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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Back in my day — the mid-2000s — baseball was a simpler game. Batters socked dingers, with or without the use of performance-enhancing drugs. Teams batted slap hitters second, fast hitters first, and aging sluggers third. Catchers were valued by their bat and their throwing arm, none of this framing nonsense. And pitchers? Pitchers helped everybody out by throwing fastballs all the time, particularly on the first pitch. Seriously, take a look at this graph:

Year after year, pitchers have examined the mix of pitches they throw to lead off at-bats and decided to lop a few fastballs off the top. It’s not quite monotonically decreasing; there have been tiny upticks in a few years, and 2008 was lower, potentially due to classification issues in the first year of data. But this is as close as a trend can get to slapping you in the face.

The first pitch of an at-bat isn’t always the most important one — in a 3–2 count, one borderline pitch can be the difference between a baserunner and an out — but the difference between a 1–0 count and an 0–1 count is huge. That’s why pitchers have historically leaned on fastballs, which are easier to control; throwing a pitch outside the zone is a sure way to end up behind in the count.
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Pitchers are scary. I know this because I recently watched an outing by Matt Brash, who, despite his nonexistent command that day, used a scorching heater and two breaking balls to stymie a formidable Astros lineup. He walked six, but those free passes went along with five strikeouts and just two hits allowed. The Mariners won by a score of 7 to 2. So it goes.

Pitchers are scary, and they’re getting scarier, in large part because they’ve developed the ability to throw harder and harder. It probably doesn’t even bear repeating at this point, but because it’s the subject we’re on, let’s refresh ourselves. Back in 2008, the first year with PITCHf/x data, pitchers averaged 91.8 mph on their four-seam fastballs. Last season, they averaged 93.8. That the league as a whole has gained two miles per hour is indicative of substantial change.

It’s 2022 now. It’s early, but so far, pitchers have been averaging 93.9 mph on their fastballs, for an uptick of 0.1 mph. Such a small difference might not seem like much, or something we should focus on right now. But this is merely a cursory glance. We haven’t even separated the starters from the relievers, and it’s the latter group that most people associate with triple-digit wizardry. Are bullpens hiding the fact that rotations aren’t quite stretched out yet due to an abbreviated spring? Let’s find out:

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The Colorado Rockies are projected to be gobsmackingly bad in 2022. Look no further than the summary of this season’s positional power rankings: They have three positions that rank 30th and six below 20th, which works out to a cumulative last-place finish. Most of it traces back to a lack of certifiable talent on Colorado’s roster. But some of it, inevitably, is a function of Coors Field. Today, I will mainly focus on the fact that at home, the Rockies allow lots and lots of runs. The common thinking is that this is because Coors is an environment conducive to home runs. While true, there’s another factor that arguably matters more. Check out this graph:

When a fly ball or line drive is hit at Coors, the resulting .459 BABIP has led all of major league baseball by a laughably wide margin for the past few years. The gap between the Rockies in first place and the second-place Red Sox (.421) is equal to that between second place and the 15th-place Orioles (.393). If you’re wondering why, the outfield at Coors is absolutely enormous, so much so that it’s hard to believe just three men patrol it. The thin air helps the ball travel, but crucially, there’s also a lot of space for it to land. It’s a two-part mechanism that captures why offense can get out of control in the Rockies’ home park. Read the rest of this entry »

A few days ago, there was a question raised on Twitter that sparked a good amount of discussion:

Which is most important for a hitter?

— Jason Ochart (@JasonOchart) March 4, 2022

It’s a straightforward premise with a not-so-straightforward answer. Bat-to-ball skills came out on top, but there’s a convincing case for each option. Bat speed matters most due to a strong correlation between exit velocity and overall production. Bat-to-ball skills matter most because without a sufficient rate of contact, it’s difficult to translate raw power into in-game power. Swing decisions matter most because good ones lead to favorable counts, which in turn lead to favorable results.

This author leans toward bat speed. It’s also the option that received the fewest amount of votes. This isn’t to say one side is in the wrong — the truth is that all three skills are integral to offensive production — but I’m here to defend what seemed like an unpopular choice. As far as I can comprehend, the remaining two are predicated on, or at least amplified by, the existence of bat speed.

Onto the case! It’s a little unorthodox, though, because I didn’t start off to prove a point. Before seeing that poll, I’d been working on a metric to evaluate a hitter’s swing decisions. It didn’t turn out as hoped, so I’ve put it on hold for now, but at least all that effort didn’t go to waste, because how hitters perform according to that metric indirectly reveals the importance of bat speed. Specifically, I’m going to narrow in on 0–0 counts, for three reasons: They’re the most ubiquitous; this article can’t go on forever; and the numbers get a bit wonky in other counts. See, there’s a reason why it didn’t work out. Read the rest of this entry »