I didn’t want to write this article. One of my favorite things to do, back when I was a full-time Cardinals fan and part-time writer, was wait for the first few weeks of the season and then start ogling velocity changes. There’s almost nothing that made me feel so unabashedly happy as seeing an extra tick or two out of some arm I’d written off the previous year. Why spoil that magic by looking into whether it actually matters?

Nothing fun can come of using data to look at incuriously held beliefs, but that’s never stopped me before, so I decided to examine pitchers who experienced velocity gains from one year to the next. Do their fastballs grade out better? Do they strike out more batters? Walk more? Do they hold the gains from one year to the next? I had no clue, but I decided to find out.

First things first: 2020 goes right out the window. The season started in late July, and no one had anything approaching their normal offseason routine. Temperatures were weird, workloads were changed on the fly, and some teams were affected by COVID-related cancelations; trying to tease something out from that noise is pointless and unnecessary. I’ll just use 2015 through 2019 instead.

Why 2015? That’s when Statcast first arrived, and with it a new tracking system. I could, I suppose, use data since 2008, but I wanted to minimize the chances of false readings stemming from the change in systems. 2020 also featured a change — to camera-based readings instead of radar — but we’re already throwing it out anyway, so no big deal there.

In each year, I looked at the population of starters who threw at least 500 four-seam fastballs. I then found the year-to-year changes for each pitcher-season combination. Justin Verlander, as an example, averaged 93.4 mph in 2015, 94.1 mph in 2016, 95.3 mph in 2017, 95 mph in 2018, and 94.6 mph in 2019. That means his ‘15-’16 change was 0.7 mph, his ‘16-’17 change was 1.2 mph, his ‘17-’18 change was -0.3 mph, and his ‘18-’19 change was -0.4 mph. This gave us a database of 236 pitcher-seasons from 2016 to 2018 — I’m leaving out changes between 2018 and 2019 because I want to know what happens the year after a pitcher gains velocity. Read the rest of this entry »

Before we get going, a heads up: this article is about a mathematical model that explains tanking. I’ll give you a preview. The way baseball is currently set up, it’s no surprise teams tank. If you want to fix that, the game’s competitive structure must change. I suggest a few ways to accomplish that change at the end of the article, but be warned: the bulk of this is a no-nonsense dissection of why teams keep tanking even as the returns go down. Personally, I think the game should make the changes I suggest, because the boom and bust cycle of team contention makes for a fraught fan experience. The way the game is set up now, however, it’s no surprise that teams do it.

The logic behind tanking is straightforward and solid. Being in the middle is the worst; there are no prizes for winning 85 games, flags fly forever, and so on and so forth. You’ve surely heard it enough times that you don’t need a repeat, but just for completeness’s sake, we’ll do it one more time.

By trading present concerns for future value, you make your team better in the future. As a byproduct of trading present concerns away, your major league roster gets bad — bad enough, hopefully, that you’ll move to the top of the draft. Additionally, with no pesky need to be competitive, you can use your major league roster to give borderline players extended tryouts. Hit on a few of them, and that’s even more tailwind for the future.

In essence, tanking is making a bet that taking a step back now will let you take two steps forward sometime in the future. Even if that isn’t the case, being quite bad for a while and then quite good for a while sure sounds better than being mediocre the whole time. Tanking works on both axes, which explains its continued appeal. Do you think your team will win something like 77 wins? Blow it up! 75 wins? Blow it up! 70 wins? You guessed it. Read the rest of this entry »

Baseball is great, but it can be better. While earlier versions of this piece had an overwrought and overly long intro on the delicate balance between the intimacy of the pitcher-batter matchup and the frenzied multi-actor action across beautiful acres of wondrous expanse resulting from a ball put in play, let’s just get to my suggestions to improve the play on the field.

Shrink the Strike Zone

One of the unfortunate side effects of the balls-in-play discussion is that strikeouts and walks tend to get lumped together. In reality, walks are a pretty static feature through baseball history, while strikeouts have fluctuated. Here’s a graph showing walk and strikeout rates over the last 50 years.

Over the last 50 years, the average walk rate has been 8.6%, which is the same as it has been the last five years, and over the last 10 seasons, it is 8.2. Whatever hitters and pitchers are doing in recent history, it hasn’t caused more walks. Strikeouts, though, have soared, and on average, there are about 26 fewer free passes per year over the last decade as opposed to the previous 40 years, compared to an additional 352 whiffs per team per year over the same time frame. While you can argue that the increase is due to changes in hitting philosophy, the average fastball has gone from 89 mph in 2002 to 93 mph last season, while pitchers throw more and more offspeed pitches and fewer pitches in the strike zone. It’s not batter philosophy causing the rise in strikeouts; it’s the pitching just getting better and also being better aided by an increase in the size of the strike zone of about 10%. Read the rest of this entry »

Last year, as part of the negotiations over a new Professional Baseball Agreement (PBA) with Minor League Baseball, Major League Baseball introduced a proposal that would dramatically reimagine the minor leagues. The proposal included plans to shift the timing of the amateur draft and realign some parent-club affiliations, league geographies, and club levels. Most importantly, it proposed stripping more than 40 clubs of their affiliated status, though it also suggested that some of the newly unaffiliated teams would assume other formats, either as so-called professional partner leagues, or as amateur summer wood bat leagues. The plan got us thinking about how access to in-person baseball across the United States would change. We were interested in how many people would lose their ability to watch affiliated baseball in person, or would see that access shift from the relatively affordable confines of the minor leagues to more expensive major league parks.

Those studies relied on a New York Times list of teams reportedly slated for contraction, as well as Baseball America’s excellent reporting. Thirteen months, a pandemic, and one extremely contentious negotiation later, MLB has informed minor league teams of their proposed fates, with 120 franchises “invited” to be part of the new, MLB-developed minor league system. Many are still reviewing the terms of their “invitations”; several find themselves occupying a new rung on the minor league ladder, or with a different parent club than before.

Meanwhile, 25 clubs find themselves ticketed either for summer wood bat leagues, including the newly formed MLB Draft League, or for pro partner leagues for undrafted players and released minor leaguers. Eighteen teams face futures that are, as of this writing, uncertain, though as Baseball America’s JJ Cooper notes, “Major League Baseball has indicated that it will pay entry fees for these teams that were left out of affiliated baseball to join new leagues. MLB will pay their way in, but as a condition those teams are expected to waive a right to sue.” The complete list of the 43 franchises slated to lose their affiliated status can be found below. Of the 43, 11 are full-season clubs:

Read the rest of this entry »

For the better part of this decade, we’ve repeatedly published an article you can more or less predict. Nearly every year, a version of the same idea gets published. “You’re never going to believe it,” the article starts, “but fastballs got faster again this year.” There are usually some GIFs, maybe a winking joke about how we write this article every year and it keeps being true, and bam, 1,500 words out the door. Oh yeah! There’s also a kicker: “Fastballs keep getting thrown less frequently, too.”

Normally, I’d be writing that article again this year. There’s just one problem: four-seam fastballs didn’t get faster this year; in fact, they’ve been plateauing for a few years. This year’s four-seamers checked in at an average velocity of 93.9 mph. Adjusting for time of year (I used only data from August onward in each season so that we didn’t have any weather effects unique to 2020), here are the last five years of four-seam velocity:

The 2019 season was the fastest on record, and 2020 fell short of that mark. In fact, the last five years look overall unchanged. Look instead at sinkers, though, and you’ll see some velocity improvement:

Which one should we believe? Four-seamers are more common than sinkers, so the blended average looks like this:

Okay, so fastballs didn’t get any faster this year. Sinkers did, and that’s interesting for sure, but at the highest level, it feels like the inexorable march towards higher velocity might have stalled for the moment.

Read the rest of this entry »

You’ve heard of home field advantage. It’s simply a part of sports, like gravity or Tom Brady being competent and obnoxious. Here’s a dirty little secret, though: A decent chunk of home field advantage is actually first-inning advantage. Here, take a look at how home and away batters performed in the first inning and thereafter from 2010 to ’19:

The first inning has the biggest gap, with only the fifth coming even close. It’s a consistent effect year-to-year, and it’s a big deal: A 22-point edge in wOBA works out to three-quarters of a run per game, which would work out to roughly a .570 winning percentage, significantly higher than the actual edge. If you could bottle that edge and apply it to every inning, baseball would look very different.

This isn’t some novel effect I’ve just discovered. It’s well-established, though I’ve never seen a completely satisfactory explanation for it. Could it be that the home team’s defensive turn in the top of the first warms them up for their turn at bat? Maybe! One counterpoint here: Home DHs have a 20-point wOBA advantage on away DHs in the first inning, then only a six-point advantage thereafter. Maybe it’s not that, then.

A theory that makes more sense to me is that home pitchers have a unique advantage in the first inning. In that inning, and that inning alone, they can exactly predict when they’ll be needed on the mound. Have a perfect warmup routine? You can finish it just before first pitch, then transition directly to the game. Visiting pitchers are at the mercy of the game. Start too late, and you won’t be ready in time for the bottom of the first. Start too early, and an extended turn at the plate might leave you cold. Read the rest of this entry »

Here’s the honest truth about baseball analysis: Most of the ideas I look into don’t work. That’s mostly hidden under the surface, because it’s not very interesting to read an article about absence of evidence. Hey, did you know that batters who hit very long home runs see no meaningful effect on the rest of their performance that day? I did, because I looked into that at one point, but imagine an article about that and you can kind of see the problem. Read a whole thing looking for a conclusion and find none, and you might be more than a little irritated.

Now that I’ve told you how bad of an idea it is to write about failed ideas, I’d like to introduce you to an article series about ideas that didn’t pan out. I know, I know: I was bemoaning the difficulty of writing such an article just sentences ago. Some failures, however, are more interesting than others, and I’d like to think that I know how to tell the difference. In this intermittent and haphazardly scheduled series, I’ll write about busted ideas that taught me something interesting in their failure, or that simply examine parts of the game that might otherwise escape notice.

In September of this year, I came up with an idea that spent the next month worming its way into my brain. We think of pitch movement as relative to zero, but that’s obviously not true. Sinkers rise more than a spin-less pitch thrown on the same trajectory would; they’re “risers”, in fact. Don’t tell a player that, though, because they’re not comparing these pitches to some meaningless theoretical pitch that no one throws. They’re comparing them to other fastballs, four-seamers to be specific, and if your brain is used to seeing four-seamers, sinkers do indeed sink. Read the rest of this entry »

Last week, I dug up an old concept: expected home run rate. The idea is deceptively simple: assign some probability of a home run to each ball a batter hits in the air, then add them up. It tells you some obvious things — Fernando Tatis Jr. hits a lot of baseballs very hard — and some less obvious things — before getting injured, Aaron Judge had lost some pop.

One question that many readers raised — reasonably so! — is whether this expected home run rate actually means anything. The list of over-performing hitters was full of sluggers. How good is this statistic if it tells you that good home run hitters are, in fact, not as good as their home runs? Sounds like a bunch of nonsense to me.

In search of truth — and, let’s be honest, article topics — I decided to do a little digging. Specifically, I wanted to test three things. First, how stable is expected home run rate? In other words, if a player has a high expected home run rate in a given sample, should we expect them to keep doing it? If the statistic isn’t stable, what’s the point?

Second, how does it do at predicting future home runs? In other words, does an expected home run rate in, say, July predict what will happen the rest of the year? It’s also useful here to see if expected home run rate (from here on in, I’ll be calling this xHR% for brevity) outperforms actual home run rate as a predictor. If xHR% doesn’t do a better job of explaining future home runs than actual home runs, what use is it? Read the rest of this entry »

Last year, I came up with a simple idea: estimate home runs based on exit velocity. That sounds pretty straightforward, and it mostly is. For example, here are your odds of hitting a home run at various exit velocities when you put the ball in the air in 2020:

Of course, some caveats apply. I’m only looking at batted balls between 15 and 45 degrees, and the sample size is still small. But for the most part, and excluding the vagaries of that small sample, the conclusion makes sense. Hit the ball harder, and you’ll find more home runs.

Of course, real life is notoriously fickle. Sometimes you mash the ball and it’s a degree too low, or you hit it to the wrong part of the ballpark, or a gust of wind takes it. Sometimes you play in Yankee Stadium and get a cheapie, or smoke a line drive that leaves a dent in the Green Monster. Sometimes you make perfect contact, and it’s at 15 degrees instead of 25 so it’s a smashed single to right instead of a bat flip highlight.

Wait — hit it at the wrong angle? That seems like something in a batter’s control. It partially is, but I’ve chosen to exclude it for two reasons. First, I’ll point again to this excellent Alex Chamberlain article. You should really read it, but the conclusion is basically this: batters control exit velocity and pitchers control launch angle. That’s not exclusively true, and there are obviously fly ball and groundball hitters, but if you start giving batters credit for the exact angle of their batted balls instead of just generally saying “in the air” or “not,” you might be going too far.

Second, this way is simpler! Simplicity has value. Overspecify a model, and you can get very precise results that are also hard to interpret, or that depend heavily on small fluctuations in initial conditions. That’s not to say that such a model is a bad idea — merely that it’s not strictly upside to add more and more gadgets and whizbangs to it. You also risk losing the signal you’re looking for, which in this case is the ability to absolutely hit the snot out of the ball, sending it skyward at stupid speeds. Read the rest of this entry »

Pinch hitting is hard. Baseball is a rhythm game, and pinch hitters are denied any semblance of routine. They’re on the bench, swinging a bat back and forth to get the blood pumping in their arms, and then just like that, they’re in the game. They might have been daydreaming about what they plan on ordering from room service, and here’s Jacob deGrom throwing 92 mph sliders. Good luck!

That’s the classical conception of a pinch hitter, and it explains why Tom Tango, Mitchel Lichtman, and Andrew Dolphin found a significant pinch hitting penalty in The Book. They found a 24-point wOBA penalty for pinch hitters, which is a large cost. That’s roughly equivalent to the platoon advantage a lefty gets when facing a right-handed pitcher.

That’s a pretty striking difference. When your team gets a lefty batter up against a righty pitcher in a big spot, it feels great. Imagine that pitcher being replaced by a left-hander. Feels pretty awful, right? That’s the same swing in effectiveness you get when a batter pinch hits rather than batting regularly.

You don’t always hear about this effect on broadcasts, because there are other decisions that go into pinch hitting. You’re getting a diminished version of whichever hitter you select, but other advantages can still tip the scales in a batter’s favor. Read the rest of this entry »