When Statheads Age
This is Joe Sheehan’s first piece as part of his April residency at FanGraphs. A founding member of Baseball Prospectus, Joe currently publishes an eponymous Baseball Newsletter. You can find him on Twitter, as well.
Like a lot of fans, I watched Sunday’s and Monday’s games fascinated by the number of pitchers who seemed to be throwing harder than they did last season. So it was a relief to see Dave Cameron’s note here Tuesday about why readings were higher. It will take some mental gymnastics to compare velo figures from 2017 to previous years, but I’m sure it will be second nature aft…
… wait, what?
This is what it’s like being a baseball fan in 2017. The issues you face are ones of reconciling changes in how the velocity of every single pitch thrown in MLB is tracked. It’s not about getting that data, but rather, about sussing out the difference between measurement points of the pitch on the way from the pitcher’s hand to home plate.
We had a different set of problems when we were putting together the first Baseball Prospectus annual, back in the winter of 1995-96. The challenges we faced weren’t discerning which measure of velocity to use, but rather, when we would get access to minor-league statistics, and how soon lefty/righty splits would be in our hands, and would anyone at all talk to us about prospects we only knew by their stat lines.
I fell into Prospectus by chance, before it even had a name. Clay Davenport had been publishing his Translations — a system that improved upon minor-league equivalencies — online for a few years, and he and Gary Huckabay had an idea for a book, an annual that would include the Translations and Gary’s Vladimir projections, and commentary on players. and team essays. I knew both from the Usenet newsgroup rec.sport.baseball (r.s.b), which was the primordial soup of internet baseball writers. I can still remember the conversation, in November of 1995, in which Gary invited me to work with them. I was a journalism major and my first real job involved layout, so I was able to edit and assemble the early annuals. We wanted to write about the importance of OBP to offense, and the necessity of limiting workloads to the health of young pitchers, and the silliness of using fielding percentage to determine who was good at playing defense. We wanted to write the book we wanted to read.
It’s been almost 22 years since baseball went from a game I love to a job I love. It’s been almost 30 since a kid in my local Strat-O-Matic league (ITBL!) showed me the 1988 edition of the Bill James Baseball Abstract. Strat and Bill James were how I learned there was more to baseball stats than what showed up on the baseball cards or the television. R.s.b built upon that knowledge, teaching me about replacement level, and the importance of age in evaluating prospects, and why throwing 130 pitches in a game is bad for 21-year-olds.
This all sounds quaint in 2017, but it was on these arguments and others like them — some we later learned were wrong — that Prospectus was built. We worked with the data we had, and we were incredibly happy to have it. The Stats, Inc. books were something of a bible back then, especially the minor-league versions. There was no Baseball-Reference [pause to genuflect], no Play Index, no sortable stats on FanGraphs. I can remember using Total Baseball and the Baseball Encyclopedia in those early days. The idea that we might some day know the velocity of every pitch thrown was visible from that moment; that we would know the spin, and break, and from where it was released, would have seemed far fetched.
For someone who remembers assembling those early books, who can see them from where he’s writing this, where we are now is astounding. Sunday’s Opening Day coverage on ESPN featured references to defensive WAR, “barrels,” and win probability. At Great American Ball Park in Cincinnati — the city where major-league baseball began — the scoreboard listed WAR, OPS+, and ISO, with text explanations throughout the game. We spent the better part of a decade at BP on a war footing, trying to get fans, media, and industry professionals to look at these ideas. There’s no war any longer.
In fact, much of the innovation now is being done by the league itself. With first PITCHF/x, and now Statcast, it is MLB, through its MLB Advanced Media arm, that has accelerated the process by which we’re learning about the game. Teams, all 30 of them, use data to make decisions as banal as where to play the second baseman in the fourth inning, and as complex as how to manage the health of their pitchers. As the industry has embraced these tools and, more importantly, the mindset that data drives decisions, the media that covers the game has come along.
The place I helped build, the site you’re on now… this is the mainstream media. That’s not a pejorative. I write for Sports Illustrated. Christina Kahrl is an editor at ESPN.com. Rob Neyer was on FS1’s baseball coverage. Jay Jaffe goes on MLB Network, as do Dave Cameron and Jeff Sullivan. We’re not that long removed from MLB refusing to let me into the Rule 5 draft in Dallas. Today, dozens of internet-only writers are credentialed. There’s an entirely new category of baseball writing that blends analysis with reportage, best exemplified by the work of Eno Sarris, but available too in places like the Boston Globe (Alex Speier) and the New York Times (James Wagner). Coverage of baseball has never been stronger, never been smarter, never been seen by more people.
Change has come so rapidly that I find myself at loose ends a bit. I’m a stathead, but I was never a sabermetrician. My greatest work in that vein is probably a research piece on Jack Morris’s career. Words are my skill, not numbers, and with each passing year, the numbers become a little more complex, a bit harder to manage, a bit more daunting. The Play Index remains my primary tool, but the real work is being done far afield from seasonal stat lines and game logs and OPS+. The real work is being done a terabyte at a time with radar that collects every movement of every solid object — bats, balls, people — on a baseball field.
I can actually understand the confusion and frustration of the ballwriters of the 1990s, who had been able to talk about batting average and RBIs and wins for years, and who now found themselves being called dinosaurs for doing their job. I look at BABIP or HR/FB, numbers that were advanced as recently as five or six years ago, and I feel like I’m not providing enough of the story. If our revolution was from context-sensitive stats to context-neutral ones, the new one is from outcome stats to skill ones. If Matt Adams hits a ball that, based on launch angle and exit velocity is a home run 92% of the time, but Albert Almora makes a great play on it, has he succeeded or failed? Jharel Cotton allowed five runs in 4.1 innings Wednesday night; as Eno Sarris tweeted, however, the four run-scoring hits off Cotton averaged 75 mph off the bat. So was Cotton bad?
This is where the conversation is now, and like those writers a generation ago, I’m faced with the choice between sticking to what I’ve done, or learning. It’s not easy; I had to corner a friend recently to get a primer on exactly what I’m looking for when it comes to spin rates on different pitches. I’m trying to figure out whether “barrels” tells us anything we didn’t know about who the best hitters are. On most days, I’m just trying to figure out how to get access to this information. The further we get from box scores, the more specialized skills, the more proprietary tools, are needed just to make sense of those terabytes.
No matter the year or the lessons, we’re all still learning about baseball. In 1996, it was translating performance based on competition and run environment; in 2000, it was DIPS theory, and what pitchers could and could not control; in 2009, it was measuring the differences among catchers in how balls and strikes were called (“pitch framing” to most, “systemic violation of Rule 2.00” to me). Now, it’s spin and launch angles and route efficiency. In 2026, I assure you, it will be something else. The lesson that runs through all these moments is this: if you cling to what you once knew, you’ll be left behind. Keep learning.
“if you cling to what you once knew, you’ll be left behind. Keep learning.”
Couldn’t be any more true of any field or walk of life.
Indeed! As with so many things, the Simpsons said it best.
This is forearm tattoo material.
“If our revolution was from context-sensitive stats to context-neutral ones, the new one is from outcome stats to skill ones.”
Best description I have seen of the boom in data-driven analysis over the last few decades. Well said!
“Words are my skill, not numbers” — and a finely-honed skill it is, too. Those of us who frequent Fangraphs are occasionally accused of paying so much attention to the numbers we miss the beauty of the game; this of course isn’t true (the numbers enhance the beauty, not distract from it), but I do think sometimes we employ the descriptive (and prescriptive) value of numbers so much we occasionally forget about the beauty of words, at least when they’re wielded by a master.
@Joser, agreed, but I’ve read more than enough “stats say this, shut up” people to know that not everyone in our little world appreciates the written word. Subtlety and context are not everyone’s friends.
Outcome stats are always descriptive of the past but only some are predictive of the future, and to varying degrees.
It’s the skill stats that are often not descriptive but may be powerfully predictive.
We enjoy knowing about the past, but both fans and front offices want to understand what’s likely to happen next.
This is great, thank you!
“The more I learn, the more I realize how much I don’t know.” – Einstein
Statcast has opened up a world of things I don’t know, which I find incredibly exciting. I do have a number of stathead friends around my age (50) who have hit the pause button, which has really surprised me. I can’t relate to walking away from lifelong learning.
Wow, can I relate to this. My first Bill James Abstract was 1984, and a was a huge stathead through the 90s. Then life kept me busy for a few years, and when I come back I find sites like Fangraphs and just absurd amounts of data, whole *dimensions* of data that we never even dreamed about back then (Spin rates? What the hell?). It’s like taking Algebra II in high school, and then 20 years later enrolling in Calculus.
I have no point here, except to tell all of you to get off my lawn.
Ah yes that voice that unheralded voice that really needed amplifying. Joe Sheehan.
Please make me a resident I also have not been relevant since 2008
Irrelevant since 2008, but apparently an unbroken thread of d!ckheadishness that continues unabated to this very day…
“Words are my skill, not numbers”
This was always the problem: a self-described “stathead” who was never good at interpreting stats. You could literally fade his “my guys” posts at BP and make a (fantasy or handicapping) profit.
I read your other piece that you linked, and I couldn’t agree more on the automated zone. Can’t wait to read more, man!
Rabble rabble youll have to pry my RBIs from my cold dead fingers
As a great fan of your newsletter and your appearances on David Todd’s radio podcasts, great to see you here!
Fine article, but I can’t help teasing you for this comment:
“It will take some mental gymnastics to compare velo figures from 2017 to previous years …”
Yes, adding or subtracting 1 can be so taxing.
Are you sure it’s that simple? I don’t think we know that, yet.
It’s not, it also depends on extension
The actual math isn’t hard. It’s remembering to do it every time when you write or talk about a pitcher’s velo, especially on days when you might talk about a dozen guys on air, or make reference to a half-dozen in a piece. In writing, it’s also have to explain it every time. (“Why did you say he’s lost a tick on his fastball when this chart says he’s the same?”)
It’s not the math. It’s the process.
Words are indeed your skill, Joe. Glad to read them here for a while.
Already a Sheehan newsletter subscriber, glad to read you some more on FanGraphs. Welcome Joe!
Think of the things that a “smart”, SABR-friendly, DIPS-aware baseball fan might have scoffed at as recently as 10-15 years ago that are now conventional wisdom.
– that defense can be immensely valuable. IE, a decade ago I would have looked at Kevin Kiermaier and Matt Kemp and thought they were comparable players, and probably given the edge to Kemp because of superior OPS. And potentially derided the “defense wins ballgames” crowd as out of touch.
– that catchers can have a meaningful difference in pitcher outcomes. “Catcher ERA” was a silly notion, full of noise and signifying nothing.
– that Murray Chass was just a curmudgeon and not some sort of evil lizard in a skin-suit.
OK, so I clearly ran out of ideas after two, but the point remains:
We’re lucky to get to enjoy a wonderful sport during a fantastic evolution of our understanding and appreciation of it.
This is a much more humble, gracious Joe Sheehan than I remember from the early BP days. And hopefully a more humble reader.
Excellent post, Joe. Your narrative made me realize just how fortunate baseball fans are today.
This is very good. Also, it does touch on something that I think is easy to lose track of inside the sabermetrics bubble, which is: how can we keep the game accessible when the conversation around it has grown to such enormous complexity? Sophisticated understanding is good, but not everyone has the ability or the opportunity to achieve that understanding. It would be a shame if baseball gained a reputation for exclusivity along educational lines, given that it is already grappling with that issue along race and class lines.
“I knew both from the Usenet newsgroup rec.sport.baseball (r.s.b), which was the primordial soup of internet baseball writers.”
Greetings, Joe, from another aged stathead spawned in that soup.
I hope Mr. Sheehan’s residency extends far beyond this month.
Great post Joe! Completely agree with you here.
I’ve been a longtime subscriber to Joe’s newsletter – good on Fangraphs for giving him this forum.
What a fine post in prose that’s easy on the eyes. It reads like a complete chronicling of my baseball stathead fandom. I know it is so for many others as well.
Welcome, Joe!
Great recap of the progression of baseball science, and of what it feels like now to be a stathead “of a certain age.”
Appreciated this piece. Raw honesty.
Joe – As a young-ish stathead working in a world far removed from baseball, your words are incredibly helpful. Trying to navigate two worlds where most have have never considered data but some are hyper excited about its potential but can’t articulate its usefulness has proven difficult. Baseball analysis is the most useful thing any ‘real world analytics’ analyst can discover. Article like these help those analyst bridge the gap. Your willingness to write about your experience will help shape careers. I thank you, sir.
It’s so hard on both ends. On one end, there’s a lot of new stuff hitting the field that I have issues keeping up with. On the other, there are many things being taken for granted such as OBP or OPS or WAR as fact just because they’ve been around for so long. Sabermetrics is becoming more art than science and identifying the way to separate the signal from the noise into something valuable… then presenting to readers in a form that’s consumable.
Very true. The hunger for more information sometimes means that the glitzy new stats receive inordinate attention, and certain “stathead” sites have been known to push their creations before their utility has been confirmed. That’s one of the main problems I had with BP back in the day, although I understood then and now that they were using aggressive marketing to build their business. It obviously worked. In any case, many of the “old” stats still have usefulness even if we know they are flawed, especially if we know the ways in which they are flawed.
I fear that the new granular data is so tempting that even the wisest in our community can be lured into overweighting “skills” data versus statistically significant outcome samples. The tension between skills and outcomes shows in an exchange I had last July with an esteemed analyst, regarding three Dodgers SPs:
— Me: Scott Kazmir in the last calendar year has a 4.45 ERA and 30 HRs in 176 IP.
— Analyst: Citing Kazmir’s ERA and HR rate while ignoring every other positive is intellectually dishonest.
— Me: Brandon McCarthy has looked great in 4 starts, but last year he stank in the same number of starts, and he wasn’t good the prior 2 years, either.
— Analyst: Saying that McCarthy “stank” last year and the year before betrays a lack of knowledge of his improvements.
— Me: Bud Norris is bad.
— Analyst: Ignoring that he added a new pitch and has run a 3.40 ERA/2.82 FIP/3.04 xFIP over his last nine starts is silly.
My point isn’t that my takes were borne out to a comical degree, with subsequent ERAs of 5.40, 9.17 and 10.93, respectively. Rather, it’s that the esteemed analyst believed the skills data — Kazmir’s K/BB stats (or something?), McCarthy’s “improvements” (presumably evidenced by some inside “knowledge”), and Norris’s new pitch and fine FIP (in a very small sample, which followed a run of horrible work in a similar sample) — were more predictive than a relative mountain of prior outcomes, including that pitchers age 31-32 overwhelmingly tend to be in decline.
We all like to find hidden meaning, especially to predict or explain surprise changes in performance. The Statcast-type data offer a new tool for that. But the science of reading that data is so young. Yes, the early interpretive efforts are essential to advancing that science. But they *are* early efforts. And while new data will eventually help us know which performance spikes are meaningful, they don’t change the fact that most spikes are noise.
A main tenet of the first sabermetric movement was to trust measurable outcomes, viewed in appropriate context — including historical context — over eyewitness impressions. I worry that the new data, in its raw form, is just scouting wrapped in a new cloak.
A big difference is that the first sabermetric movement made its gains on player evaluation by basing decisions on outcome-based metrics. I think the current focus on the “skills” related data is fueled by the technological imperative to find and use data that can help to improve player performance, since much of the gain from improved player evaluation has become mainstream.
Your penultimate paragraph is the key. The art of analysis is wedded to knowledge of the target being analyzed, meaning that baseball insiders should theoretically have a better interpretation of the numbers than us laypeople. But, they still need to be on the lookout for statistical misinterpretation, conformation bias, etc., which remain vitally important.
Great column. Thanks, Joe!