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 »

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 »

What makes you happy? Among the things that bring a smile to my face, talking about catching is up there. I will look for any excuse to write, talk, or think about catcher defense. I’m one of those people that has missed catching bullpens since I’ve stopped playing regularly. For those of you who do not know, that is not normal! So whenever Statcast drops new information about catcher defense, I have to write about it.

A few weeks back, I covered some catchers who were throwing at a rate that suggested additional defensive value relative to their peers in the new, more aggressive stolen base environment. Soon thereafter, MLB.com’s Mike Petriello revealed a new stat, Caught Stealing Above Average, to the public, and that Baseball Savant would roll out a leaderboard that would offer a more objective look at throwing out runners relative to the traditional caught stealing stat. You can check out the full leaderboard here.

There are multiple components taken into consideration for Statcast’s model that try to even the playing field when it comes to throwing out runners — variables like pitcher delivery speed, a runner’s lead and jump, and more. Evening all of those out provides more insight on how some catchers are more deserving of outs than others. Typically, I would highlight the catchers who have excelled at throwing out runners, but to emphasize the value of this statistic, I instead want to look at those who have been unlucky this year and last despite consistent strong throws, as well as other catchers where the trends are concerning. The first of this group is expected but notable nonetheless:

J.T. Realmuto (1 Catcher Caught Stealing Above Average in 2023, no. 7)

Base stealers have been running like wild against the Phillies this season, and it’s made for some confusing statistics for J.T. Realmuto. Out of his first 17 stolen base attempts of second base, he’s only caught five runners. If you remember this piece back in November, you know Realmuto has one of the strongest arms and fastest pop times in the game; if anybody should be throwing out most runners, it’d be him. But this year, he is only running a 29% caught-stealing rate, partially due to plays like the one above, where his pitcher was just slightly too slow to home. He has still been better than his expected rate of 22%, but his bar is much higher than any catcher in the league.

Realmuto’s 2022 track record is even more impressive than this year’s. If you combine all of last season’s attempts with this year, his CS% sits at 48%, with an estimated CS% of 23%. His laser-quick pop time makes up for his slower-to-the-plate pitchers. Realmuto is elite at throwing, framing, and blocking; nobody else can make that claim to this extent. We are watching one of the best defensive catchers of his generation.

Shea Langeliers (2 Catcher Caught Stealing Above Average in 2023, no. 5)

Not many things are going right for the A’s, but Shea Langeliers has impressed on both sides of the ball. With a 109 wRC+ fueled by a .244 ISO, he is off to a solid start with the bat, and both his swing and power are promising. On the defensive side of the ball, he has averaged a 1.95 pop time but has been a little unlucky with throwing out runners, with a CS% in his first 19 attempts to second base of 32% but an estimated mark of 24% — a similar discrepancy as Realmuto. On this throw, he was as perfect as you can be, but his pitcher was dragging his feet to get the ball home, and Tony Kemp lost his glove on the tag anyways.

While the pop time isn’t elite on average, Langeliers has proven that he is accurate enough to throw runners out if given the opportunity. It’s one of those situations where he isn’t necessarily a top tier thrower and therefore doesn’t have much room for error if his pitchers don’t cut him some slack or if his throw is just slightly off line. To me, Langeliers is the type of catcher who offers a glimpse into the future. With some automated ball-strike system inevitably coming to MLB, his profile is one that would perfectly transition to that new reality, as he is only average at blocking and framing but is slightly above average at throwing. Combine that with above-league-average offense, and you have yourself a perfect prototype for the potential new catching environment.

Martín Maldonado (2 Catcher Caught Stealing Above Average in 2023, no. 4)

If it weren’t for Realmuto, Martín Maldonado would find himself atop the new throwing leaderboard from 2022 through today, with seven CS Above Average. This year is no different, as he holds a 33% CS% despite a mere 14% estimated rate. The pitchers in Houston aren’t helping him, but he is nabbing runners regardless. Case in point: the play above, where Cristian Javier’s big leg kick and loopy arm took up too much time, undermining Maldonado’s pinpoint throw. But even when he isn’t catching runners, he is making it close.

Over the past few years, I’ve gone back and forth on the Astros’ decision to use Maldonado as their primary catcher. He is an incredible fielder but is consistently one of the worst hitters in the game. As his framing skills have undergone a slight regression compared to earlier in his career, it’s statistically unclear if it’s worth keeping him as the mainstay. But the additional data on Maldonado’s elite throwing and blocking in the last few months reminds us why he’s the lead catcher in Houston. Combine that with his glorified intangibles, and you can easily understand why he has cemented himself as the starter despite the lack of offense.

This new information can work in the other direction, too. Sometimes catchers are unlucky because their pitchers put them in a hole, and other times they can be even worse than expected. Unfortunately, there are some promising young catchers who fall into the latter category. Keibert Ruiz is 2-for-17 (12%) to start the year despite a 23% estimated CS%. His plus-2.00 pop time is the main reason for this. If his framing continues to trend in the wrong direction along with his arm, he will need to be an above-average hitter to live up to his prospect pedigree. Francisco Álvarez faces a similar dilemma, going 0-for-12 to start the year. His estimated CS% was only 12%, but his throws have not been competitive regardless.

For a while, catching was a semi-mystery; we knew who had rocket arms and who didn’t. Now, we have information about framing, blocking, and throwing that helps us figure out the true value of a gifted defensive catcher. It’s an exciting time to be a catching fanatic.

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 »