Understanding Sabermetrics in Baseball: What It All Means
A casual fan's guide to Sabermetric terms
By: Athlon Sports | 3/6/13, 11:20 AM EST
The world of advanced statistics can be intimidating for the casual baseball fan. The acronyms can be confusing. The numbers lack meaning. Fans understand a .300 batting average or a 2.50 ERA or 50 saves. More esoteric are the meanings behind numbers like a .900 OPS, a 3.00 FIP or 10 wins above replacement. Once you understand the logic behind the statistics, it’s easy to see why they’re helpful in understanding the game. Here’s a guide to some of the most commonly used advanced metrics, and why they’re useful.
What: Wins Above Replacement, a catch-all metric designed to quantify a player’s overall contribution to his team’s win total. The statistic measures offense, defense and baserunning for position players. There are two prominent versions: One from FanGraphs.com, the other from Baseball-Reference.com. Each uses a separate formula.
Why: Let’s get this out of the way. Few sabermetrically inclined writers view WAR as the end-all, be-all of statistics. It’s used as the start to a conversation, not the end of it. WAR operates as a tool to add up all the disparate things a player does on the field. It also adds value based on the defensive spectrum, recognizing that positions like center field and shortstop are more difficult to play than first base or a corner outfield spot.
Example: The reason Mike Trout finished 2012 with 10 WAR, according to Fangraphs, and Miguel Cabrera finished with 7.1 WAR, is simple. Trout plays much better defense. He runs the bases much better. And their offense was also comparable, considering that Trout plays his home games in an extreme pitchers’ park, while Cabrera plays in a more neutral park.
What: This statistic is a very simple way to measure a batter’s offensive output. It stands for On-Base Plus Slugging Percentage, and it means what it says. You add up a player’s OBP and his slugging.
Why: Because there are more effective ways to measure a player’s offensive output than just batting average. OPS paints a rudimentary picture of a player’s season: How often did he get on base? How many bases did he accumulate with each at-bat?
Example: Jose Reyes led the National League in 2011 with a .337 batting average. Ryan Braun finished second with a .332 batting average. Yet Braun was the far more accomplished hitter that season. His OPS was .994, the best in the National League and third-best in baseball. Reyes’ OPS was .877, the 26th-best in baseball.
What: Weighted On-Base Average attempts to add some nuance to OPS further, as a way to calculate a player’s overall offensive value. The numbers read like batting average: A .400 mark is considered excellent. A .300 mark is considered poor.
Why: OPS treats on-base percentage and slugging percentage as equals. They are not. Getting on base is considered a bit more valuable. wOBA reflects that. It takes the basic picture created by OPS and refines the number, placing added emphasis on the game’s most critical skill: Getting on base.
Example: Joey Votto may be the premier on-base machine in baseball. Since 2010, only Miguel Cabrera rates higher in wOBA (.428 to .425). Cabrera also has a 1.025 OPS to Votto’s .998 OPS. Votto makes up the difference with a .434 OBP, compared to Cabrera’s .420.
What: Batting Average on Balls in Play records just that: How often a player gets credited with a hit when he puts the ball in play.
Why: Because there’s so much luck involved once a batter makes contact. He can sting a liner right at an outfielder. Or he can bloop a broken-bat double. During the course of the season, BABIP helps measure how much a player is affected by luck or defense. The average mark settles in around .300, with higher marks expected for speed-base players.
Example: In 2008, Nick Swisher muddled through the weakest season of his career. He hit 24 homers, but still finished with a middling .743 OPS. Yet during the next four seasons, his OPS jumped back to an average of .850. The best explanation for his trying 2008 year resides in his .249 BABIP, a mark more than 50 points below his career average (.303). Once his luck evened back out, Swisher went back to being a solid corner outfielder.
What: ISO measures true power. To calculate this, subtract a player’s batting average from his slugging percentage. A .200 ISO is considered very strong.
Why: This is a simple way to measure a player’s ability to accumulate extra-base hits. Sometimes slugging percentage can be deceiving. ISO helps provide more information about the batter’s season: Is the slugging percentage a result of good BABIP luck (and a high batting average) or a series of extra-base hits?
Example: Since 2010, Jose Bautista leads all of baseball with a freakish .322 ISO. To put that in context: Babe Ruth’s ISO was .348. So even though Bautista batted just .271 during that time period, with a mediocre .256 BABIP, when he made contact, he did serious damage.
What: Ultimate Zone Rating is probably the most popular defensive metric. The methodology is difficult to explain, but in essence, the statistic measures how many runs a defender prevents (or allows) based on range, ability to avoid errors, arm and ability to turn double plays.
Why: There’s so much information available about offense — and comparatively so little about defense. UZR is a start. These numbers can be fickle, especially in a small sample size. But with several years of data, you get a sense of how a player handles his position.
Example: From 2009-11, David Wright was one of the worst third basemen in the majors. He allowed about 10 runs more than the average defender. But an offseason adjustment in the winter of 2012 — a new emphasis on positioning his feet and using his whole body when throwing across the diamond — led to a remarkable change. In 2012, he was worth 15.4 more runs in the field than the average defender.
What: Fielding Independent Pitching measures ERA by removing batted-ball luck from the equation. In other words, pitchers are judged on the three things they specifically can control: Strikeouts, walks and home runs.
Why: This statistic can help predict future success — or future struggles — with a bit more nuance than ERA. In general, it is believed a pitcher cannot control what happens once a hitter makes contact. There’s so much variance involved, as we explained with BABIP. The defense might be terrible. The pitcher’s luck might be poor. FIP measures performance if all things were considered equal.
Example: James Shields had terrible luck in 2010, despite a solid 3.67 strikeout-to-walk ratio. His BABIP against was a career-high .344 and more homer-prone than ever. So while his ERA was 5.18, his FIP was a more reasonable 4.24. In the past two seasons, as his luck evened out and his strikeout-to-walk ratio remained about the same, Shields’ ERA slipped back down to a cumulative 3.15.
What: SIERA takes FIP one step further. It stands for Skill-Interactive ERA, and it adds some batted-ball results into the equation. SIERA rewards pitchers for ground balls and pop-ups (because those are tougher to turn into extra-base hits).
Why: Pitching is not simple. FIP treats it as such — which is useful for predicting what might happen in the coming years. SIERA tries to crack through the complexity of the craft by measuring batted-ball results.
Example: Cliff Lee leads the majors in SIERA from 2010-12 with a 2.93 mark. He hits all the checkmarks: He strikes out a ton of batters (24.1 percent of the hitters he faces). He doesn’t walk anyone (3.4 percent). He gets a good deal of grounders (44.4 percent) and infield pop-ups (11 percent).
—By Andy McCullough
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