Sign In
  • Support FanGraphs
    FanGraphs Membership
    Gift a Membership
    Donate to FanGraphs
    FanGraphs Store
  • Games
    Ottoneu Fantasy Baseball
    Signup, FAQ, Blog Posts
  • Blogs
    Blog Roll

    FanGraphs
    Podcasts: FanGraphs Audio | Effectively Wild

    FanGraphs Prospects
    Podcasts: UMP: The Untitled McDongenhagen Project

    RotoGraphs
    Podcasts: The Sleeper and The Bust | Field of Streams

    The Hardball Times
    Podcasts: THT Audio

    Community Research

    Archived Blogs: NotGraphs | TechGraphs | FanGraphs+
    Archived THT: THT Live | Dispatch | Fantasy | ShysterBall
    Archived Podcasts: Stealing Home | Doing It For Bartolo | OttoGraphs
  • Projections
    2021 Pre-Season Projections
    ZiPS, Steamer, Depth Charts
    ATC, THE BAT, THE BAT X
    2021 600 PA / 200 IP Projections
    Steamer600
    2021 Updated In-Season Projections
    ZiPS (RoS), ZiPS (Update)
    Steamer (RoS), Steamer (Update)
    Depth Charts (RoS)
    THE BAT (RoS), THE BAT X (RoS)
    3 Year Projections
    ZiPS 2021, ZiPS 2022
    DFS Projections
    SaberSim
    Auction Calculator
  • Scores
    Today
    Live Scoreboard, Probable Pitchers
    Live Daily Leaderboards
    Win Probability & Box Scores
    2020, 2019, 2018, 2017...
  • Standings
    2020 Projected Standings
    2020 Playoff Odds, Playoff Odds Graphs
    ZiPS Postseason Game-By-Game Odds
    AL East
    AL Central
    AL West
    NL East
    NL Central
    NL West
  • Leaders
    Major League Leaders
    Batting: 2020, 2019, 2018, 2017, Career
    Pitching: 2020, 2019, 2018, 2017, Career
    Splits Leaderboards
    Season Stat Grid
    60-Game Span Leaderboards (Special)

    KBO Leaders
    Batting, Pitching

    Minor League Leaders
    AAA: International, Pacific Coast, Mexican
    AA: Eastern, Southern, Texas
    A+: California, Carolina, Florida State
    A: Midwest, South Atlantic
    A-: New York-Penn, Northwest
    R: Appalachian, Gulf Coast, Pioneer, Arizona
    R: Dominican
    Legacy Minor League Leaderboards

    WAR Tools
    Combined WAR Leaderboards
    WAR Graphs
    WPA Tools
    WPA Inquirer
    Rookie Leaders
    Batters 2020, Pitchers 2020
    Splits Leaders
    Batters: vs L, vs R, Home, Away
    Pitchers: vs L , vs R, Home, Away
  • Teams
    Team Batting Stats
    2020, 2019, 2018, 2017...
    Team Pitching Stats
    2020, 2019, 2018, 2017...
    Team WAR Totals (RoS)
    AL East
    Blue Jays  |  DC
    Orioles  |  DC
    Rays  |  DC
    Red Sox  |  DC
    Yankees  |  DC
    AL Central
    Indians  |  DC
    Royals  |  DC
    Tigers  |  DC
    Twins  |  DC
    White Sox  |  DC
    AL West
    Angels  |  DC
    Astros  |  DC
    Athletics  |  DC
    Mariners  |  DC
    Rangers  |  DC
    NL East
    Braves  |  DC
    Marlins  |  DC
    Mets  |  DC
    Nationals  |  DC
    Phillies  |  DC
    NL Central
    Brewers  |  DC
    Cardinals  |  DC
    Cubs  |  DC
    Pirates  |  DC
    Reds  |  DC
    NL West
    D-backs  |  DC
    Dodgers  |  DC
    Giants  |  DC
    Padres  |  DC
    Rockies  |  DC
    Positional Depth Charts
    Batters: C, 1B, 2B, SS, 3B, LF, CF, RF, DH
    Pitchers: SP, RP
  • RosterResource
    Offseason Tools
    2021 Offseason Tracker
    2021 Free Agent Tracker
    2021 Injury Report
    Current Depth Charts
    AL East
    Blue Jays
    Orioles
    Rays
    Red Sox
    Yankees
    AL Central
    Indians
    Royals
    Tigers
    Twins
    White Sox
    AL West
    Angels
    Astros
    Athletics
    Mariners
    Rangers
    NL East
    Braves
    Marlins
    Mets
    Nationals
    Phillies
    NL Central
    Brewers
    Cardinals
    Cubs
    Pirates
    Reds
    NL West
    D-backs
    Dodgers
    Giants
    Padres
    Rockies
    2020 Opening Day Tracker
  • Prospects
    Prospects Home
    THE BOARD!
    THE BOARD: Scouting + Stats!
    Top Prospects List
    Top Prospects
    20212020
    AL
    BALCHWHOU
    BOSCLELAA
    NYYDETOAK
    TBRKCRSEA
    TORMINTEX
    NL
    ATLCHCARI
    MIACINCOL
    NYMMILLAD
    PHIPITSDP
    WSNSTLSFG
    AL
    BALCHWHOU
    BOSCLELAA
    NYYDETOAK
    TBRKCRSEA
    TORMINTEX
    NL
    ATLCHCARI
    MIACINCOL
    NYMMILLAD
    PHIPITSDP
    WSNSTLSFG

    • 2020 Preseason Top 100

  • Glossary
    Library
    Batting Stats
    wOBA, wRC+, ISO, K% & BB%, more...
    Pitching Stats
    FIP, xFIP, BABIP, K/9 & BB/9, more...
    Defensive Stats
    UZR Primer, DRS, FSR, TZ & TZL, more...
    More
    WAR, UBR Primer, WPA, LI, Clutch
    Guts!
    Seasonal Constants
    Park Factors
    Park Factors by Handedness
  • Sign In
Help Support FanGraphs


Become a Member No Thanks
Already a member? Log In
  • Intro
  • Features
  • Offense
    • Complete List (Offense)
    • OBP
    • OPS and OPS+
    • wOBA
    • wRC and wRC+
    • wRAA
    • Off
    • BsR
    • UBR
    • wSB
    • wGDP
    • BABIP
    • ISO
    • HR/FB
    • Spd
    • Pull%/Cent%/Oppo%
    • Soft%/Med%/Hard%
    • GB%, LD%, FB%
    • K% and BB%
    • Plate Discipline (O-Swing%, Z-Swing%, etc.)
    • Pitch Type Linear Weights
    • Pace
  • Defense
    • Overview
    • Def
    • UZR
    • DRS
    • Defensive Runs Saved – 2020 Update
    • Inside Edge Fielding
    • Catcher Defense
    • FSR
    • RZR
    • TZ / TZL
  • Pitching
    • Complete List (Pitching)
    • ERA
    • WHIP
    • FIP
    • xFIP
    • SIERA
    • Strikeout and Walk Rates
    • Pull%/Cent%/Oppo%
    • Soft%/Med%/Hard%
    • GB%, LD%, FB%
    • BABIP
    • HR/FB
    • LOB%
    • Pitch Type Linear Weights
    • SD / MD
    • ERA- / FIP- / xFIP-
    • Plate Discipline (O-Swing%, Z-Swing%, etc.)
    • Pace
    • PITCHF/x
      • What is PITCHF/x?
      • Pitch Type Abbreviations & Classifications
      • Heat Maps
      • Common Mistakes
      • PITCHf/x Resources
  • WE/RE/LI
    • RE24
    • Win Expectancy
    • WPA
    • LI
    • WPA/LI
    • Clutch
  • Principles
    • DIPS
    • Regression toward the Mean
    • Replacement Level
    • Sample Size
    • Splits
    • Projection Systems
    • Linear Weights
    • Counting vs. Rate Statistics
    • Park Factors
    • Park Factors – 5 Year Regressed
    • Positional Adjustment
    • Aging Curve
    • League Equivalencies
    • Pythagorean Win-Loss
    • Luck
  • WAR
    • What is WAR?
    • WAR for Position Players
    • WAR for Pitchers
    • FDP
    • fWAR, rWAR, and WARP
    • WAR Misconceptions
  • Business

BABIP

by Steve Slowinski
February 16, 2010

BABIP Flash Card 8-9-15

Batting Average on Balls In Play (BABIP) measures how often a ball in play goes for a hit. A ball is “in play” when the plate appearance ends in something other than a strikeout, walk, hit batter, catcher’s interference, sacrifice bunt, or home run. In other words, the batter put the ball in play and it didn’t clear the outfield fence. Typically around 30% of all balls in play fall for hits, but there are several variables that can affect BABIP rates for individual players, such as defense, luck, and talent level. Hitters have more control over their BABIP than pitchers do and that lack of control for pitchers has lead to the creation of Defense Independent Pitching Statistics (DIPS).

BABIP is one of the simplest and more important sabermetric statistics, but it is also one of the most misunderstood. Understanding the factors that lead to a higher or lower BABIP is important for analyzing player performance and knowledge about the principle itself will lead you to a more nuanced appreciation of the game.

Calculation:

The BABIP equation is:

BABIP = (H – HR)/(AB – K – HR + SF)

This equation is the same for each season and league, so it is quite easy to calculate. The numerator is the number of hits minus the number of home runs and the denominator is at bats minus strikeouts and home runs with sacrifice flies added back in.

Note: You may notice if you use this formula it may not match exactly what is listed on the site for pitchers, or you might see BABIP values for pitchers that are different than what you find at Baseball-Reference. This is because our database does not remove sacrifice bunts from the denominator. This is a data problem on our end and not a disagreement about the proper methodology.

Why BABIP:

BABIP is important because the frequency with which a player gets a hit on a ball in play or allows a hit on a ball in play is very telling. Three main factors influence BABIP and all three of those factors tell us something important about that player’s overall stat line. Those factors are defense, luck, and talent level.

a) Defense – For instance, imagine a player cracks a hard line drive down the third base line. If an elite fielder is playing at third, they may make a play on it and throw the runner out. However, if there’s a dud over there with limited range, the ball could just as easily fly by for a hit. Players have no control over the defenses they’re facing, and they can only direct their hits to a limited extent. Sometimes a batter makes good contact, but simply hits the ball right at a fielder. Also, a batter that consistently hits into a shift may have a lower BABIP than a typical player. The inverse is true for pitchers. If you have an exceptional defense behind you, it is likely that you will allow fewer hits than if you have a poor defense behind you even if you throw the exact same pitches to the exact same hitters.

b) Luck – Bloop hits fall in. A batter may turn a nasty pitch into a dribbler that just sneaks past the first baseman even though the hitter barely got a piece of it. On the other hand, a well hit ball may go right to where a fielder is standing even though the pitch was grooved and the batter struck it at a very high velocity. Hits can fall in despite the best pitches and the best defenses due to simple luck. Batters and pitchers do not have complete control over where a ball lands so even high quality contact can turn into outs and low quality contact can turn into hits. In the long run, this will even out but it takes a pretty significant sample of balls in play to do so.

c) Talent Level – The harder a ball is hit, the more likely it is to fall in for a hit so a better hitter will usually have a higher BABIP than a worse hitter and a worse pitcher will usually have a slightly higher BABIP than a better pitcher given a sufficient sample size. A good hitter might be able to register a hit on 35% of their balls in play with consistency, but BABIP fluctuates quite a bit based on defense and luck so using it to capture true talent can be tricky even if true talent does influence the number.

Defense, luck, and talent all feed into the final BABIP number which is useful in different ways for batters and pitchers. For batters, BABIP can be used as an indication about the batter’s overall quality of contact if you have a large enough sample of balls in play. Over three seasons, if a batter has a .345 BABIP, it is probably safe to say that batter is above average in this aspect of the game and is probably making better contact on average than most.

However, changes in BABIP are to be met with caution. If a batter has consistently produced a .310 BABIP and all of a sudden starts a season with a .370 BABIP, you can likely identify this as an instance in which the batter has been lucky unless there has been a significant change in their style of play.

For hitters, we use BABIP as a sanity test of sorts that tells us if their overall batting line is sustainable or not. Virtually no hitter is capable of producing a BABIP of .380 or higher on a regular basis and anything in the .230 range is also very atypical for a major league hitter. In other words, BABIP allows us to see if a hitter seems to be getting a boost from poor defense or good luck or getting docked for facing good defenses and having bad luck.

A hitter has control over how often they put the ball in play and how hard they hit the ball, but due to the unpredictable nature of luck and defense, their BABIP may not be a perfect reflection of their performance to date and it is easier to observe this fluctuation when looking at BABIP compared to wOBA, OBP, or SLG for example.

BABIP is likely even more important when evaluating pitchers because they have almost no control over what happens to a ball once it is put in play. A pitcher can control their strikeouts, walks, and home runs, and through those, the number of balls they allow to be put into play, but once the ball leaves the bat, it’s out of their hands. As a result, pitcher BABIP is heavily influenced by defense and luck, which means the number of hits a pitcher gives up is influenced by things outside of their control. And if hits are somewhat outside of a pitcher’s control, so will their runs allowed totals.

This is a long way of saying that pitchers with a high BABIP are most likely victims of poor defense or bad luck, and neither is the pitcher’s fault. Their defense might be attached to them, but their luck is not, meaning that we typically expect most pitchers with extreme BABIP values to regress toward league average going forward.

This is not to say that pitchers have no control over the quality of contact against them, but research has shown that they have very limited control over whether a ball that is put into play becomes a hit.

Due to this flakiness, BABIP can dramatically affect a hitter’s batting average or a pitcher’s batting average against even if their true performance is unchanged. If a large number of balls in play go for hits, that can boost their batting average significantly. Similarly, if a large number of balls in play get caught, it can reduce the total number of hits.

When we evaluate players we want to do our best to isolate their individual performance and BABIP can help point us in that direction. If a hitter has a .420 BABIP, it is very unlikely that they are actually making dramatically better contact than everyone else in the league, but instead are making very good contact with some good fortune mixed in. For pitchers, the opposite is true. If a pitcher is preventing runs at a much better rate than ever before with a .190 BABIP, it is likely that we can uncover quality defensive play and good luck.

Neither instance invalidates the performance to date, but BABIP is a tool that can allow us to better isolate which factors are driving certain outcomes.

How To Use BABIP:

Most people who are familiar with BABIP have a pretty good idea about why it’s important, but using it responsibly and properly is much more challenging. We know that league average BABIP is almost always right around .300, so many people look at a player’s BABIP and if it is significantly different from .300 they assume that player is either very lucky or very unlucky. This is not always the appropriate way to think about BABIP.

For hitters, you typically want to adjust your expectations toward that player’s career average rather than league average. Batters have much more control over their BABIP than pitchers do, which is another way of saying that a higher percentage of batter BABIP is controlled by actual talent levels. It’s certainly possible for hitters to improve their offensive game and raise their BABIP, but short, dramatic spikes are usually due to luck.

If a hitter has a .320 career BABIP and all of a sudden has a .260 BABIP over the first month of the season, you shouldn’t just expect them to regress to .300 or stay at .260. In fact, they are probably more likely to have a .320 BABIP going forward. Hitters who consistently hit above or below .300 for their BABIP are not simply getting lucky, they are actually leveraging a skill which needs to be accounted for when analyzing their performance.

For pitchers, the same basic principle applies except for the fact that it takes longer for BABIP to become predictive for pitchers than it does for hitters. In other words, if you can get a sense of a hitter’s true talent BABIP after about 800 balls in play, it might take more like 2,000 balls in play to get a sense of what a pitcher’s true talent BABIP truly is. For this reason, we’re more inclined to expect a pitcher’s BABIP to look more like league average in the future than whatever number they might have for the current season because pitcher BABIP over the course of one season has little predictive power, and if it has little predictive power, it is likely not a matter of skill.

This is not to say that some pitchers can’t control their BABIP. Clayton Kershaw, for example, typically has a lower than average BABIP because he’s a fly ball pitcher (fly balls fall for hits less often) with a high strikeout rate. He has a long history of limiting opposing BABIP, but most pitchers’ year to year BABIP don’t tell you much about their future BABIP.

The best advice is to expect batters to BABIP close to their career average and for pitchers to gravitate toward league average, but very large samples can move the needle for pitchers. It is not right to observe that a high BABIP or low BABIP is simply due to luck even if luck plays a role. Luck influences short term changes in BABIP that can impact a player’s stat line, but not every player should be expected to approach league average BABIP.

Context:

The average BABIP for hitters is around .300. If you see any player that deviates from this average to an extreme, they’re likely due for regression, but the best hitters in the league are capable of sporting BABIPs in the .350 range while the worst hitters might hang around .260. Research indicates that you need about 800 balls in play before a hitter’s BABIP “stabilizes.” In reality, there is no magic threshold at which one’s BABIP becomes predictive of future BABIP, but about two seasons worth of data will give you a decent indication of true talent.

The average BABIP for pitchers is also about .300, but their ability to sustain high or low BABIPs is much more limited. Their BABIPs will vary season to season, but in the long run you won’t see many pitchers outside of the .290 to .310 BABIP range. Research indicates that you need about 2,000 balls in play before a pitcher’s BABIP “stabilizes.” Again, there is no magic threshold at which one’s BABIP becomes predictive of future BABIP, but you need about three full seasons of data for starting pitchers before you can start to make any conclusions about a pitcher’s true talent BABIP.

Things to Remember:

● Saying a player will “regress” is a tricky statistical subject that confuses many people.  See our section on regression for more info.

● Line drives go for hits more often than groundballs, and groundballs go for hits more often than flyballs. This means that a pitcher or batter with a specific batted ball profile might be prone to higher or lower BABIPs.

● A high or low BABIP is not necessarily a sign of luck, but a BABIP that is substantially different from one’s career mark usually is.

● BABIP requires a large sample before it “stabilizes,” meaning that you can’t say a player has established a new talent level without a significant sample size.

● The long-run ceiling on a player’s BABIP is about .380, as no player with more than 4,000 career PA has ever had a career BABIP higher than that, but .350 is a more realistic mark for the very best hitters in the league.

Links for Further Reading:

Intro to BABIP – Big League Stew

BABIP: What Do We Know? – Beyond the Boxscore

Batting Average on Balls in Play – Wikipedia

BABIP Research – Hardball Times

Luck Dragons BABIP – YouTube

Why We Care About BABIP – FanGraphs





Regression toward the Mean
 
WPA

Steve is the editor-in-chief of DRaysBay and the keeper of the FanGraphs Library. You can follow him on Twitter at @steveslow.

1
Leave a Reply

Please Login to comment
1 Comment threads
0 Thread replies
0 Followers
 
Most reacted comment
Hottest comment thread
1 Comment authors
John Recent comment authors
newest oldest most voted
John
Guest
John
You can flag a comment by clicking its flag icon. Website admin will know that you reported it. Admins may or may not choose to remove the comment or block the author. And please don't worry, your report will be anonymous.

comment

Vote Up5Vote Down 
8 years ago
You are going to send email to

Move Comment

Updated: Sunday, January 17, 2021 5:23 AM ETUpdated: 1/17/2021 5:23 AM ET
Player Linker - Contact Us - Advertise - Terms of Service - Privacy Policy
bis logo
All major league baseball data including pitch type, velocity, batted ball location, and play-by-play data provided by Baseball Info Solutions.
mlb logo
Major League and Minor League Baseball data provided by Major League Baseball.
Mitchel Lichtman
All UZR (ultimate zone rating) calculations are provided courtesy of Mitchel Lichtman.
TangoTiger.com
All Win Expectancy, Leverage Index, Run Expectancy, and Fans Scouting Report data licenced from TangoTiger.com
Retrosheet.org
Play-by-play data prior to 2002 was obtained free of charge from and is copyrighted by Retrosheet.