Since the Enlightenment, champions of progress have urged us to break free of the chains of tradition.
Just because "we've always done it this way," is no reason to keep doing it this way. It is irrational, it is dumb, indeed, it is frequently dishonest, to cling to traditions, they say. If we aim to understand the world and control it — the abiding ambition of all empirically minded thinkers — then surely we can dispense with the baggage of inherited convention.
Keith Law has just published a book that explores this question. The book is opinionated and it is sparked by fury. Indeed, Law writes as one who speaks truth to power. It is written by someone who thinks of himself as at the vanguard, the revolutionary forefront.
It is now possible, he insists, indeed, it is now mandatory, that we use mathematical analysis and statistics not only to evaluate human achievement, but also to learn how to predict it in the future.
I exaggerate maybe just a little bit.
Law's book is about the use of statistics in baseball. And while his assault on the Old Ways is driven by a real sense of outrage at the way irrational tradition shackles progressive thinking, he confines himself, by and large, to bad thinking in the domain of baseball. It is baseball he wants us to learn to think right about.
Law is a writer at ESPN and his his book, published in April, is called Smart Baseball: The Story Behind The Old Stats That Are Ruining The Game, The New Ones That Are Running It, And The Right Way To Think About Baseball.
For Law, the "old stats" are ruining the game. Batting Average, for example, is a terrible measure of a batter's offensive "value," since it considers hits-per-at-bat. This is doubly wrong-headed, he contends: It ignores the fact that not all hits are created equally (a home run is worth more than a single), and it disregards the batter's offensive achievements (e.g. walks), which don't happen during at bats (since not all plate appearances count as at bats). Likewise, Runs Batted In is not only uninformative about how good a player is offensively, it is dishonest, for it confuses his accomplishments with those of his teammates, Law says. You can only drive batters in, after all, if there are runners on base to be driven in.
Or consider the evaluation of pitching performance by wins; this is even more outrageous, he says. You can only win if your team scores, and the pitcher has no control over that. The idea that it is the pitcher who wins is premised on the idea that good pitchers have a kind of magic that leads their teams to victory. And that, Law is certain, is so much nonsense. Praising an individual player for results over which he has nothing resembling control isn't very bright. It isn't going to help you figure out what's really going on on the field, and might very well lead you to make bad baseball decisions.
We use statistics, Law holds, to evaluate performance. We want to understand what a player actually does on the field, and we want to predict likely performance going forward. We need objectivity to do this. We need data. We need metrics that cut through the noise to the reality. The last thing we need are old fashioned prejudices about pitchers winning games and RBI being a measure of a player's offensive value to his team, he says.
Can we do what Law and his fellow "quants" demand? Can we use numbers to assign value, to sort through praise and blame, and to ground baseball decisions in matters of value-neutral fact? I get it that this is something baseball executives want. Michael Lewis explained in Moneyball that the new statistics make it possible to discover sources of baseball value that traditional thinking has tended to ignore. And I get it that if you're a player, or a manager, or a fan, the problem of evaluating and predicting is of the greatest importance.
But is it actually possible, in baseball, or in life, so to regiment, comprehend and control human behavior?
I think there are reasons to doubt this.
One of the things that particularly bugs Law about the RBI stat is that there are cases, as he notes, where the official scorer has discretion over whether to award the RBI. He continues:
But in fact, I would argue, all baseball stats rest, finally, on just this sort of subjectivity. Consider, at the lowest level, baseball is about hits and outs. For example, Law argues that the basic job of a batter is to not make an out -- that is, to get on base.
But are outs determined in a value-free, objective way? Not really. Very frequently, at least, the question of whether an out was made is a judgment call. Instant replay hasn't changed this. It's just removed the required judgment call to a remote location.
And the same is true of hits themselves. When is a hit a hit, and when is it the result of a fielder's error? Nothing determines this other than the decision of the official scorer.
And let's not even get into balls and strikes!
However you look at it, the low-level facts on the ground, the smallest units of meaningful baseball — hits, outs, balls, strikes, foul or fair — are themselves intrinsically soft, squishy, value-laden matters of interpretation.
Bring the biggest quantificational canon you can find. It won't shoot straight if you set in down on shifting sands.
But maybe this is not a bad thing. Maybe this is what we love about baseball. We are called on to evaluate, to make choices, to make predictions, to lay odds, precisely when there are no algorithms or mathematical rules to do this for us.
I don't advocate a return to tradition. I think Law and his colleagues are right that there is a value in new analytical tools for thinking about baseball. But that's a far cry from accepting his idea that it is possible to use numbers, by themselves, to identify and control value, in baseball, or anywhere else.
Want to know what happened on the field? You'd better take a look.
Alva Noë is a philosopher at the University of California, Berkeley, where he writes and teaches about perception, consciousness and art. He is the author of several books, including his latest, Strange Tools: Art and Human Nature (Farrar, Straus and Giroux, 2015). You can keep up with more of what Alva is thinking on Facebook and on Twitter: @alvanoe
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