As you’re probably aware, Apple TV+ has stepped onto the baseball broadcasting scene this year, airing two games every Friday. They’re stylistically different from your average baseball broadcast, even at a glance. The colors look different, more muted to my eyes than the average broadcast. The score bugs are sleek, the fonts understated. The announcers are mostly new faces. And most interestingly, to me at least, the broadcast displays probabilities on nearly every pitch.

As a big old math nerd, I love probabilities. They appeal to something that feels almost elemental. Every time I watch a baseball game, I wonder how likely the next hitter up is to get a hit – or to reach base, or strike out, or drive in a run. It’s not so much that I want to know the future – probabilities can’t tell you that – but I would like to know whether the outcome I’m hoping for is an uphill battle or a near-certainty, and how the ongoing struggle of pitcher against hitter changes that.

The Apple TV+ broadcasts gets those probability numbers from nVenue, a tech startup that got its start in an NBC tech accelerator. According to an interview with CEO Kelly Pracht in SportTechie, the machine learning algorithm at the heart of nVenue’s product considers 120 inputs from the field of play in making each prediction.

Machine learning, if you weren’t aware, is a fancy way of saying “regressions.” It’s more than that, of course, but at its core, machine learning takes sample data and “learns” how to make predictions from that data. Those predictions can then be applied to new, out-of-sample events. Variations in initial conditions produce different predictions, which is why you can think of it as an advanced form of regression analysis; at its most basic, changes in some set of independent variables are used to predict a response variable (or variables). Read the rest of this entry »

Whether or not you’ve seen it, you likely know the premise of Freaky Friday. A mother and her daughter switch bodies in a great cosmic mixup, and hijinks ensue. Hello! Welcome to FanGraphs. I’m Ben Clemens, and today we’ll be covering classic teen cinema of the early 2000s (and mid-1970s), as personified by last night’s Giants-Mets game.

Tuesday night could have been just another day at the (beautiful, well-appointed) office for the Mets and Giants. After a comfortable win by New York in Monday’s series opener, the Giants returned the favor early in last night’s game. Chris Bassitt, the steadiest starter in a rotation buffeted by injuries, had his worst start of the year, surrendering eight earned runs in only 4.1 innings thanks to three homers, two by Joc Pederson. Logan Webb, meanwhile, cruised through five innings (six strikeouts, one walk, two runs), turning what was billed as a pitching duel into an 8-2 rout.

Teams don’t come back from six-run deficits. When Pederson launched his second homer, a two-run shot that pushed the score to 8-2, the Giants’ win expectancy climbed to 98.2%. Tune into 50 games, and you might see the trailing team pull one out. The Mets behaved accordingly; they brought in Stephen Nogosek, the last reliever in their bullpen, to eat some innings.

That’s the way the game could have ended – but let’s get back to Freaky Friday. In 2021, the Giants won these games, whichever side of the 8-2 score they were on. They were both excellent and a team of destiny, and you have to win plenty of tough ones to end the regular season with 107 wins.
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“When you’re pitched away, take the ball to the opposite field.” It’s a training mantra that seemingly exists everywhere. I heard it in Little League. I hear it on major league broadcasts to this day. The data show that hitters do it, and it’s just a natural swing. I can think of few hitting sayings I believe more than this one.

Of course, just because you can hit the ball the other way doesn’t mean you have to. Over the last two years, the list of righty hitters who have pulled the ball most when they swing at away pitches (from right-handed pitchers, just to standardize the sample) probably matches your intuition:

You basically understand the kinds of hitters on here. The guys ranked sixth and seventh are similar types: Salvador Perez and Mike Zunino. It’s big boppers who try to lift and pull the ball no matter where they’re pitched, as well as guys like Marcus Semien who sell out to pull in an attempt to juice their power. If you do the most damage on the pull side and accrue most of your offensive value through power, it’s a natural approach. You think anyone’s coming to the ballpark to see Patrick Wisdom slap a well-placed cutter the other way? They want dingers!

The list of the hitters who pull the ball least often when pitched away is mostly who you’d expect, and also not who you’d expect at all. Feast your eyes on the top five:

The top four are contact-oriented hitters with elevated groundball rates… and the fifth might be the most powerful baseball player in history. Read the rest of this entry »

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The Yankees had the third-best bullpen in baseball last year, but you’d be forgiven for thinking that had changed this year. Last season’s two best relievers, Jonathan Loáisiga and Chad Green, have combined for 0 WAR and an ERA above 5.00, and Green will miss the rest of the season due to injury. Their highest-paid reliever, Aroldis Chapman, has lost more velocity and is recording strikeouts at a below-average clip. Their big speculative offseason addition, Miguel Castro, is below replacement level.

Naturally, they have the second-best bullpen in baseball in 2022. Michael King, who I recently wrote about, is the headliner so far this year, but he’s hardly alone. Clarke Schmidt, who profiles as a starter long-term, has looked good. Wandy Peralta is a competent lefty specialist. And that brings us to King’s running mate, the other best reliever on the Yankees: Clay Holmes.

Holmes is hardly new to the majors. He toiled in obscurity with the Pirates for years, walking too many to take advantage of his grounder-inducing sinker. Then the Yankees got their hands on him, and he turned that sinker into an entire identity, filling the zone and letting the chips fall where they may. Read the rest of this entry »

This Wednesday, Corbin Burnes had a forgettable start. In six innings, he allowed four runs and struck out five while walking none. The scoring came courtesy of two home runs, a three-run shot by Austin Riley and a solo homer by Marcell Ozuna. Burnes lasted six innings, and while the Brewers ended up winning the game 7-6 in extras, it wasn’t exactly the kind of start you expect from the defending NL Cy Young winner.

Last year, Burnes was downright electric en route to winning the award. He led the NL in ERA at 2.43, and his ERA was significantly higher than his 1.63 FIP. He struck out 35.6% of the batters he faced while walking only 5.2%. He did it in only 28 starts and 167 innings, which raised questions about the trade-off between transcendent pitching and bulk innings.

If you only looked at his first and most recent starts of 2022, you might think the same thing was happening again. You’ve already heard about the most recent one; in his first start, he went five innings against the Cubs, allowed three earned runs, and struck out only four while walking three. Sure, the runs were uncharacteristic, but five and six inning starts? We’ve seen that before. Read the rest of this entry »

Last Saturday, Jalen Beeks had a thoroughly unimportant day at the ballpark. With the Rays trailing Toronto 5-1, he came in to pitch the top of the ninth inning. The stakes? Helping the team hit the showers 10 or 15 minutes earlier, I’d say – he wasn’t going to catapult Tampa to a win with a good performance, what with a four-run deficit and only three outs remaining, but everyone on the team would surely appreciate an efficient outing.

He did it! He got three straight groundball outs. After that, while he presumably changed into his street clothes in the clubhouse, the Rays failed to score in the bottom half of the inning, and the game ended. Thank you for coming to this episode of “FanGraphs Narrates Low-leverage Relief Outings.”

But wait! After this humdrum appearance, an anonymous tipster lit the FanGraphs signal (it’s like the Bat Signal, only with the FanGraphs logo instead). There was more to this half inning than first met the eye. I was on the case. Read the rest of this entry »

Yesterday, a reader in my chat asked me a question I had no idea how to answer: Are teams increasingly pulling pitchers from games after 4 2/3 innings, even with the lead, in an attempt to cut down on wins and arbitration payouts? Here’s the question in its entirety:

My snap judgment was “probably not.” After thinking about it for a while longer, my answer is still no – but now I have some neat graphs and charts that will hopefully make the point clear. Without further ado, let’s dive into the shape of league-wide starting pitching trends since 1974, the first year in our database of game logs.

In 1974, the concept of a five-inning start existed, but it was almost an insult. More than a quarter of starts went nine or more innings. That’s hard to do, particularly when that’s an impossible feat for a visiting team that trails after the top of the ninth inning. If that’s roughly a quarter of games (it’s not every game the visiting team loses, but road teams lose more than half of the games they play), that means that roughly a third of eligible starts went at least a full nine. That’s downright wild. Here’s a graph of that wildness:

There were a few short starts, even back in the 1970s – 21% of starts went fewer than five innings. More importantly, a pattern we’ll see repeated again and again is immediately evident. Managers like leaving their pitchers in for a whole number of innings. It’s a natural endpoint to the day, mid-inning pitching changes can be tricky, it’s a way of boosting your starter’s confidence – there are plenty of reasons for this to be the case, and I’m not sure which is most true, but that’s just a fact of baseball. Managers like to pull their starters between innings rather than partway through. Read the rest of this entry »

A quick word of warning: this one is pretty abstract. If you like baseball math, it’s definitely got that. If you like analysis of the 2022 major league season, it absolutely does not have that. I think it’s pretty fun, but if that’s not your cup of tea, this one might not be for you. Anyway: on to the nonsense!

I’m the kind of maniac who likes to play baseball video games when I’m not writing about baseball. Right now, that’s Out Of The Park 23, specifically the Perfect Team mode. It’s a baseball simulation where you collect cards representing current and historical players, build teams, and then play simulated games against other players’ teams.

The headline mode of the game lets you collect whoever you want and battle against your opponents’ best shot – peak Mickey Mantle against peak Tex Hughson, say. That’s fun in its own way (for what it’s worth, Mantle strikes out more than you’d like when facing top-tier competition), but I’m more interested in another mode the game offers: tournaments where you match a limited pool of your players against a limited pool of opponents.
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