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Smart Baseball

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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

With Leigh’s saber slant on “Casey at the Bat” earlier today, it felt like a good idea to pair it with KP’s latest literary dive. Both are splendidly written. Enjoy.—BB

In general, my conception of the SSS Literary Supplement is to introduce little or lesser-known books that readers here may find interesting and worthwhile. I don’t, in general, see a whole lot of purpose for writing about books that most of us have read and already have set opinions about.

For the most part, all that does is either confirm what you already believe or conversely, serve as an iconoclastic take that does little other than stir arguments. And while I don’t mind arguments, when we’re talking about a book, especially in the case of fiction, arguing often just boils down to: “I like/don’t like it, so you’re an idiot if you don’t/do.” While I do want to start discussions, where I can, arguments get old fast and encourage us to type things we later wish we had typed differently.

I am guessing a great number of folks here on the site have read Keith Law’s Smart Baseball. It’s been out for a couple of years, and a lot of people have their, sometimes strong, opinions of Law. However, a number of posts and comments over the past few months have shown that there are also a number of us here for whom SABR-like analysis, with lots of its attendant statistics and terminology, are either new, unclear, or both.

I happily banter WAR around, but once we get much deeper than that, I retreat to a respectful distance and sit and watch. And while this does little to dampen my love of baseball, I find myself gazing longingly through the window from time to time, wishing I understood a bit more of the detail and nuance. And a few months back I asked for some recommendations for books that would help me out. One of those recommendations was Smart Baseball.

Law’s book is broken up into three parts, creating a logical flow from the old stats we all know from the backs of baseball cards, the broadcasts of our youth, and the depressing newspaper box scores of our present. SMRT Baseball (with an acknowledged hat-tip to Homer Simpson), starts us off with explaining why the old, “traditional” stats either don’t tell us very much, mislead us, or are downright liars. Batting average, RBI, W-L records, and saves all come in for some well-earned roasting, stolen base totals and fielding percentage are held up to scrutiny (and found very, very wanting), and concepts like “clutch” and lineup protection are summarily dismissed as the hokum they turn out to be the moment you turn on the lights.

In each case, Law details why these stats don’t illuminate anything meaningful to us, except in terms so broad as to render them useless for understanding a player’s performance. I suspect that most of here do understand this, at least intuitively based upon our general knowledge of what all goes into playing baseball well, but Law does a nice job of laying out the evidence against in a logic and coherent manner.

The second section, and meat of the book, is Smart Baseball, where he lays out the case for why “advanced” statistics are vastly more useful, and tell us a lot more about individual players. He looks first at stats that are now commonly used and understood, like OBP, slugging percentage, and OPS, explaining why they are better at explaining how a hitter performs despite each statistic’s own shortcomings and insufficiencies. Law discusses ERA, too, and how it simultaneously illuminates and obfuscates pitching performance. And then he turns out attention to “new” stats, and this is where naïfs such as myself benefit.

For the math-phobic, Law doesn’t much go into the formulas themselves (there is not no math here, but very little). Still, Law proceeds to explain what, for example, wOBA (weighted on-base average) and wRC+ (weighted runs created) are, what they attempt to measure, and how these provide a fuller understanding of what a hitter actually does — without the noise of defense, park-related factors, and his other teammates’ performance. These, and the other advanced stats, try to hone in on precisely how effective, and hence valuable, an individual player is.

Pitcher A, who has a stellar defensive team behind him, will obviously benefit from that — as opposed to pitcher B, who is backed by, say, the 2018 White Sox defense, and suffers thusly. Or Pitcher C pitches in Colorado, while pitcher D pitches in Oakland. The purpose of these stats, and the formulas they’re based upon, are intended to put A and B on a flat surface, as it were, and see how they actually stack up against each other, as well as against pitchers C-Z, and pitchers A-Z from, if you will, 1957, or 1938, or 2003.

The biggest jump forward that has come with newer stats may be in measuring defensive effectiveness and results. Law shows how the newer defensive stats help us understand how those measurements work, as well as how far we still have to go in measuring defense. Having earlier exposed the futility of looking at a player’s fielding percentage, Law now looks at the much better accounting provided by range factor (useful, but limited), UZR (ultimate zone rating, helpful in figuring out how effective a fielder is in covering his general area of the field), and dRS (defensive runs saved, which attempts to refine that effectiveness, adding adjustments for parks and positioning, etc.), while still understanding the limits of these stats.

Law then looks at, and explains WAR, the all-inclusive stat that attempts to assign a value to the play of the overall player. While there are different formulas for arriving at a WAR figure, leading to such variations as bWAR (Baseball-Reference), fWAR (FanGraphs), WARP (Baseball Prospectus), and more esoteric variations like kenWAR (Kenwo, though this metric is falling out of favor here on SSS) and soxWAR™ (yours truly).

While these obviously vary across channels (if you will), all are formulated to assign value to what each player brings to the game (more directly, I suppose, to his team) but looking at their total performance, be it pitching, defense, hitting, throwing, etc. Different players will provide baseball-playing value in different ways, but they all provide a value that can be measured, if imperfectly. Even if it’s negative value. Again, the point isn’t that these stats are complete, or infallible in and of themselves, only that they are better than what came before.

Which then brings us, and Law, to his final section, “Smarter Baseball,” in which he examines a handful of stat-related issues, including the near future of developing greater statistical detail thanks to MLB’s Statcast and individual teams’ proprietary stats.

Statcast, in particular, promises to help quantify a great deal about how well players perform on the field. Defensive metrics, especially, stand to be improved and refined over the next few years, including — please note — the true significance of pitch framing, wherever that significance may ultimately fall. Interestingly, Law reports that many MLB teams are using these incredibly detailed looks at performance to enhance their medical approaches to players, hoping to be able to see and predict when injuries are liable to happen and then take steps to mitigate them. From a fan’s perspective, this new data is enticing, but still mostly out of reach, as teams guard their data (and the formulas they use to analyze it) jealously, jostling for advantages over the competition.

Law also, in this last section, looks at something currently near and dear to our hearts here lately: the Hall of Fame. Using advanced statistically analysis, as laid out in the first two-thirds of the book, he argues for and against a handful of recent picks (that is, 21st Century — the book was published in 2017). Law goes into great depth illustrating how Lou Whitaker should not only have stayed on the HOF ballot well beyond his first year of eligibility, he should have been a shoo-in. Conversely, fan and writer favorite Kirby Puckett should have had to buy a ticket to get in. And many others are discussed, on both sides. Law’s point is ultimately that while the traditional, mostly counting stats may have been the best available for judgment in the past, they are now wanting, and should be happily ignored now that we know better.

Conveniently, and relatedly, it took me long enough to get writing on this that I am able to add a note about seeing the Chicago Tribune’s baseball writers to publish their ballots for this year, along with their “thoughts” and rationalizations.

Mark Gonzales, in discussing Mariano Rivera, quotes statistics both “traditional” (ERA, saves), but more advanced (WHIP and OPS against). Not exactly cutting edge, but nice to see some deeper analysis. His comments on Roy Halladay, who he votes for, mentions both two Cy Youngs (ouch), and a “… for those who believe wins matter, Halladay led the majors twice.” Not quite sure if this is snark, or self-defense.

Teddy Greenstein complains about the Harold Baines vote, then justifies a Billy Wagner vote by citing his K/9 rate (which was pretty good, but seems an odd stat to single out), and uses Gold Gloves and (heh) fielding percentage to support Omar Vizquel. Partial credit for basing his Edgar Martinez vote, in part, on his career OPS, which he points out is higher that Alex Rodriguez’s.

Finally, there is dependable stalwart Paul Sullivan, who rails against PED users before using ERA (okay, some use), a perfect game (hi, Philip Humber), and complete games (once useful, but not much anymore), win totals (heh), and yep, more Gold Gloves, to make his HOF cases.

TL/DR: Smart Baseball is clearly written, and logically structured. For those like me coming to it with a fuzzy previous knowledge but engaged curiosity, it’s pretty useful in explaining not how all these fancypants stats are arrived at, but what they are trying to measure and explain, along with what they still miss. Law’s tone is conversational, occasionally a bit smug (see the bolded “right” in the subtitle), and once in a while a little condescending. But only once in a while. At no point did I feel talked down to, lectured, or hectored. Just talked to, and explained to. As noted, there is little math required. To his credit, Law readily credits those who created and honed these stats, his forbearers and contemporaries whose hard work he’s distilling here. At some point, I’m going to dive into a couple of their books.

Finally, as Law points out (and I will agree with), a lack of an understanding of advanced stats in no way hinders any of us from watching, enjoying, and loving baseball. It’s perfectly possible and acceptable to pay no attention whatever to the launch angle José Abreu’s homer to left and enjoy it every bit as much as the woman next to you who’s calculating it in her head based upon the replay on the Jumbotron. On the other hand, if you want to delve into the nuances of performance that are now available to us, here’s a good starting place.