highest among those same elders with at least 500 plate appearances since 1920. This year, the slugger has largely picked up where he left off, with a 164 wRC+ through his first 186 trips to the plate. And according to xwOBA, he’s been significantly better than last year.

In case you’re not familiar, Weighted On-Base Average (wOBA) evaluates overall offensive performance in one stat, using linear weights to measure the relative value of each offensive outcome and then putting that number on the same scale as OBP. xwOBA, a product of Baseball Savant, combines a hitter’s walk and strikeout numbers with a prediction for how they should have faired on balls in play based on launch angle and exit velocity.

Last year, Goldschmidt put up a career-best wRC+, but xwOBA was telling us that some of that was smoke and mirrors: his .367 mark was well shy of his actual wOBA of .419. That 52-point divergence was the fifth-highest overperformance among hitters with at least 500 plate appearances in a single season since the introduction of xwOBA in 2015. Entering his age-35 season and due for some regression, I dismissed the idea of another big year from the first baseman. Read the rest of this entry »

We all know about the shift ban. This year, nary a shortstop can be found on the right side of the infield, nor a second baseman on the left. (Except, of course, for Mookie Betts.) Why, then, are we still denoting so many balls in play as occurring during “traditional shifts” on our splits leaderboards?

For those of you who don’t know, we have a great glossary section on this site. It’s what I used as a budding baseball nerd to understand what the heck Jeff Sullivan was saying about James Paxton back in 2016. In this case, I’ll point you to the section on our shift data. A traditional shift, brought to us by the folks over at Sports Info Solutions, does include the typical Ted Williams variety; that is, three infielders playing on the hitter’s pull side, a tactic used in the hopes of thwarting the great Red Sox outfielder’s offensive prowess (to little avail). Traditional shifts also include those where one infielder — usually the second baseman — is playing at least 10 feet into the outfield. These two flavors of shift are both obsolete now.

But the “partial” Ted Williams shift is having its heyday. When a shortstop (for a lefty) or a second baseman (for a righty) is “shaded” up the middle, with the corner infielder moving over towards the vacated spot halfway between the bases, that’s a partial Ted. Now, without the option to use a full Ted, teams are largely employing its partial cousin in its stead. Excluding non-traditional shifts (situational shifts — such as corners in, double-play depth, etc.), the ratio of shifts to no shifts on balls in play last year was just over 1.5 to 1. This year, it’s only dropped to just under 1.4 to 1. Hardly what you’d expect from a shift “ban.” Read the rest of this entry »

Though it feels like Opening Day was just yesterday, we’re officially a month into the 2023 regular season. On the macro level, that means the disappointing and surprising players are already starting to come out of the woodwork. More specifically (and importantly for writers like me), we’re at the point in the season when hitters are routinely cracking the century mark in plate appearances and pitchers are notching 35 innings.

Yet in some ways, this juncture is almost more maddening than Opening Day; we’re still in small-sample-size territory, but enough baseball has been played that we’re tantalizingly close to being able to take a hard look at some of the narratives being spun. For the time being, though, it still makes more sense to look at changes in approach rather than surface-level stats to predict rest-of-season production.

So I returned to a project I started this offseason — analyzing pitcher arm slots — to examine some hurlers who’ve made discernible tweaks to their release in accordance with early shifts in their performance. The equations I used to calculate these numbers can be found here. Read the rest of this entry »