Sabermetric and Advanced Pitching Statistics: FIP, xFIP, BABIP and HR/FB

With the motion picture Moneyball set to hit theaters this weekend, appropriately, is adding a “Sabermetric Statistics” section to its Scouting Encyclopedia series. Eight years after Michael Lewis’ book hit store shelves, Sabermetric statistics have become a regular part of the game, with OPS and OBP now a commonality on statistical reference sites like However, with the emergence of Saber, has arrived a slew of new more complex baseball statistics like WAR, FIP and wOBP, leaving many confused.

Though these new sabermetric statistics won’t ever best a good old fashioned in-person approach to scouting, they provide a solid analytical supplement and are another invaluable tool in forecasting a players’ future production. As such, anyone who reads’s (great) scouting reports will need to be familiar with these new measurements and forecasting methods. Happy reading!

Batting Average on Balls in Play (BABIP)

BABIP = (H – HR) / (AB – K – HR + SacFly)

BABIP is the percentage of plate appearances ending with balls put in play that are credited as a hits. BABIP excludes home runs and is only a measure of how often a ball put (hit) in-play (not foul and inside of the park) allows a batter to safely reach base without allowing an out.

BABIP is determined by pitching’ skill, a batting skill, fielding skill and luck. A pitcher that allows a high BABIP (X >.300) is either falling victim to: (1) poorly-skilled fielders playing behind him, (2) bad luck, (3) his facing particularly good hitters, (4) his own lack of skill or (5) any combination of the (previous) four.

Line drives and hard-hit balls will more likely result in hits than groundballs, pop-ups and weakly hit balls; However, luck can place a pop-up– that would usually result in an out– between fielders. Poor fielders, like slow-footed Adam Dunn for example, will more often allow balls in play to result in hits than good fielders like Brett Gardner. Therefore, starting pitchers that play for the Chicago White Sox– the club that Adam Dunn plays for- will be more likely to give up more hits on balls in play than those pitching for a team that employs fielders as skilled as Brett Gardner.

League Average BABIP (for pitchers) is generally between .290 and .300. A BABIP higher or lower than .300 can be expected to regress toward league average over time (as the sample gets larger).

Groundball and sinkerball pitchers will normally post a lower BABIP on groundballs than other pitchers; though they will also post a higher BABIP on fly-balls than other pitchers. Knuckleball pitchers will tend to post lower BABIP than other pitchers.

Trevor Cahill, the young Oakland Athletics pitcher with a top-tier sinking fastball, has managed a .270 BABIP for his career. Cahill gets plenty of groundballs (56% GB%), and he’s very reliant on his defense. When the Athletics’ defense was ranked atop the American League, Cahill’s BABIP was .236 (last season). Though, when the Athletics defense deteriorated this past year, Cahill’s BABIP ballooned over sixty points to .301 despite his maintaining the same groundball rate (56%), home run rate (.88 HR/9) and a similar K/BB (1.74 vs. 1.87). Cahill’s ERA rose from 2.97 in 2011 to 4.31 this past season, illustrating the effect BABIP- and his fielder’s defensive ability– has on a groundball pitcher’s performance.

Though “pitching to contact” seems to be a mythical approach, pitchers who keep their pitches in difficult-to-hit portions of the strikezone and have more late-movement on their pitches will tend to post a lower BABIP. Knuckleballer Tim Wakefield for instance, a pitcher who throws his pitches with highly unpredictable break, has managed to maintain a .274 BABIP for his career.

BABIP is a red flag statistic. It is best used to spot unsustainable performance; A pitcher with a much higher/lower ERA than league average and a much higher/lower BABIP will generally see his numbers regress toward league average as his BABIP moves back towards .300. Pitchers that consistently allow a far higher BABIP than league average won’t pitch in the MLB or professionally for a long period of time.

Fielder-Independent PItching (FIP)

FIP = (13*HR + 3*BB – 2*K) / IP + 3.20

Popularized by, fielder-independent pitching is the most widely used defense-independent pitching statistic. An attempt at isolating a pitcher’s real contribution to his team, FIP measures a pitcher’s performance by focusing on the facets of a baseball game that a pitcher can control– walks allowed (BB), strikeouts (K), homeruns (HR). Unlike WHIP and ERA, FIP doesn’t include hits allowed. Closely related to fielding and coaching prowess, hits are an imperfect measure of a pitcher’s effectiveness and have a weak correlation with true pitching performance.

The FIP equation includes a constant (either 3.10 or 3.20), used to put FIP on to a recognizable ERA scale.

FIP is the most effective statistic at forecasting a pitcher’s future performance and more accurate than earned run average. However, smaller sample sizes (using FIP from game to game) proves FIP less effective.

Expected Fielder-Independent Pitching (xFIP)

xFIP = [13(.106*Fly Balls) + 3*BB - 2*K] / IP + 3.20

Developed by Dave Studeman at The Hardball Times, xFIP keeps homeruns constant at a league average rate (10.6% HR/FB) for every pitcher. The result is a more stable statistic and a more accurate predictor of future ERA in the short and long term than FIP– particularly for pitchers who change teams and leagues more than others.

xFIP is on a slightly higher scale than ERA and FIP. Therefore, a xFIP of 3.20 is more impressive than a FIP or ERA of 3.20.

Groundball pitchers, like Brandon Webb and Rick Porcello, have higher HR/FB rates than fly-ball and strikeout pitchers, and therefore xFIP is a less accurate predictor of their future performance.

For pitchers who consistently outperform (or underperform) league-average HR/FB rates, like Tim Lincecum for instance (8.8% career, above 9% in 1 season), xFIP is a less accurate predictor of their future performance.

xFIP has the highest correlation (determined by root means square error) with future ERA of all pitching metrics.

Home Run Rate (HR/FB)

HR / FB = (Home Runs Allowed / Fly Balls Allowed) * 100%

A volatile and inaccurate predictor of future performance. Because elements like weather, different park sizes and Torii Hunter (a notorious home run thief), home run/fly ball rates aren’t closely tied to a pitchers’ skill. For instance, starting pitchers who spend half of their starts in big parks (like Petco Park) will tend to have lower HR/FB rates than those who pitch in small parks (Wrigley Field). Pitchers who spend their careers in Arizona– a climate with dry air and therefor less air resistance to fly balls– will have lower home run rates than those who pitch in the cold, dank climate of the Pacific Northwest (Seattle Mariners).

HR/FB ratio is still a useful metric however. If a pitcher had a particularly inflated ERA or posted numbers inconsistent to his career line, looking at HR/FB ratio will give incite into their future production. For instance, a pitcher who have up an abnormally high HR/FB ratio one year is likely to perform better in the future as his home run rate regresses back toward the league rate.

Pitchers can reduce the amount of home runs they allow by either increasing their groundball rate and lowering their flyball rate, or by reducing their HR/FB ratio.

Groundball pitchers and sinkerballers will normally post higher HR/FB ratios than flyball and strikeout pitchers.


Fangraphs Sabermetrics Library

The Hardball Times Glossary

THT Expected BABIP Calculator Scouting Encyclopedia