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The New Stats Explained
by Ryan T. Campbell
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Johanna quoted in the Chicago Tribune about Fan Safety.
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This Month
Michael Street reviews Lee Panas'
Beyond the Batting Average
Monday, June 7, 2010 10:00am
I've been playing fantasy baseball and writing about the game since the mid-1990s, and both can be a challenge, especially to the novice. Once you try to go beyond familiar and flawed standard statistics like ERA, batting average, RBI, or wins, it takes a lot of self-education. And it's only gotten more complex in the past ten-plus years.
There's a thicket of advanced metrics out there, each one trying to be the best one to measure the value of a player to his team. Trying to figure out what each stat means and how it's calculated means jumping from site to site and reading forums or arcane webposts that require even more reading to understand what the heck these guys are talking about. I've often wished that someone, somewhere would just explain them all in one place.
My wish has finally come true.
Lee Panas, a baseball writer, Detroit Tigers fan and research analyst at Brandeis University, has written Beyond Batting Average, a book that breaks down every baseball statistic you can think of (and many you may never have heard of) to show what the stat measures, where it came from, and what problems it presents. This incredibly helpful guide needs to be on the bookshelf of every baseball fan, and should be required reading for anyone wishing to be a baseball analyst, scout or front-office executive. It's just that good and that important.
More than any other sport, baseball is a numbers game, and understanding those numbers have become essential to understanding the game. Ever since Alexander Cartwright decided that the game should be played on a diamond ninety feet on a side, coaches and writers have tried to quantify success with good, hard numbers. We can all gasp at a deftly turned double play, a crushing grand slam, or marvel at a pitcher in a groove, but how do they compare with other plays and other players on different teams and different eras? How valuable was that each play in the context of the game and the team's season? That's where the numbers come in.
Deciding how to measure player performance- in essence, how to cram that gasp into a quantifiable box-is essential in so many areas of baseball. Scouts and owners need to assess and compare players to squeeze every last drop from their payroll. Baseball writers and on-air personalities need to easily show why this player is overpaid or that one is underperforming. And fans like to argue why, say, Tim Lincecum is better (or worse) than Nolan Ryan or Sandy Koufax.
That fan conversation has also become a business of its own, as those same fans play out their arguments in fantasy baseball, and analysts like me advise those fans on which players to keep and which to discard. Listening to a group of fantasy geeks discussing statistical regression and calculating home run-to-fly ball ratios would make anyone wonder how there could be a deficit of math skills in this country.
This numerical obsession has grown out of sabermetrics, an attempt to find the best statistics to assess player performance, and it's a practice that's gone from the fringes to the mainstream. Televised games routinely feature sabermetric stats like OBP and SLG, and Boston GM Theo Epstein is one of many sabermetricians to rise to prominence in front offices across the league.
Sabermetrics has revolutionized how we all measure the game, leading to the statistical alphabet soup that Panas so deftly helps us interpret. Like any good historian, Panas starts with the early game, showing how the numbers have grown in parallel with our understanding of the game.
In the early game, pitchers were merely supposed to throw hittable strikes, not fool batters, and batting metrics became transcendent. An early form of on-base percentage was introduced in 1879. Later, the moundmen grew in importance and analysts found ways to measure pitching performance, and statisticians didn't develop good fielding metrics until much later. Not coincidentally, teams like Tampa Bay and Seattle have only recently exploited the value of a player who knows what to do with that leather pouch strapped to his hand.
It's the growth and development of these statistics that give value to Beyond Batting Average value. I would imagine that most fans have been educated in much the same haphazard way that I have. We hear about new statistics (or ones that are new to us) and we dig around for an explanation of it, ignorant of the context of where it came from.
Even after learning about a bunch of stats, it's often hard to understand how they fit together. How can one statistic compensate for the deficits of another? Which one was developed first? Which is the best for valuing a player or a team, and why? More often than not, writers and fans grab hold of the one metric we truly understand, and use it to try and explain everything and everyone, whether it's appropriate or not.
Panas solves all these problems by tracing the development of each statistical element-batting, pitching, and fielding-from the earliest days of the game all the way up to the bleeding edge of statistical analysis today. He shows how these stats have combined to measure simple on-field performance (how many runs did A-Rod drive in?) to assessing that performance in terms of contributions to a win (how much did Franklin Gutierrez's glove contribute to Seattle's 2009 record?) and comparing that overall contribution to another player (who meant more to their team, Dustin Pedroia or Chase Utley?).
To help his explanations, Panas uses graphs and charts from some of the best baseball websites and enlists the help of such statistical superstars as Pete Palmer (writer of the classic The Hidden Game of Baseball) and Tom Tango (who developed stats like FIP, Fielding Independent Pitching). Employing that level of advisory talent not only shows how rigorously researched this book is, it also shows how well regarded Panas is in the sabermetric community. These guys don't lend their talent to just any statistical effort, and their support of Beyond Batting Average is well deserved.
As an analyst already familiar with the stats he explains, I was blown away by how well Panas explained the origins of each, showing how its deficits led to other statistical developments, and how choosing the right ones can lead to correct player valuation. He not only clearly lays out each statistic, requiring virtually no math beyond simple arithmetic and fractions, he also offers examples of each, showing how (say) Jason Giambi's 2006 season, when he hit .256, was more valuable than Ivan Rodriguez's .300 batting average that same year.
After each description, Panas shows how that statistic is distributed across MLB players, making it easier to grasp whether a .450 SLG is good or a 10 VORP is bad. Panas discusses repeatability of statistics (how likely is a player to produce a similar stat line in succeeding years?), offers chapter wrap-ups when appropriate, and points the reader to later chapters, so the book can be read both cover-to-cover or used as a reference for individual stats.
This is yet another feature that makes the book such a great buy. It's not only the kind of book you will read through, it's one you'll want close at hand when trying to understand a new statistic or remind yourself how one led to, or complements, another. If you only want to learn a bit, you read a bit; when you want to learn a lot, read a lot.
Because Beyond Batting Average is published through Lulu, you can get either an affordable paperbound edition, or download it as a PDF (even cheaper!) to keep it on your favorite handheld. And buy it you should; there's no better book around to help you slice through the explosion of basic and advanced metrics that have become essential to understanding the elegant game of baseball.
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