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 »

As a measure to improve baseball for the average fan — or even the decidedly non-average fans who frequent our pages — I think the pitch clock has been a resounding success. Trimming almost half an hour from the length of games hasn’t diminished baseball itself, with the cutting room floor mainly littered with the things that take place in between the action. Now, you can argue that we’ve also eliminated some of the dramatic tension from crucial situations in important games. But for every high-stakes matchup between two great players in a big moment, there were a multitude of unimportant ones stretched out endlessly by a parade of uniform readjustments and crotch reconfigurations. I enjoy having a leisurely Campari and soda with a friend while waiting for dinner, but I certainly don’t want to do that for every meal, and if I could chop down cocktail hour to get my food more quickly, I’d happily find other moments for social bonding.

Of course, game length isn’t the only consideration when assessing the pitch clock. I’m frequently asked in my chats if I think a given pitcher’s underperformance relative to expectation can be attributed to the clock. It can’t feel great to do a job for a number of years and suddenly experience such a monumental change in how you go about executing it. Steve Trachsel ain’t punching no time clock!

Another big question is whether the pitch clock, which can result in mechanical changes, could have an effect on injuries, a subject Will Sammon, Brittany Ghiroli and Eno Sarris explored for The Athletic after a high injury rate in April. While we obviously don’t have enough data to reach a verdict on the long-term effects of the clock (and things like Tommy John surgery count are still going to involve relatively small samples), as we near the halfway point of the season, we do have enough information to look at how the data are shaking out and arrive at some kind of preliminary conclusion about what’s going on. Read the rest of this entry »

One of the strange things about projecting baseball players is that even results themselves are small sample sizes. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero — individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is — but we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.

Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to 2019, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, while toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits. Read the rest of this entry »

Let’s pull back the curtain a little. I’ve been covering baseball full-time for about 10 years now, and in that time I’ve basically written five types of article over and over. Every sportswriter cranks out game stories and interview-based features, and at least two or three times a week, every FanGraphs writer pens a focused topical analysis punctuated by charts and jokes. I’m no different. Category no. 4 involves Political/social/economic commentary, since our sport is governed by the society it exists within, and should be analyzed accordingly.

Which brings up category no. 5: I become fixated on something weird or trivial that nobody else in the world cares about. And rather than throw out a joke tweet and forget about it like a normal person, I spend days and days finding, compiling, and analyzing data in a vain attempt to discover the truth. If a truth as such even exists. Then, indifferent to whether the readers of FanGraphs Dot Com — i.e. all of you fine folks — give a tinker’s damn about the subject, I post the results on this little corner of the internet.

Be warned, this is a category no. 5 post. Read the rest of this entry »

Last week at Baseball Prospectus, Rob Mains did some digging into home field advantage and found a very curious effect: home teams did worse in extra inning games than in regular-season games. More specifically, he found that home teams won roughly 54% of games overall but only roughly 52% of extra inning games. There are no two ways about it: that’s strange.

Mains looked into many potential explanations for this discrepancy: team quality, pitcher quality, games that were tied going into the ninth, and various ways of looking at how teams have adapted to the zombie runner era. Today, I thought I’d throw my hat into the ring with a slightly different way of thinking about why home teams are less successful in extras than they are overall.

My immediate thought when I heard this problem was something Ben Lindbergh mentioned on Effectively Wild: home field advantage accrues slowly, and extra innings have fewer innings than regulation. The minimum scoring increment in baseball is one run, naturally. Home field advantage is clearly less than a run per inning; it’s less than a run per game. I like to think of home field advantage as fractionally more plays going the home team’s way. A called strike here, a ball that lands in the gap instead of being caught there, and eventually one of those plays might put an extra run on the board. 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 »

The drama of a superlative catch at a crucial point in a game is one of baseball’s great narrative moments. A ball is struck and everyone – fans, baserunners, sprinting outfielders – holds their breath for a few seconds waiting for it to hit either leather or grass, sending those baserunners and swinging the game in one direction or the other. It’s baseball’s version of a three-pointer heading towards rim or net, or a wide receiver and a cornerback extending for the same airborne pass – a moment of suspense in the most literal sense of the term, during which the only thing drawing us closer to a conclusion is gravity.

Now, because there’s nothing baseball fans love more than taking a beautiful moment of athleticism, emotion, and aesthetics and distilling it into numbers, I’ve been mulling how to appropriately credit an outfielder for a play like this – particularly with respect to how it impacts the game in that moment. We have a pretty good measure for what a batted ball is worth if it falls in for a hit or is caught for an out, adding to one team’s chances of winning depending on the score and base-out situation – Win Probability Added. But what about when the ball’s in the air and it’s up to the outfielder to track it down? How much credit (or blame) is owed to the outfielder? How do we measure how much the outfielder’s defense itself swung the game? Read the rest of this entry »

There’s a growing stereotype that even the most unheralded reliever coming off the shuttle from Triple-A can pump triple digits and throw wipeout secondary stuff out of nowhere. We’ve seen plenty of examples of this phenomenon in the pitch data era, from the Rays developing Jason Adam into a high-leverage ace to Yennier Cano improving his ERA from 11.50 to 0.35. Baltimore and Tampa Bay are known for turning people off the street into elite relievers, but nearly every team is light years ahead of where the industry was just a few years ago. Of course, not every pitcher can have a 200 ERA+, but I wanted to see just how many replacement-caliber relievers really are the real deal. Let’s take a look at a nondescript game from earlier this week and find out.

On Tuesday, the Angels and Red Sox faced off. The two teams had played a rather close game through the end of seven innings. Boston starter Brayan Bello surrendered just two solo shots in the longest start of his young career, while his opponent, Griffin Canning, one-upped him with seven shutout frames. As the bullpens came in, the Sox still had a fighting chance to win… at least until Mike Trout clubbed a two-run homer off Joely Rodríguez, who would then allow two more runners to reach base. While just a one-run swing would make it a save situation, the leverage index sat at a measly 0.07. At this point, both teams went to the back of their bullpens, with the Sox summoning Justin Garza and the Angels letting Jacob Webb complete the game. Read the rest of this entry »

A lot of my job involves spinning a story I don’t completely believe in. I know, I know, you’re shocked! You mean I don’t actually think that the four to five players I highlight every week are each breaking out by doing something they’ve never done before? And I don’t think that each of them is doing it sustainably? What are the odds?

Some of that comes with the territory. If you’re looking across the universe of major league players for something interesting, some portion of what you find interesting will have happened by random chance. That pitcher who’s striking everyone and their mother out? He might just be on a hot streak. The hitter who’s currently smashing high fastballs? There’s some chance he just felt really good for a week and then will stub his toe when walking out of the clubhouse tomorrow.

I know all that. One thing I wasn’t sure about, though, was how often false signals pop up. Even without searching them out, you might end up seeing a breakout around every corner. There’s a famous quote from Nobel Prize winning economist Paul Samuelson: “The stock market has predicted nine out of the last five recessions.” Is the same general idea true of batted ball data? I came up with a simple experiment to investigate. What follows is a breakdown of the exact method I used, but if you’re just interested in the conclusion, it won’t surprise you: When hitters put up hot streaks of a reasonable length, it’s a good but not infallible sign that they will finish the year as above-average hitters.

I took every batted ball from the 2022 season and broke it out by player. From there, I put them all in chronological order and calculated each player’s best stretch of 50 batted balls. I calculated it for a variety of “advanced” metrics: average exit velocity, xwOBA, and barrels per batted ball. Those are some of the most commonly used underlying statistics – if I’m citing someone who’s really hitting, I’d likely use batted ball outputs like this to assess the validity of their performance, so I excluded things like batting average on contact or wOBA on contact, which might be quite noisy in 50-ball samples. 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 »