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| Fielding Independent Pitching | |
|---|---|
| Name | Fielding Independent Pitching |
| Abbreviation | FIP |
| Type | pitching metric |
| Introduced | 2000s |
| Creator | Voros McCracken |
| Used by | Major League Baseball, front offices, sabermetricians |
Fielding Independent Pitching Fielding Independent Pitching estimates a pitcher's effectiveness by isolating outcomes attributable to the pitcher from those influenced by defense and ballpark effects, focusing on Strikeout, walk, Hit by pitch, and Home run events to predict runs. Developed within the sabermetrics community, it is used by teams such as the Boston Red Sox, New York Yankees, Los Angeles Dodgers, and organizations including Major League Baseball and the Society for American Baseball Research. FIP is often compared to traditional measures like Earned run average and modern metrics such as Expected runs (xR) and Wins Above Replacement.
Fielding Independent Pitching is defined mathematically to convert pitcher-controlled outcomes—Strikeouts, walks, Hit by pitchs, and Home runs—into a run-equivalent scale similar to Earned run average. It attributes responsibility for sequencing and defensive plays to entities such as Umpires, Infielders, Outfielders, and catchers, thereby attempting to measure the pitcher's standalone contribution. The metric is applied across leagues like Major League Baseball, Nippon Professional Baseball, and Korean Baseball Organization to compare pitchers such as Jacob deGrom, Max Scherzer, Clayton Kershaw, Pedro Martínez, and Sandy Koufax on a standardized basis.
The baseline FIP formula is typically expressed as: FIP = (13*HR + 3*(BB + HBP) - 2*K)/IP + constant, where HBP denotes Hit by pitch and IP denotes innings pitched. Variants include versions with adjusted multipliers, park-adjusted FIP used by Tampa Bay Rays analysts, and skills-based measures like SIERA developed by researchers at institutions including Baseball Prospectus and FanGraphs. Extensions incorporate inputs used by DRS and UZR proponents to create hybrid metrics such as xFIP, which replaces actual Home run rate with an expected value based on Fly ball rates, and FIP- which normalizes to league average akin to ERA+ used by the Baseball Hall of Fame voting community.
FIP traces to early 2000s research by Voros McCracken, whose analyses challenged prevailing wisdom held by figures like Bill James and inspired adoption by analytics teams in franchises including the Oakland Athletics, Cleveland Guardians, and Houston Astros. Influential platforms such as Baseball Prospectus, FanGraphs, and publications by The New York Times and ESPN helped diffuse FIP into mainstream coverage alongside pioneers like Billy Beane in the Moneyball era. Academic contributions from researchers connected to MIT, Cornell University, and University of Chicago economics and statistics departments refined the statistic, while sabermetric conferences like the SABR Analytics Conference facilitated cross-team adoption.
Analysts use FIP to evaluate components of pitcher skill such as Pitch velocity and Pitch movement, and to forecast future performance for pitchers like Gerrit Cole or Stephen Strasburg. Front offices integrate FIP into projection systems alongside models from Steamer and ZiPS to estimate contract values and arbitration cases judged by panels including representatives from Major League Baseball Players Association. Media outlets and broadcasters for teams like the Chicago Cubs and St. Louis Cardinals present FIP alongside WHIP and K/9 to contextualize results, while researchers correlate FIP with metrics from Statcast such as Exit velocity and barrel rates to improve predictive power.
Critics note that FIP omits batted-ball context provided by Statcast and ignores elements influenced by Catcher framing and defensive positioning as used by teams like the New York Mets and San Francisco Giants. Analysts from outlets including The Athletic and academics at Harvard University and Stanford University highlight sensitivity to home run variance and reliever usage patterns exploited by managers such as Joe Maddon and Tony La Russa. Debates persist in sabermetrics forums, including SABR committees and Baseball Prospectus articles, over whether FIP undervalues pitchers who induce weak contact through pitch sequencing used by staff in franchises like the Milwaukee Brewers.
Compared to Earned run average, FIP focuses on skills rather than run outcomes; compared to SIERA, it uses fewer batted-ball inputs. Metrics like xERA and Expected Fielding Independent Pitching (xFIP) extend FIP by incorporating expected home run rates or batted-ball predictors used in models such as Statcast’s expected statistics. WAR calculations in systems from Baseball-Reference and Fangraphs integrate FIP-derived components when estimating pitcher value against alternatives like RA9-WAR. Teams and analysts weigh FIP against defensive metrics such as Defensive Runs Saved and Ultimate Zone Rating when constructing roster decisions.
Scouts and front offices use FIP to identify pitchers with sustainable skills, influencing trade decisions involving players from organizations like the Toronto Blue Jays, Texas Rangers, Philadelphia Phillies, and international signings from Cuban National Series. Managers apply FIP in bullpen construction strategies, matchup planning against hitters like Mike Trout or Mookie Betts, and bullpen deployment in games like the World Series and All-Star Game. Player development staffs in minor league systems operated by clubs including the Seattle Mariners and Atlanta Braves combine FIP with pitch-tracking data from TrackMan and PITCHf/x to refine mechanics and pitch selection for prospects such as those highlighted in Baseball America.
Category:Baseball statistics