On Sunday afternoon, Juan Soto stepped up to the plate in the top of the first inning with a runner on first base. Soto, as he is wont to do, took the first pitch. He took the second pitch, too, as Kyle Freeland struggled with his command. Freeland relented and threw a slider over the heart of the plate, middle-away, hoping to sneak back into the count. Soto hit it 400 feet for a home run, putting the Nationals up 2-0.

When Soto batted to lead off the bottom of the fifth inning, Freeland was still pitching. Again, Soto got ahead 2-0. This time, Freeland was far more careful. He clipped the top of the zone with a fastball for a called strike one, then attempted to paint the corner low and away on his next pitch. He missed, and down 3-1, he threw another pitch low for ball four. Soto took his base, but the Nats couldn’t drive him home.

Why did Freeland challenge Soto in the first? Why did he change his approach in the fifth? I can’t read minds, but the decision seems fairly straightforward to me. In the first, Freeland didn’t have the luxury of pitching around Soto; a walk would put a runner in scoring position. In the fifth, the situation wasn’t quite so bad; a walk put a runner on base, which isn’t ideal, but there’s something primally scary about walking a runner to second.

That’s the theory, at least. It’s how I’ve understood baseball as long as I’ve watched it. Good hitter, base open, advantageous count? That hitter might as well send his bat back to the dugout, because he’ll rarely get a pitch to hit. Put that runner on first base, and the equation changes completely – now a walk hurts too much, and pitchers will take their chances in the strike zone.
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Last week, I wrote an article about the influence of batted ball spin. The takeaways were simple: For one, even though confounding variables like temperature and wind speed are hard to eliminate, it’s entirely plausible that batted ball spin alone can subtract crucial amounts of expected distance. Also, while hitters may display a penchant for certain types of spin, they seemed to have little control over it on a daily basis. Potential inaccuracies aside, these findings made sense; hitting a baseball is hard, and batted ball spin is just another piece of the puzzle.

After the article ran, I didn’t expect to revisit this topic anytime soon. But two things inspired me to start exploring again. First, a Twitter mutual was kind enough to provide me with Trackman data of college baseball games that include — you guessed it — batted ball spin axis, which opened up multiple avenues of research. Second, Dr. Alan Nathan, a physics professor at UIUC, summarized his own findings on batted ball spin in the comments. Armed with new data and knowledge, it was time to dive back in. Read the rest of this entry »

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 »

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 »

If I may, I think the uncertainty regarding this season’s offensive environment has made us a bit paranoid. Are hitters lagging behind pitchers due to an irregular spring training? Is the ball not traveling like it once did because it’s been replaced yet again, or is the mass introduction of humidors to blame? Or worse, has MLB introduced multiple balls into the game, some of which are being used in certain games to boost action or influence outcomes?

That last theory has been floating around my Twitter feed for a while now. I’m not going to discuss whether it’s true, but I brought it up because supporters of the multiple ball theory will often compare two batted balls with near-identical exit velocities and launch angles. One ends up traveling more than the other, despite all the indications that it should not. Aha! Something must be up.

In response, a lot of people have suggested batted ball spin as an explanation. Maybe one ball came off the bat with backspin and the other came off with topspin, which would drag the ball down as it traveled through the air. Unfortunately, since data on batted ball spin isn’t available on Baseball Savant, this might seem like a dead end. Don’t worry, though: I had some leftover Trackman data on 2021 NCAA Division I baseball games from a piece that Eric Longenhagen and I collaborated on during last year’s Draft Week, and they contain mostly complete readings on the spin of a batted ball. Could we use collegiate baseball to learn about the odds and ends of batted ball spin, and what it tells us about hitting? 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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The month of April is now complete and the verdict is in: The in-play home run rate for the 2022 season is down from recent seasons, as shown in Figure 1. Much has already been written about this feature by a variety of authors, including Jim Albert, Rob Arthur, Eno Sarris and Ken Rosenthal, and Bradford Doolittle, Alden Gonzalez, Jesse Rogers and David Schoenfield, and various reasons have been proposed for the relative dearth of home runs. Some argue that the baseball has been deadened, resulting in lower exit velocities and therefore fewer home runs. Others have suggested that the drag on the baseball has increased, perhaps due to higher seams. Yet others have argued that it is the effect of the universal humidor.

In this article, we will address the issue of reduced home run rates and hopefully add more light to the discussion. Specifically, we will examine home run rates during the month of April for the 2018-22 seasons, excepting the ’20 season for which there was no major league baseball in April. Here is our approach. Read the rest of this entry »

Before the 2020 season, I wrote a series of articles that looked at how much control batters and pitchers exerted over various outcomes: home runs, strikeouts and walks, fly balls, that kind of thing. I found the conclusions helpful, if mostly as expected: batters have more to say about home runs and line drives, but both sides have input on strikeouts, walks, grounders, and fly balls.

I decided to apply the same methodology – which I’ll detail below – to check on something that I thought I already knew the answer to: do pitchers exert any control over barrel rate, and how much do hitters do the same? Barrels are essentially batted balls hit extremely hard and at dangerous angles; I think they’re a great way of thinking about hard contact.

There’s already been plenty of research on the year-over-year stability of batter barrel rate. There’s been plenty on the fact that pitchers don’t do the same. Here’s a preview of my findings: I didn’t find anything that disputes that. I still think it’s useful confirmation, however, and I’m also pretty proud of the method. Thanks to Tom Tango, there’s even a rough rule of thumb to use if you want to estimate future barrel rates. Without further ado, let’s get to it. Read the rest of this entry »

At FanGraphs, our headline WAR number for pitchers is based on FIP. Because of that, and because people enjoy debating and arguing, there’s a yearly refrain that you’ve probably heard. “FanGraphs pitching WAR only considers (X)% of what a pitcher does, how can that be used for value?” No one would dispute that year-one FIP does a better job of estimating year-two ERA than ERA does – or at least, not many people would – but the discussion around whether FIP does a good job of assigning year-one value is alive and well.

One reason for this view is pretty obvious. FIP considers home runs, strikeouts, walks, and hit batters to estimate pitcher production on an ERA scale. Our WAR does some fancy stuff in the background – it treats infield fly balls, which virtually never fall for hits, as strikeouts, and it adjusts for park and league. In the end, though, it’s estimating pitcher value using just three (well, actually four — HBPs always draw the short straw) outcomes. There are a lot of other outcomes in baseball!

In 2021, roughly 39% of plate appearances ended in a homer, strikeout, walk, hit batter, or infield pop up. One thing you could think, in recognition of that fact, is that FIP-based WAR doesn’t consider enough of a pitcher’s production. You wouldn’t use 40% of a hitter’s plate appearances to calculate their WAR, so why do it for pitchers? But that doesn’t actually make sense, as David Appelman pointed out to me recently. Assuming “average results on balls in play” is actually going to be pretty close for every pitcher, by definition. Read the rest of this entry »

Last week, when I was waxing poetic about Jeff McNeil’s ability to wait for a good pitch and then drop it into left for a single, I made an offhand mention to the player with the best swing decisions of 2021: Mike Tauchman, who doesn’t even play in the major leagues anymore. Then I moved on.

That wasn’t an accident. It’s what we in “the business” (no one calls it this) like to think of as a preview. I got multiple texts (another pro writer tip: “multiple” sounds better than “two”) from friends this weekend asking where the whole list of hitters was. That list is right here!

As a quick refresher, the idea here is to take every swing decision a hitter makes and compress them into one number. Every hitter who saw at least 50 pitches in each of the four attack zones (heart, shadow, chase, waste) is on the list. I took each of those rates and gave them league-average production for those decisions. The result looks like this, stated in terms of run value per 100 of the relevant zone/decision combination (take a waste pitch, say, or swing at a pitch in the shadow zone):

From there, I assumed a league-average percentage of pitches in each zone. Combined with each hitter’s swing rates, that let me produce an overall run value assuming an average rate of pitches in each zone.

Here’s a quick guide on how to interpret these numbers. For each hitter, there are three numbers. The first two are just the same statistic said different ways. The first metric, “RV/100,” is how many runs above or below average each hitter on the list would be, per 100 pitches, if they got exactly average results on every zone/decision combination. The higher the number, the better positioned a hitter is to succeed, by taking tough pitches and swinging at good ones.
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