In a recent piece on Grantland.com, Jonah Lehrer takes apart not the fundamentals of sabermetric measures in sports, but rather how we use them. In doing so, Lehrer uses a rather slow and unnecessary windup about cars before reminding us 25 percent of the way in that the piece is not at all about cars. He also catches himself in a few traps from which he can't escape, leading to a conclusion we don't think he really believes. But he also makes some pretty valid points. Most of all, he touches off a storm from those who hold new numbers most dear – a predictable tempest, considering the source. (A good summary is here).

Those trying to bring sabermetrics into the mainstream are often young, intelligent and impassioned about representing their still-fledgling viewpoints. This also makes them seem smug (which they can be), thin-skinned (which they can be, though not as thin-skinned as Packers fans) and up in arms over anyone who would dare to challenge them because they are used to issuing the challenge.

Those resistant to new numbers are often more established. They like the stats they've grown up with. They like the eyeball test. And perhaps most importantly, they often like believing in heart-felt sports myths about scrappy underdogs. They hate seeing wins and losses reduced to probability determined by raw data.

And both sides want to be absolutely right when the truth, as usual, lies somewhere in the middle.

In many ways, it reminds us of the bloggers vs. mainstream media battle that, thankfully, we seem to have mostly moved past after several years of tired jokes about typing away in a mother's basement. The new argument often just as quickly descends into the gutter, with the sabermetric crowd being painted as a soul-less bunch that doesn't even like actual games and the skeptics painted as out-of-touch relics who'd rather have Nick Punto than A-Rod because he allegedly "plays the game the right way."

But again, that middle ground.

The old-school holdout might say, "I don't need OPS to tell me Barry Bonds was a terrifying hitter," which is true. But you might benefit from advanced metrics when figuring out what to do with, say, the Twins' outfield situation in 2012. Because your eyes and your heart might deceive you.

Similarly, there is validity to the intangibles of which Lehrer writes: "Here's my problem with sabermetrics — it's a useful tool that feels like the answer. If we were smarter creatures, of course, we wouldn't get seduced by the numbers. … But that's not what happens. Instead, coaches and fans use the numbers as an excuse to ignore everything else."

Indeed. And we're not necessarily talking about "heart" or "grit," or the other buzzwords that number-lovers smirkingly toss around. We're talking about players who [redacted] themselves when it really matters, regardless of what numbers might say they should do. We're talking about NBA guys who might have a great PER but are such a train wreck in a locker room that they ruin a team. Numbers can tell us a lot about puzzle pieces; they often can't fit those pieces together.

You can't always pretend that everything about sports is irrational. But you also can't always make irrational things rational. To extract more from numbers than is there to be mined is just as dangerous as ignoring the reality of what is there. So while Lehrer concludes, "This is the paradox of sports statistics: What the math ends up teaching us that is that sports are not a math problem," he is trying too hard to make a grey area black and white. So, too, are those who clutch too tightly to their win shares.

The middle ground that we might suggest: New ways of looking at numbers can teach us a lot of things about sports. Among the most important things, though, is that they can't teach us everything.

If you believe anything else, you're trying too hard to convince yourself of a truth that isn't there.