Beating Up the Sabermetric Strawman

It might come as a shock to FanGraphs readers, but not everyone has a use for the statistics we use on this site. Plenty of fans, and probably even some people more closely connected to the game, are comfortable with their personal observations. That’s fine. Baseball is a game, and the game is meant, first and foremost, to entertain. We all enjoy it in our own ways, and, as long as it doesn’t involve harming others, no one should disparage anyone else’s way of enjoying it. By hearing them out we might even find new ways to enjoy the game ourselves. But enjoyment is not at all the same as evaluation. That’s where we’ve run into some issues.

Earlier this year brothers Alan and Seymour Hirsch published a book called The Beauty of Short Hops, in which they argue that a reliance on sabermetrics ignores the finer aspects of the game. Yesterday on Grantland science writer Jonah Lehrer argued a similar point. While he does give nod to the virtues of sabermetrics, he spends most of his words talking about what the stats do not tell us. The crux, as with any article worth publishing, comes towards the middle:

But sabermetrics comes with an important drawback. Because it translates sports into a list of statistics, the tool can also lead coaches and executives to neglect those variables that can’t be quantified. They become so obsessed with the power of base runs that they undervalue the importance of not being an asshole, or having playoff experience, or listening to the coach. Such variables are the sporting equivalent of a nice dashboard. They can’t be quantified, but they still count.

Lehrer offers up this loaded assertion without one iota of evidence. He tells the story of the NBA Champion Dallas Mavericks and how one of their statistically bereft players helped change the tone of the series. But he doesn’t show that somehow the Heat threw aside intangibles while the Mavericks embraced them. It is patently ridiculous to think that any front office has thrown subjective analysis in the garbage solely in favor of statistics. Of course they consider if a player is an asshole and all of those other things Lehrer listed. They all affect a player’s performance on the field, and a front office’s job is to put the best possible team on the field.

Perhaps Lehrer’s assertion can be more appropriately tied to analysts, such as your gracious hosts here at FanGraphs. But even then it falls short. Yes, we argue mostly from a statistical standpoint, and oftentimes we make no mention of intangibles or even scouting aspects of the game. That does not mean that we devalue or neglect them. Rather, we’re focusing on one aspect of the argument. Other writers focus on other aspects. Sometimes these arguments will contradict each other. That’s fine, and even good in most instances. It gives us more to discuss, and with more discussion we can get closer to the heart of the matter.

This all takes us back to the age-old stats vs. scouting argument, which is actually one big misstatement. As many before me have noted, stats are not in conflict with scouting. They’re two different tools that teams can use in evaluating players. Stats are merely a record of what happened on the field. They’re sometimes put in greater context, but they’re the results of the game nonetheless. Scouting is a more subjective, closer look at those same events, and it does include other aspects such as a player’s makeup. Taken together they can provide a team with a reliable player evaluation. Either part without the other, though, can miss important factors.

Lehrer and the Hirsch brothers have done little except beat up on a strawman with a big target on its back. Plenty of people dislike statistics, and so writing about the perils of statistical evaluation is sure to catch some attention. Yet both of their arguments miss the point on many counts. Lehrer’s might be the worse offender, because he provides no evidence for his claims. No one — no one worth reading, at least — discounts the immeasurable side of the game. Since it is immeasurable, though, it is difficult to find evidence to back claims. Therefore, many statistically minded writers opt not to deal with it altogether.

Think of it like FIP. We all know that a pitcher who leaves a ball over the middle of the plate is probably going to get hit hard. And yet, we removed balls in play from the formula anyway? Why? Because we want to know one specific thing about the pitcher: how he performs in terms of the events he most directly controls. Stats-based analysts and writers know that a player’s attitude can affect himself and his teammates. But we’re more interested in the events as they occurred on the field. It’s not one to the neglect of the other. There are other writers and analysts who can better cover the other aspects of the game.