Recently, I was listening to one of my favorite non-baseball podcasts when baseball unexpectedly cropped up. Well, the theory of skin in the game cropped up. The idea, espoused by many people but notably by Nassim Taleb, is that actors perform better when they get rewarded for a good outcome and punished for a bad outcome. Want a better doctor? Fine them if they misdiagnose a patient, but give them a bonus for prescribing the correct treatment. Better money manager? Force them to invest all their own money alongside their client. You get the idea.

Anyway, one example of skin not being in the game is a sports team playing out the string. For most teams at most times, sports is a very skin-in-the-game-intensive field. If you hit well, you get paid more. If you don’t, you might get sent to the minors. If your team wins, they make the playoffs. If the team doesn’t win, no postseason. The incentives are straightforward.

At the end of a long season, however, it might not feel that way. If you’re 50-100 in late September, the rewards of a good game aren’t that high, and the cost of a bad game is quite low. If you’re 15 games out in the race, being 16 games out won’t suddenly bring out the detractors. You can think of these teams as having no skin in the game; the result of one game won’t change anything for them. Read the rest of this entry »

FanGraphs readers are a smart bunch. Though the comments can sometimes unravel into a series of shouting matches, the usual atmosphere is encouraging and collegial. For example, here’s a thought-provoking question I received a few weeks ago and my reply to it:

This is from an article I wrote about Framber Valdez and how he was on pace to shatter his own historic groundball-to-fly ball ratio. A five-man infield in any other circumstance would be out of the question, but consider just how many grounders Valdez generates. Among starters with a minimum of 200 innings pitched since 2020, he’s first in groundball rate (66.7%) by a wide, wide margin. With so few balls heading towards the outfield, does it make sense to reinforce the infield instead? It’s an intriguing inquiry, one that I promised would receive an answer. So here goes! Read the rest of this entry »

If you’re a major league pitcher right now, there’s a good chance life is pretty smooth. You’ve realized that you can throw more and more sliders without repercussions; it might even be an ideal strategy. You’ve also learned that by using a two-seam grip, you can upgrade a regular slider into a “sweeper,” which is shockingly effective for a pitch that’s so easy to learn. You’ve probably gained a much better understanding of how and why certain pitches do or don’t move. Knowledge is power, especially in baseball, and the modern pitcher is possibly the most educated athlete around.

Meanwhile, there are formerly innovative approaches that you don’t think twice about nowadays – they’ve become the norm. A great example is the high fastball. Back in the days of yore, a perfect fastball meant one located at the knees, down and away. But as pitch data became widely available, teams started to realize that throwing the fastball up would maximize swings and misses and minimize damage on contact. Regular high cheese also served to counteract the so-called fly ball revolution; an uppercut swing made golfing pitches at the bottom of the zone easier but left a hole at the top. Read the rest of this entry »

At its core, hitting is about hitting fastballs. I’m not sure that’s a good thing – pitchers don’t throw as many fastballs as they used to, because they know that hitters are hunting fastballs. Look at the aggregate data, though, and it’s clear. So far this year, batters are 93 runs above average against fastballs, and naturally enough, 93 runs below average against all other pitches. Last year, they were 344 runs above average against fastballs. It’s a consistent pattern throughout baseball history. Ask a hitter, and they’ll probably tell you the same thing. You make your paycheck on fastballs, and you hope not to spend it all on everything else.

That’s not to say that it applies to all hitters equally. Mike Trout is a good secondary pitch hitter – he’s a great hitter overall. Rafael Devers might be a better secondary pitch hitter than he is a fastball hitter. The archetype exists, because, well, good hitters are good.

The opposite is true as well. Max Muncy has done almost all of his damage against fastballs throughout his career. So has Joey Votto, surprisingly enough – from 2018 to now, he’s been five runs below average against sliders, curveballs, changeups, and splitters combined. There’s no one way to be a great hitter – you can tattoo fastballs and live with the damage from everything else, hunt everything else and survive against fastballs, or find some happy medium.

I thought it would be fun to figure out who most embodies this “baseball is about hitting fastballs” lifestyle. In other words, I’m looking for a hitter who is good overall, but incredibly poor at handling secondary pitches. It won’t do to find someone who’s bad at hitting sliders because they’re just bad at hitting; Billy Hamilton is the worst slider hitter in baseball over the past five years (by run value per pitch seen), but well, he wasn’t in the majors for his hitting. Read the rest of this entry »

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.
Read the rest of this entry »

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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