Baseball stats get even more specific

The statistical revolution came to baseball long ago, and these

days it’s as much a part of the game as cowhide, sunflower seeds

and functionally illiterate Bill Plaschke columns. And despite much

fretting to the contrary, the statistical revolution is nothing to

fear, loathe or run from.


enhance baseball. Any baseball argument is informed by the

numbers (even if you’re using the wrong ones), and any

understanding of this great game is made better and deeper by an

understanding of the numbers.

So, with those dearly held principles in mind, let’s take a

walking tour of some of the more illuminating stats that the

revolution has produced.

General concepts

BABIP (Batting Average on Balls in Play)

A conceptual knowledge of BABIP is essential to, among other

things, projecting the performances of pitchers. Broadly speaking,

a pitcher exerts the most control over whether a batter walks,

makes contact, hits the ball in or out of the park and hits the

ball on the ground or in the air. What becomes of a fair batted

ball that isn’t a home run, however, is largely a function of luck

and defense.

Most often, a pitcher’s BABIP will fall somewhere between

.290 and .300. If his BABIP deviates wildly from this (and his

career norms don’t justify such deviation), then you can probably

expect him to return to the .290-.300 range the next season.

By extension, when forecasting pitcher performance, it’s best

to look at his strikeout rate, walk rate, home run rate and

groundball-to-fly ball ratio, then make separate estimations of the

defense that’ll be supporting him. A traditional pitching measure

like ERA, of course, misses such vital nuances.

Replacement Level

When it comes to grasping modern statistical analysis in

baseball, there’s no getting around the idea of “replacement

level.” Replacement level is, for many useful measures, the

baseline to which all players are compared. The name says it all

— replacement.

Imagine that a team under tight budget and talent constraints

loses a regular contributor to injury or surprise retirement or

some other unforeseen phenomenon. To replace him, they’d turn to a

prospect who’s in need of further seasoning. Or they’d sign a

scrap-heap free agent. Or they’d pick up someone off the waiver

wire. Or they’d trade for a B-lister who’d require little in


All of that is the essence of the replacement player —

what you could summon up on short notice and with few resources.

The replacement player’s level of performance varies from year to

year, but it’s always measurably worse than the league average.

That’s the case whether it’s a replacement-level position player or


The concept is important because it’s not the hypothetical

average performer to which players should be compared; it’s the

contingency plan — the replacement level — against

which they should be measured.




Think of this as a souped-up version of that old standard,


ERA+ is simply ERA adjusted for the effects of the pitcher’s

home park and the league environment. That is, ERA+, unlike plain

old ERA, accounts for the fact that pitching in, say, Dodger

Stadium circa 1968 is ludicrously easier than pitching in Coors

Field circa 2000.

Also, ERA+ is scaled to 100, which means that an ERA+ of 100

equals a league-average ERA after you adjust for park effects.

Anything more than 100 is that many percentage points better than

the league average (again, adjusted for the pitcher’s home yard),

and anything less than 100 is that many percentage points worse

than the league average.

For example,


Rivera is the career leader in ERA+ (minimum 1,000 innings)

with a mark of 202, which means his career ERA is, on a

park-adjusted basis, 102% better than the league average (wow). On

the downside, Brad Lidge in 2009 posted an ERA+ of 59, which means

his ERA was 41% worse than the league mean after correcting for his

home park.


Here’s where BABIP comes into play for the pitcher. FIP

stands for “Fielding-Independent Pitching,” and, as the name

suggests, it attempts to decouple pitching and defense. It takes

into account strikeouts, unintentional walks, hit batsmen and home

runs allowed.

Also, FIP yields a number that

looks like ERA, so it’s in a statistical language the

everyday fan can understand. It’s useful in that it allows you to

evaluate a pitcher in a vacuum, without the good fortune or

misfortune of his defensive help getting in the way.



You’ve probably encountered OPS, which is shorthand for

on-base percentage plus slugging percentage. As quick-and-dirty

measures go, OPS is a good one, but OPS+ constitutes an


Like ERA+, OPS+ takes its foundational metric (OPS in this

instance), adjusts it based on park and league conditions and

scales the final result to 100. Also like ERA+, OPS+ is a useful

tool when you want to eyeball production and compare across years

and even eras.

For comparison’s sake … the top career OPS+ is


Ruth, 207. Albert Pujols’ OPS+ in 2009 was 188.



  (Value Over Replacement Player)

Simply put, VORP tells you how many more runs a hitter

produces relative to a replacement-level hitter. This means “runs”

in the theoretical sense — i.e., how he contributes to the

scoring of runs with his homers, doubles, singles, stolen bases,

walks, etc.

VORP also compares hitters to their positional peers. For

instance, shortstop is a more demanding position than third base,

which is a more demanding position than right field. And so on.

When comparing players across positions, you

must take into account positional scarcity. In the

circumscribed universe of Major League Baseball, it’s easy to find

a first baseman who can hit. It’s manifestly difficult to find a

catcher who can hit. VORP reflects this fact.




(Ultimate Zone Rating)

The problem with fielding errors (and, by extension, fielding

percentage) is that they make little account for range. That is,

they reward or penalize fielders for making (or not making) the

routine plays, but they don’t consider the batted balls that were

never reached in the first place. In other words, you can’t make an

error on a ball you never got to, but the ball you never got to

matters greatly.

To cite an extreme hypothetical, you field a soft grounder

straight at you and so does 1985 Ozzie Smith. Then 1985 Ozzie Smith

ranges deep in the hole after a sharp bounder, splays his arms out,

dives as far as he’s able, snags the ball in the last fraction of

webbing and nicks the runner at first. You … barely even lean

in the direction of the ball as it zips past you for a base hit.

According to traditional fielding measures, you and 1985 Ozzie

Smith are equally adept glove men.

On top of that deficiency is the fact that the scoring of

errors is a highly subjective process, and it’s also one as prone

to homerism and “superstar treatment” as anything an NBA ref could

come up with. And that brings us to UZR.

UZR makes note of errors, and it also, by dividing the entire

field up into 64 different fielding “zones,” evaluates a fielder’s

range. It does all this by calculating what percentage of balls hit

into a fielder’s zone are converted into outs and then making a

host of contextual adjustments (e.g., park, ball speed, pitcher

tendencies, etc.). Then that figure is compared with the league

average for the position.

You need at least three years’ worth of UZR data to make

sensible valuations of fielders, but even with those limitations

UZR is substantially better than the numbers usually bandied about

in discussions of defense.



(Defensive Efficiency Rating)

DER is a wonderfully simple statistic (wonderfully simple in

conception, anyway) that gives you a nifty snapshot of team

defense. Simply put, it’s the percentage of balls in play (i.e.,

fair balls that aren’t home runs) a team’s defense turns into outs.

Home parks and other conditions can create some noise in the

numbers, but it’s still measurably better than judging teams based

on error totals or fielding percentage.

Clutch indicators



(Win Probability Added)

This handy little widget tells you how a hitter or pitcher

changes his team’s chances with any given play. In other words,

it’s what a clutch statistic

should be. Unlike flawed stats like game-winning RBI or

saves, WPA accurately gauges how crucial a situation is and then

determines how the player changes the complexion of the game with

his performance at that given moment. It’s expressed as a

percentage. In the case of a positive figure, the player increased

his team’s chances of winning by that much. In the case of a

negative number, he harmed his team’s chances by that much.

For instance, when Bucky Dent homered for the Yankees in the

top of the seventh in the one-game playoff with Boston in 1978, he

increased their chances of winning by 46 percent. Thus, his WPA on

that one swing was 46 percent.

Total player value

WAR (Wins Above Replacement)

Here we have a stat that provides a complete picture of

player value. For hitters, WAR for the most part takes into account

on-base percentage and a modified version of slugging percentage,

with OBP weighted more heavily in the sausage-making process. As

well, WAR uses UZR to evaluate the defensive contributions (or lack

thereof) of those same hitters. The output, then, is an expression

of what a player contributes, in wins, over and above the

aforementioned replacement level, to his team.

WAR also works for pitchers. In their instances, the WAR

calculus includes FIP, the replacement level and home and league


Last season,


target="_blank">Ben Zobrist (!) led all position players with a

WAR of 8.6, and Zack Greinke led all pitchers with a WAR of


So, viva la revolucion and all that.