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A pitcher's average on batted balls ending a plate appearance, excluding home runs. Based on the research of Voros McCracken and others, BABIP is mostly a function of a pitcher's defense and luck, rather than persistent skill. Thus, pitchers with abnormally high or low BABIPs are good bets to see their performances regress to the mean. The league average for modern pitcher BABIP is around .300.
Hitter BABIP is much more of a skill, based on how well they are able to hit and place the ball, along with their speed.
BABIP = (H - HR) / (AB - K - HR + SF + SH)
Refers to the Baserunner State combined with the number of
outs in the current half inning, used to calculate Win Expectancy. For example, '2-103' indicates two outs with runners on first and third.
Def Eff, or Defensive Efficiency, is the rate at which balls put into play are converted into outs by a team's defense. Def Eff can be approximated with (1 - BABIP), if all you have is BABIP, but a team's actual Def Eff is computed with
1 - ( H - HR ) / ( AB - SO - HR + SH + SF )
the Team Audit Standings use the latter formula.
Alternately, it will be seen some places (including some past editions of Baseball Prospectus) computed as 1 - ((H + ROE - HR) / (PA - BB - SO - HBP - HR)), which comes out slightly lower in most instances.
Here is an example of the Defensive Efficiency spectrum based on the 2011 season:
Excellent - Tampa Bay .735
Great - Texas .722
Average - Toronto .710
Poor - Pittsburgh .700
Horrendous - Minnesota .693
FIP is a component ERA inspired by the work of Voros McCracken on defense-indepdendent pitching statistics, but has become more widely used because of the ease of computation - it requires only four easily-found box score stats, uses only basic arithmetic operations and has four easily-memorized constants. It was conceived of by both Tom Tango and Clay Dreslough, the latter of who called it Defense-Independent Component ERA.
At Prospectus, we are including hit batters in the walks term. The constant we use is both league and season specific - in other words, a pitcher in the American League will have a different FIP constant than a pitcher in the National League. This differs from the presentation of FIP on sites such as Fangraphs, which use one constant for both leagues in each season.
Here is an example of the Fielding Independent Pitching spectrum based on the 2011 season:
Excellent - Roy Halladay 2.17
Great - David Price 3.36
Average - Tim Stauffer 4.00
Poor - Carlos Zambrano 4.56
Horrendous - Bronson Arroyo 5.68
Offensive Winning Percentage. A Bill James stat, usually derived from runs created. In EqA terms, it could be calculated as (EQA/refEQA)^5, where refEQA is some reference EQA, such as league average (always .260) or the position-averaged EQA.
For Pythagenpat, the exponent X = ((rs + ra)/g)^.285. Although there is some wiggle room for disagreement in the exponent, that equation is simpler, more elegant, and gets the better answer over a wider range of runs scored than Pythagenport, including the mandatory value of 1 at 1 rpg. See here for more.
Speed Score (SPD) is one of five primary production metrics used by PECOTA in identifying a hitter's comparables. It is based in principle on the Bill James speed score and includes five components: Stolen base percentage, stolen base attempts as a percentage of opportunities, triples, double plays grounded into as a percentage of opportunities, and runs scored as a percentage of times on base.
Beginning in 2006, BP has developed a proprietary version of Speed Score that takes better advantage of play-by-play data and ensures that equal weight is given to the five components. In the BP formulation of Speed Score, an average rating is exactly 5.0. The highest and lowest possible scores are 10.0 and 0.0, respectively, but in practice most players fall within the boundary between 7.0 (very fast) and 3.0 (very slow).
Unintentional Walk Rate (BB) is one of five primary production metrics used by PECOTA in identifying a player's comparables. It is defined as (BB-IBB)/PA.
The probability of winning the current game, given some
information about how many runs each team has scored to a certain point in the game, how many outs there are, whether there are runners on base, and the strength of each team. Keith Woolner outlined a method for computing Win Expectancy given all of these parameters in BP 2005.
A correction made to raw runs when converting them to a standard league to preserve their win value. Define an average team from season games played, league runs per game (9 innings or 27 outs, depending on whether you are using pitcher or batter data), and appropriate adjustments (park, team hitting/pitching, difficulty). "Team" is the effect of replacing one player on the average team with the player we are analyzing. Calculate the pythagorean exponent from (average + team) / games as your RPG entry; calculate winning percentage using the modified pythagorean formula. Now, go backwards, solve for "team" runs, given the winning percentage, an average team that scores 4.5 per game, and a pythagorean exponent of 2.00.