Billy Beane is a former baseball player whose career most people zip past for the simple fact that it was pretty average. But I find the fact that he played major league baseball for six years pretty awesome! Seriously—it’s amazing when anyone gets to the majors, let alone sticks around for six years! Beane subsequently became famous for his time as the General Manager of the Oakland A’s, where he introduced a data-driven approach to the game popularized in the book and subsequent movie, Moneyball.
Beane’s statistical-oriented management approach is called Sabermetrics (or SABRmetrics). Its point is to break the game into small, quantifiable chunks of data that can be analyzed to inform and evaluate decisions. This was a huge perspective shift, for prior to this the game was played in a dark age of obscurity and delusion, grim days when managers and players made decisions based on mushy pablum like feelings and intuitions. Sabermetrics eradicated the soft, squishy stuff and replaced it with hard facts that are now used across all aspects of the sport. Front offices, managers and even players now regularly consult stats. Data have also seeped down into the fan base, which we’ll return to below.
Beane was the A’s GM from 1997 — 2015. During that time the A’s made the playoffs four years in a row and also set an MLB record, 20-game win streak. They accomplished all this largely without big name superstars, which is another way of saying cheaply, relative to big spenders like the Yankees and Dodgers. They were so good at being thrifty the team eventually saved up enough money and left Oakland for Las Vegas, an absurdity that can’t be pinned on Beane or Sabermetrics.
That Beane’s approach changed the game is beyond dispute. Stats are everywhere in baseball today and are increasingly utilized in other sports. This approach was so impactful that within a few years every team had a squad of geeks running (around) their back offices. The shift is not unreasonable to compared to the Beatles, whose arrival spurred a comparable demand of imitators.
Whether Beane’s approach deserved the imitation it received is a hypothetical. One way of imagining an answer would be to examine his results. Since this is an essay somewhat about stats, let’s use them to evaluate Beane’s success. The A’s winning percentage while Beane was GM is .487. You don’t need a degree in advanced mathematics to determine that number is below average. This raises the obvious question: Were Beane’s imitators acting on what the stats factually revealed about Sabermetrics, or were they relying on their feelings, intuitions, and prejudices, aka: the very “soft” information a data-driven approach was supposed to remove?
∑
The reason Beane is considered successful has nothing to do with win percentages and everything to do with another metric: dollars. Analytics enabled the Athletics to spend less money while still obtaining average-ish results. Since baseball is very much a business measured in bottom lines, this approach made the A’s more efficient. As Beane put it, “Running a sports team is ultimately about maxisming the dollars that you have in being efficient.”
(Sorry about the missing Z; the author of the article this quote is taken from is British. Beyond wondering what they’re doing to our language over there, another interesting question arises: If Beane, who’s American, speaks maximizing with a Z, but the author, who’s British, hears and records it with an S, what’s the proper understanding of the word?)
Evaluating how efficient Beane’s efficiencies actually were depends, as it usually does, on one’s perspective. If you were one the of A’s owners, you spent less and obtained almost-average results, a fact that likely explains why other teams imitated Beane’s approach. If you were a player, however, this was arguably just another step down the path of reducing you from a human into a uniformed conglomeration of statistics.
Because of the money involved some readers may struggle with the idea of empathizing with the players. The current league minimum is $760,000 annually, a number that is objectively a lot, though it’s peanuts next to the $12-billion in profits MLB recorded last year. Still, most players earn more in a single season than many fans earn in a decade (or a lifetime). And because baseball contracts are guaranteed, you end up with situations like this: In 2020, Anthony Rendon signed a 7-year, $245-million contract with the Anaheim Angels. Rendon has been perpetually injured since then, though he has found time to assault the occasional fan. To date he’d only played in 257 games for the Angels, which means he’s made roughly $1-million a game to get a hit 24% of the time. A lack of sympathy is understandable.
∫
Either way you measure the efficiencies, the game is now awash in VORPs and WARs, launch angles and WHIPs and BABIPs, all of which sound like fetish terms used in a role-playing game. Which brings us to the fans. If you’ve watched a baseball game recently, or God help you talked with a friend about his fantasy baseball team, you can’t miss the prevalence of stats. They’re almost literally everywhere in today’s game, and many people—aka, Dudes who lack the emotional maturity to discuss other topics—think they’re useful information for understanding the game (as a card-carrying member of the dude-avoidance-tribe, I can comfortably make that assertion) Since stats are considered so essential, we should examine what they actually can tell us about the game.
Baseball is unique in that it has some very objective parameters that allow for an absurd amount of data to be collected (this is in contrast to football, a sport that collects piles of data but still can’t consistently define if or when a ball has been caught). But even with all the cameras, tracking devices and biometrics available, baseball is obviously not a controlled laboratory. This means there are way too many uncontrollable factors for anything genuinely predictive ever to be formulated.
A player’s batting average is simply the average of his past batting performance. A .487 win percentage reflects the average number of games Beane’s A’s won. Some measures involve multiple variables and comparisons and are more convoluted, but—and this is key, really: if you take nothing else from this essay please remember this—descriptive stats can only describe events that have occurred in the past, and can never be used for prediction.
Let me repeat that: BASEBALL STATS CANNOT PREDICT ANYTHING. Remember that next time the announcer tells you Cal Raleigh has a 38% chance of hitting a home run in a specific at-bat. That’s misleading, willfully so since these stats are usually provided by a tech company that prides itself on its mathematical wizardry. That 38% can only tell you how Cal or other others has performed in the past. This is actually fortunate for Cal_Bert (my preferred nickname, as The Big Dumper is atrocious) because in the present at bat he, like everyone in his position, is always looking at a 50% chance of hitting a home run. The math is astonishingly simple: he will or he won’t.
∉
In addition to being incapable of prediction, all this data can’t explain the game to you in any meaningful way, a fact that should be so obvious it doesn’t need explanation. More significantly, none of this data can tell you a single thing about what it’s like to experience a baseball game, a fact we won’t skip over. Does knowing a pitcher’s WHIP tell you anything about how he felt when he noticed tightness in his left hamstring in the top of the 5th, a fact he only noticed after the light reflecting off the sunglasses worn by the blond woman in the third row behind home plate made him pause midway through his delivery?
You might push back by saying that I’m talking about another person’s experience, which is notoriously difficult to articulate, with or without statistics. Fair point. So let’s focus instead on your experience attending a game.
Which stat can tell you a single thing about that intrusively floral smell outside the mens bathroom, as if a lemon had been drowned in boric acid, a smell so insistent and penetrating that you can’t ignore it, a small that reminds you of the PineSol your grandmother used to wax the furniture, which was almost the exact smell you noticed in your high school locker room that year you tried out for the basketball team only to realize that you should’ve stuck with the French horn? What piece of data can explain how all of those disparate memories popped into your head in one cohesive unitary moment?
Which set of stats reveals how the grass shimmers behind second base when the sun finally breaks through the clouds? Which data captures how the plastic seat sticks to the back of your thighs in a way that causes your legs to make squishy, farting noises every time you shift your weight, a shifting-squishing-farting-hotchpotch you have to repeat continually, despite the embarrassed looks coming from your neighbor, because you’re sweating terribly and the seat’s so awfully uncomfortable?
To put this comparatively, which stat better captures your experience of attending a game: knowing Cal_Bert’s WAR or the whole shifting-squishing-farting-thing?
=
The lived experience of attending a game is not quantifiable with any amount of calculations, for it’s a qualitative leap into the purely subjective. Before pursuing that further, I will note it’s possible data could be captured to track how many times dudes like me use data to express our experience of the game, just as another stat could measure how often we conflate our lived experiences with statistics. While hypothetical, both numbers are frightening to consider.
I hope it’s clear I’m not opposed to math or science. I wouldn’t be able to write on this computer, just as you wouldn’t be able to (gleefully) read it on your device, if I were. A lot of what the sciences, especially the applied varieties, have to offer is staggering and should be esteemed, including the fact that I’m writing this on an entire constellation of technologies that didn’t even exist when I was born!
That said, a lot of what we consider revelatory isn’t much more meaningful than that 38% mentioned above: a potentially interesting summary of the past, but one that needn’t have any relevance to the present. For all of its amazing advances, the applied sciences cannot — and I would argue will never be able to — tell me anything truly informative about my experience of a baseball game, or anything else.
≈
Let me conclude this by suggesting a test. Get some cheap tickets and go to a baseball game. Grab a beer and a bag of peanuts. Don’t consult your phone for data about WARs and bat speeds and OBPS. Instead, simply enjoy the fact that some amazingly talented humans are performing a highly intricate and challenging activity. Watch the grass shimmering in the sunlight behind second bass. Pinch your nose when you walk past the mens room. And don’t apologize when your thighs stick to the seats and it sounds like you’re farting every time you shift your weight. You don’t need stats to show you that life’s right there. And in the crack of a bat, it’ll be gone.