Quick primer on sabermetrics

July, 2, 2013
7/02/13
12:37
PM ET
Two friends walk into a bar. Friend A looks at the other and says, “Man, Jose Iglesias has been on fire the past few weeks. This guy isn’t slowing down anytime soon.”

Friend B is taken off guard. “I’m actually not too sure how long he can keep this up,” he says. “His BABIP this year is off the charts so he’s bound to regress at some point.”

We’ll continue this riddle later. There’s no doubt that Iglesias has been a tremendous spark for the Red Sox offense since his return from Triple-A. However, when looking at a few less mainstream statistics, there might be a reason for skepticism moving forward.

Statistics like the one mentioned by Friend B are referred to as sabermetrics, a term made popular in the 1990s by famed statistician Bill James. After initially being rejected by the game’s traditionalists, sabermetrics have found a home in Major League Baseball as they account for the variables many of us can’t see before us when watching a baseball game.

If we can’t see it, then how are we supposed to figure out these new-age statistics? What follows are nine examples of valuable sabermetrics that can be figured out a lot easier than you may expect (formulas courtesy of FanGraphs.com).

On-base percentage (OBP)
Leading off for the sabermetric team is the statistic that has become the benchmark for determining a leadoff hitter’s success in their role. Popularized by the book-turned-movie-turned-way of life “Moneyball,” on-base percentage is used to determine how often a hitter gets on base. And as Billy Beane makes clear in “Moneyball,” you can’t score runs unless you have people on base.

Calculated by adding a player’s hits, walks and times hit by pitch and dividing that number by his at bats, walks, hit by pitches and sacrifice flies, on-base percentage accounts for everything a batter can do in the box in order to get on base.

Let’s use Dustin Pedroia as an example. Taking Pedroia’s 104 hits, 44 walks, four sacrifice flies and one hit by pitch in his 323 at bats, we can calculate that Pedroia is currently sporting an OBP of .401, good for fifth in the American League. With a look at the names above him (Detroit’s Miguel Cabrera, Baltimore’s Chris Davis, teammate David Ortiz and Minnesota’s Joe Mauer) you can see why OBP is a strong way of determining a player’s value on offense.
Slugging percentage (SLG)
The second-most used offensive sabermetric, slugging percentage is a way to measure just how much power a hitter is putting on display. Much like total bases, SLG assigns a numerical value to each type of hit (ex. double = two, triple = three; homer = 4) in order to provide an easy-to-digest statistical version of how many bases a hitter is averaging.

In order to figure out slugging, you take the numerical number assigned to each type of hit and multiply it by the number of that type of hit that the player has. Then divide by the number of at bats.

Just how much power is David Ortiz displaying this year? Considering his 41 singles, 17 doubles, two triples and 16 home runs over the span of 240 at bats, Ortiz has posted a SLG of .604, good for third best among AL hitters. Can’t argue with that kind of power production coming from the cleanup spot in the order.

OPS (on-base plus slugging)
OPS is calculated by adding OBP and SLG. It is one the most trusted ways to determine a hitter’s all-around value.

Isolated Power (ISO)
Enough with the basic hitting statistics; it’s time to make our way to one you won’t find on any jumbotron across the league. ISO is used to determine the level of raw power that a player is displaying.

ISO is simple to determine. All you need to do is take a player’s SLG and subtract their batting average from it. With this number, you have measured the number of extra base hits a player averages per at bat.

Along with his high slugging percentage, Ortiz is also among league leaders in ISO with a mark of .288. The typical range for a power hitter is between .240 and .300.

Earned run average (ERA)
You’ve definitely seen this one before. It’s one of the most useful ways to determine a pitcher’s level of success. But did you also know that it’s a sabermetric?

Earned run average is among the most important statistics in baseball, as it measures the number of earned runs a pitcher gives up per nine innings. The pitchers who allow the fewest earned runs are the ones who are considered most dominant as they allow the opposing team the fewest chances to score. Out of the past five AL Cy Young winners, how many led the league in ERA? All five of them.

So let’s use our own early season ace as an example here. So far this year, Clay Buchholz has allowed 16 earned runs in 84 1/3 innings. Take his 16 earned runs and multiply them by the benchmark nine innings that a standard ballgame lasts. Now divide that number by his innings pitched and you get a sparkling ERA of 1.71, tops among qualified starters in the majors.

Walks plus hits per inning pitched (WHIP)
Among the more mainstream of sabermetric pitching statistics, WHIP is a way to determine the average number of baserunners a pitcher allows in an inning. Much like OBP, WHIP takes into account both walks and hits as each of those statistics result in a similar advantage for the offense.

Calculated by adding the number of hits and walks allowed by a pitcher and dividing that sum by their number of innings pitched, WHIP is valuable in determining just how effective a pitcher is against the batters they face. The benchmark for WHIP is often placed at 1.0, with anything lower putting a pitcher among the elite in terms of limiting an offense.

Did you know Pedro Martinez’s 128 hits and 32 walks allowed in 217 innings pitched during his 2000 season for the Red Sox gave him a WHIP of 0.737, good for the best single-season mark of all-time?

Now we move on to some of the more advanced sabermetrics.

Fielding independent pitching (FIP)
There’s a very good chance that you have not heard of this one before. FIP is an advanced sabermetric statistic as it measures what a pitcher’s ERA should really look like in regard to four primary statistics found in any pitcher’s line score: walks, hit by pitches, strikeouts and home runs allowed.

FIP is calculated by multiplying the number of home runs allowed by 13, adding walks and hit by pitches and multiplying the sum by three, then adding these two numbers up and subtracting the number of strikeouts multiplied by two before dividing the total of those calculations by the number of innings pitched.

Here’s the formula:

FIP = ((13 x HR) + (3 x (BB+HBP)) – (2 x K)) / IP

Oh, and don’t forget to add the constant. With FIP meant to represent an alternative to ERA, a constant of 3.20 was derived by sabermetricians in order to put the statistics on a similar scale. This constant is not meant to drastically change; it will in no way interfere with your sabermetric skills.

Unsurprisingly, Buchholz’s numbers add up to give him a FIP of 2.47, second best in the AL. According to a FIP scale organized by FanGraphs, anything lower than 2.90 puts a pitcher among the most elite in their craft.

Batting average on balls in play (BABIP)
Remember that interesting statistic that friend B threw out there about Iglesias? That was BABIP, and it’s among the most effective ways to determine whether a player can maintain the level of success he’s displayed during a season.

Baseball is a fluky sport and a lot of things can either go right or wrong to make a player look a lot better or worse than they actually are. Therefore, BABIP was created to determine the number of batter’s balls put in play that go for hits or the number of balls put in play against a pitcher that go for hits.

For pitchers, BABIP is somewhat dependent on the defense behind them and their ever-present level of luck. Meanwhile, for hitters, BABIP serves as a testament to skill, as it is based on how well the hitter is able to place the ball when putting it in play.

Regardless, the benchmark for BABIP is placed at .300, with anything lower meaning that the pitcher or hitter is bound to improve to the mean soon enough and anything higher meaning that regression is likely on the way.

For hitters and pitchers the calculation of BABIP is the same: take the player’s number of hits / hits allowed and subtract their number of home runs / home runs allowed from that number. Then divide by the number of at bats subtracted by strikeouts and home runs and add the amount of sacrifice flies and you get a player’s BABIP. Like FIP, I’ll recap the formula for clarity:

BABIP = (H – HR) / (AB – K – HR + SF)

Friend B had a point when he was mentioning Iglesias’s BABIP. As spectacular as the young infielder has been, his BABIP is currently at a towering .465, meaning that a massive regression toward his batting average is likely as the season continues. However, you have to give Iglesias credit for managing to build up a BABIP that high, it’s a testament to just how great he’s been at putting the ball in play this season.

Game score
Game score is a system devised by James in order to figure out how particularly well a starter did in a given outing. It’s actually a lot like a fun alternative to scoring a game while you watch. Here’s how it works:

Start with 50 points.

Add a point for each out recorded (three points per inning pitched).

After the fourth inning, add two points per inning pitched.

Add one point for each strikeout.

Subtract two points for each hit allowed.

Subtract four points for each earned run allowed.

Subtract two points for each unearned run allowed.

Subtract one point for each walk allowed.

Easy enough, right? No advanced statistics involved, just some basic math that gives you a different way to determine how effective a certain player is.

At the end of the day that’s all sabermetrics really are. Baseball has been played the same way with the same statistics for centuries, leaving many mathematicians with a desire to figure out new ways to measure productivity within the games linear walls. It’s up to you, the fan, to decide whether to embrace a statistic but the fact of the matter is that scouts and ownership groups have just about all jumped onto the sabermetric train.

In 2003, the Boston Red Sox hired sabermetrician James. He played a role in the formation of the 2004 and 2007 World Series teams, both of which were among the top teams in baseball in terms of OBP.

After last year’s poor season, John Henry noted in an interview with the Boston Herald that “we’ve gotten [James] more involved recently in the central process and that will help greatly.” The 2013 Red Sox are the best team in the American League and lead all of baseball with a team on-base percentage of .349.

By now, the point of my riddle should be obvious. Which friend at the bar are you?

Kyle Brasseur is a contributor to ESPNBoston.com.

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