Watching the Masters over the weekend I was bombarded, over and over, with a stat that didn’t exist when I started watching golf as a kid. Maybe you remember those old standbys — greens in regulation, putts per round, driving distance, fairways hit, etc. Those are gone, replaced with a newer, supposedly more mathematically sound statistic called “strokes gained.”
There are a few different variations of this: strokes gained, strokes gained tee-to-green, strokes gained putting, etc. According to the PGA Tour’s website:
Strokes gained is a better method for measuring performance because it compares a player’s performance to the rest of the field and because it can isolate individual aspects of the game. Traditional golf statistics, such as greens in regulation and putts per green, are influenced by a player’s performance on shots other than those being measured. […]
Strokes gained: total simply compares a player’s score to the field average. For example, a player will gain three strokes on the field if he shoots 69 on a day when the field averages 72. A player who shoots 74 on that day loses two strokes to the field.
Strokes gained: putting measures how many strokes a player gains (or loses) on the greens. Strokes gained: tee-to-green measures all strokes not taken on the putting green. […]
Strokes gained: tee-to-green + strokes gained: putting = strokes gained: total
This, the PGA Tour explains, is a concept developed in 2011 by Mark Broadie, a professor at Columbia University (to whom, coincidentally, I still owe many thousands for my own graduate degree). And to be fair, a lot of people, including the Uproxx Sports editors I pitched this piece to, love this stat. I suppose it can tell you how much better one player drove the ball or hit approach shots than the rest of the field, but it’s a failure of imagery. It’s a qualitative, this guy is doing this particular thing a lot better than the field stat. Which I find sort of pointless in an individual sport, because at the root of it, it just says “this guy is doing better.” And for that we already have the leaderboard. A green in regulation or a fairway hit is something you can picture, and that alone makes it a more effective “statistic” than a strenuously accurate comparative measure.
Regardless, it’s of a piece with the same “Sabermetrics Revolution” that overtook baseball and gave us Moneyball and everything else. This quantitative (supposedly) revolution has gone on to affect virtually every other sport, the most obvious example being baseball, where old fashioned metrics like RBIs and stolen bases have largely been replaced by Sabermetrics statistics like On Base Percentage Plus Slugging. Which, as Brad Pitt taught us in a middling movie adaptation of a Michael Lewis book, is a much more effective measurement of a player’s value than the old stats. We’re all now expected to treat our sports stars with the same productivity metrics that corporations apply to their employees.
The same movie (and book, which like most Michael Lewis books is an entertaining and informative read) taught us that the old scouts were stuck in arbitrary group-think, relying on capricious measures like whether a player had a “good baseball body” or an attractive girlfriend to decide who to draft. They were in desperate need of a shakeup, which BIG DATA was only too happy to provide. Whether actual fans were in need of same is another matter.
The shakeup was probably necessary — at least for people who were trying to decide how much to pay their employees — but now it seems like we’ve merely traded one false God for another. Now we don’t repeat “defense wins championships” like an incantation. We don’t argue about whether Joe Flacco is elite (as much). Now Amazon Web Services (AWS!) can accurately (so they claim) calculate the percentage at which Patrick Mahomes will complete a certain pass! This according to NFL-season commercials I was forced to sit through approximately twelve million times. These up-to-the-second “chance of a successful play” stats are every bit as worthless as whether a player has the “correct” looking body or a sufficiently attractive lover. Either Mahomes completes the pass or he doesn’t. Do we need to know it was a 7% chance of completion to be awed by the play? Why would we need a hypothetical, faux-quantitative measurement of what our eyes were already telling us?
The problem isn’t so much that these sort of statistics exist, it’s that entities like Amazon expect us to care. To be as thrilled by these hyper-accurate hypotheticals (note the oxymoron here) as Amazon execs are by statistics measuring how much time their workers spend actually sorting and how much they spend pissing in bottles, say. It’s using data to create the illusion of certainty. The human is not a human, but a productivity machine. This kind of thinking has infected all of culture, where moviegoers are expected to care not only about whether they liked a movie, but how it fits in with the brand’s larger expanded universe. If, as John Steinbeck argued, poor Americans all see themselves as temporarily embarrassed millionaires, now television networks want sports fans to see ourselves as temporarily demoted team executives.
Like Amazon itself, the Sabermetrics Revolution has spurred a drive towards more and more “effective” and “accurate” stats, without ever questioning what the value of those stats actually is in the first place. A lot of people didn’t discuss the old stats because we believed that they were a perfectly objective measure of sports performance (nor did we care to do what these stats were actually intended to, measure a player’s “value”). We discussed them because they conjured an image in the mind. When someone says “home run,” you instantly picture the crack of a bat, the roar of the crowd, and a baseball sailing over the outfielders’ awed heads while some poor sap hangs his head on the mound. What happens in your mind when you hear, say, BPM, a fancy math stat for basketball players? Here’s the explanation of that one, from basketball-reference:
BPM uses a player’s box score information, position, and the team’s overall performance to estimate the player’s contribution in points above league average per 100 possessions played. BPM does not take into account playing time — it is purely a rate stat! Playing time is included in Value Over Replacement Player (VORP) which is discussed below.
League average is defined as 0.0, meaning 0 points above average or below average. Because above-average players play more minutes, there are far more below-average players than above-average players in the league at any time. A value of +5.0 means the team is 5 points per 100 possessions better with the player on the floor than with average production from another player. (In the 2018-19 season, teams averaged around 100 possessions per 48 minute game.)
Stats like this sound more like entries in Excel spreadsheets. Which, essentially, they are. They turn something heroic into drudgery. And that was fine for Billy Beane, because baseball was his fucking job. For the fans, of baseball, or golf, or football, or whatever, sports is not our job, and it shouldn’t have to be. It’s our escape from our jobs.
Sports statistics, I would argue, are a form of storytelling. The point was never to be a perfect, all-encompassing assessment of a player. What we’re looking for isn’t necessarily the most objective measurement of quality, but to be able to relive those moments of glory. The worst of these Sabermetrics-style stats take sports, a thrilling exhibition of humans transcending the normal boundaries of physical achievement, and applies to them the cold, corporate logic of extracting maximum value for minimum input.
I can understand if sports fans spent decades being terrorized by Skip Bayless and Stephen A. Smith takes about how “Tim Tebow is just a winner” and whether certain teams have that “killer instinct,” and that in that context hard numbers might feel refreshing by comparison. But if the antidote is a sports stat that’s harder to parse than my taxes, I choose the poison. You don’t have to be a team executive. It’s okay to just be a fan.