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Glossary: Sabermetric

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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)


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Baserunning Runs. Measures the number of runs contributed by a player's advancement on the bases, above what would be expected based on the number and quality of the baserunning opportunities with which the player is presented, park-adjusted and based on a multi-year run expectancy table. BRR is calculated as the sum of various baserunning components: Ground Advancement Runs (GAR), Stolen Base Runs (SBR), Air Advancement Runs (AAR), Hit Advancement Runs (HAR) and Other Advancement Runs (OAR).

Here is an example of the Baserunning Runs spectrum based on the 2011 season:

Excellent - Ian Kinsler 11.6
Great - Coco Crisp 4.3
Average - Bobby Abreu 0.0
Poor - Casey Kotchman -4.4
Horrendous - Ryan Howard -9.4

Note: Credit/blame for Double plays is not ascribed to either batter or runner(s) via BRR. Hence Manny Machado can lead MLB in GIDP in 2018 yet have almost +1 BRR. Or Tommy Pham with 18 GIDP in 570 PA and almost +5 BRR.

Base-Out State

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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.


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Break:Tunnel Ratio - This stat shows us the ratio of post-tunnel break to the differential of pitches at the Tunnel Point. The idea here is that having a large ratio between pitches means that the pitches are either tightly clustered at the hitter's decision-making point or the pitches are separating a lot after the hitter has selected a location to swing at. Either way a pitcher's ratio can be large.

CS Prob

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This stat tells us the likelihood that a particular pitch will be called a strike based on a variety of factors. CS Prob is calculated on every pitch thrown by a pitcher. CS Prob is a proxy for control, or the ability of a pitcher to throw strikes.


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Deserved Run Average (DRA) uses a collection of multilevel models to estimate the most likely contributions of pitchers to the run-scoring that occurs around them. Unlike other component metrics, DRA considers (and adjusts) for park, opponent, and, when helpful, framing, temperature, and pitch type as well. DRA achieves significant improved reliability over both raw pitcher statistics (like ERA) and other pitcher run estimators. DRA estimates include uncertainty estimates, making it easier to compare players to one another and to appreciate the stability of DRA's estimates, even early on in the season. In general, a pitcher's DRA, plus or minus one standard deviation (DRA_SD) encompasses at least 70% of the "true" DRA values for that pitcher.


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DRA-Minus ("DRA–") As noted above, we've received multiple requests for a "minus" version of DRA, something that rates pitchers by how well they compared to their peers rather than by an amount of predicted runs allowed in a given season. Knowledgeable baseball fans are familiar with statistics like this. Common examples include wRC+ and ERA-. The idea is to put an average player for each season at 100, and then rate players by how much they vary from the average. By rating every pitcher by how good (or poor) he was by comparison to his peers, we can make fairer comparisons across different seasons and different eras. These comparisons aren't perfect: We can't make baseball 50 years ago more diverse or force today's players to endure the conditions of 50 years ago, but metrics like DRA– allow comparisons of pitchers across seasons and eras to be much more meaningful.

The formula for DRA-Minus is DRA / DRA_mean * 100

Unlike cFIP (which considers only the three true outcomes), DRA– will not have a forced standard deviation. The two numbers (which are otherwise both scaled to 100) can still be compared, but be mindful of that distinction. For both cFIP and DRA–, lower is better.

See: http://www.baseballprospectus.com/article.php?articleid=26613


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Learn more about DRC+ at our DRC+ Showcase page.

Traditional metrics compromise accuracy in two ways: (1) they summarize play outcomes in which players were involved, not player contributions to those outcomes; and (2) they treat all outcomes as equally likely to be driven by the player, even though no one believes that is true.

DRC+ addresses the first problem by rejecting the assumption that play outcomes equal player contributions, and forces players to demonstrate a consistent ability to generate those outcomes over time to get full credit for them. DRC+ addresses the second problem by recognizing that certain outcomes (walks, strikeouts) are more attributable to player skill than others (singles, triples). DRC+ gives more weight to extreme performances in the former (because they are probably meaningful) and less weight to extreme performances in the latter (because they are less likely to be meaningful). By addressing these two deficiencies in existing metrics, DRC+ ends up being substantially more reliable and predictive than any other baseball hitting metric. (The PA-level opponent and park adjustments don’t hurt either). The Top 15 undervalued players are not “undervalued” in the sense that they are hidden Hall of Famers; they get undervalued by OPS / wOBA / SLG / wRC+ / whatever because those metrics do not weight each event by the likelihood it was driven by the player himself, as opposed to random variance.

A player like Alberto Callaspo has somewhat extreme numbers in metrics DRC+ considers uniquely likely to be driven by the player: healthy walk rates and exceptionally low strikeout rates. Callaspo saved runs every year, on average, by striking out very infrequently. He was above average in walks for a while, although not every season. One consequence of not striking out so much is that he was hitting more singles, and consistently gets credit for hitting more singles than average. The point isn’t that Callaspo is some great player: it is that DRC+ better understands how “real” his contributions were than other metrics, because the latter make no effort to distinguish the various aspects of his game. It brings the same, more sophisticated perspective to other players also.

Def Eff

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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


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Plate Differential - This statistic shows how far apart back-to-back pitches end up at home plate, roughly where the batter would contact the ball. This includes differentiation generated by pitch break and trajectory of the ball (which includes factors like gravity, arm angle at release, etc.).


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Release Differential - When analyzing pitchers, we often talk about consistency in their release point, pointing to scatter plots to see if things look effectively bunched or not. This stat measures the average variation between back-to-back pitches at release.


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Tunnel Differential - This statistic tells you how far apart two pitches are at the Tunnel Point—the point during their flight when the hitter must make a decision about whether to swing or not (roughly 175 milliseconds before contact).


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(from http://bbp.cx/a/26195)

Under baseball’s scoring rules, a wild pitch is assigned when a pitcher throws a pitch that is deemed too difficult for a catcher to control with ordinary effort, thereby allowing a baserunner (including a batter, on a third strike) to advance a base. A passed ball is assigned when a pitcher throws a pitch that a catcher ought to have controlled with ordinary effort, but which nonetheless gets away, also allowing a baserunner to move up a base. The difference between a wild pitch and a passed ball, like that of the “earned” run, is at the discretion of the official scorer. Because there can be inconsistency in applying these categories, we prefer to consider them together.

Last year, Dan Brooks and Harry Pavlidis introduced a regressed probabilistic model that combined Harry’s pitch classifications from PitchInfo with a With or Without You (WOWY) approach. RPM-WOWY measured pitchers and catchers on the number and quality of passed balls or wild pitches (PBWP) experienced while they were involved in the game.

Not surprisingly, we have updated this approach to a mixed model as well. Unfortunately, Passed Balls or Wild Pitches Above Average would be quite a mouthful. Again, we’re trying out a new term to see if it is easier to communicate these concepts. We’re going to call these events Errant Pitches. The statistic that compares pitchers and catchers in these events is called Errant Pitches Above Average, or EPAA.

Unfortunately, the mixed model only works for us from 2008 forward, which is when PITCHf/x data became available. Before that time, we will rely solely on WOWY to measure PBWP, which is when pitch counts were first tracked officially. For the time being, we won’t calculate EPAA before 1988 at all, and it will not play a role in calculating pitcher DRA for those seasons.

But, from 2008 through 2014, and going forward, here are the factors that EPAA considers:

  • The identity of the pitcher;
  • The identity of the catcher;
  • The likelihood of the pitch being an Errant Pitch, based on location and type of pitch, courtesy of PitchInfo classifications.

Errant Pitches, as you can see, has a much smaller list of relevant factors than our other statistics.

In 2014, the pitchers with the best (most negative) EPAA scores were:


Errant Pitches Above Average (EPAA)

Carlos Carrasco


Ronald Belisario


Jesse Chavez


Clay Buchholz


Felix Doubront


Daisuke Matsuzaka


And the pitchers our model said were most likely to generate a troublesome pitch were:


Errant Pitches Above Average (EPAA)

Masahiro Tanaka


Jon Lester


Matt Garza


Dallas Keuchel


Drew Hutchison


Trevor Cahill


t want to add a new term.


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FIP is a component ERA inspired by the work of Voros McCracken on defense-independent 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


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Speed Changes - This is the average difference, in seconds, between back-to-back pitches.


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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.


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This is the selected pitches for drilling down on a specific sequence.


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This is the selected pitches for drilling down on a specific sequence.

Pitcher CSAA

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This stat details the additional called strikes outside the reference zone that are credited to the pitcher after accounting for catcher, umpire, pitch type, etc. This stat is calculated on all called pitches (i.e., balls not in play). Pitcher CSAA is a proxy for command, or the pitcher's ability to locate the ball precisely. Base "CSAA" values are the "expected" or "mean" values for each player. We're now also pleased to provide, for seasons 2008 to the present, the standard deviations (SD) for both CSAA and CSAA / Framing Runs. SDs allow you to calculate uncertainty intervals to say how certain we are that the true measurement of the player falls within the defined interval.


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Break Differential - This stat tells us how much each spin-induced movement is generated on each pitch between the tunnel point and home plate. Think of this like PITCHf/x pitch movement, except that it is only tracking the time between the Tunnel Point and home plate.


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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.


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Release:Tunnel Ratio - This stat shows us the ratio of a pitcher's release differential to their tunnel differential. Pitchers with smaller Release:Tunnel Ratios have smaller differentiation between pitches through the tunnel point, making it more difficult for opposing hitters to distinguish them in theory.


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Speed Score

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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).


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(from http://bbp.cx/a/26195)

Our hypothesis is that base-stealing attempts are connected with the pitcher’s ability to hold runners. When baserunners are not afraid of a pitcher, they will take more steps off the bag. Baserunners who are further off the bag are more likely to beat a force out, more likely to break up a double play if they can’t beat a force out, and more likely to take the extra base if the batter gets a hit.

Takeoff Rate stats consider the following factors:

  • The inning in which the base-stealing attempt was made;
  • The run difference between the two teams at the time;
  • The stadium where the game takes place;
  • The underlying quality of the pitcher, as measured by Jonathan Judge’s cFIP statistic;
  • The SRAA of the lead runner;
  • The number of runners on base;
  • The number of outs in the inning;
  • The pitcher involved;
  • The batter involved;
  • The catcher involved;
  • The identity of the hitter on deck;
  • Whether the pitcher started the game or is a reliever.

Takeoff Rate Above Average is also scaled to zero, and negative numbers are once again better for the pitcher than positive numbers. By TRAA, here were the pitchers who worried baserunners the most in 2014.


Takeoff Rate Above Average (TRAA)

Bartolo Colon


Lance Lynn


Hyun-jin Ryu


Adam Wainwright


T.J. McFarland


Nathan Eovaldi


And here were the pitchers who emboldened baserunners in 2014:


Takeoff Rate Above Average (TRAA)

Joe Nathan


Tim Lincecum


Drew Smyly


Tyson Ross


A.J. Burnett


Juan Oviedo


Unintentional Walk Rate

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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.


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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.

Win Adjustment

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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.


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(from http://www.hardballtimes.com/fip-in-context/)

Because cFIP is on a 100 “minus” scale, 100 is perfectly average, scores below 100 are better, and scores above 100 are worse. Because cFIP has a forced standard deviation of 15, we can divide the pitchers into general and consistent categories of quality. Here is how that divides up for the 2014 season, with some representative examples:

Representative Examples, 2014 Season
cFIP Range Z Score Pitcher Quality Examples
<70 <-2 Superb Aroldis Chapman (36/best), Sean Doolittle (49), Clayton Kershaw (57), Chris Sale (63)
70–85 <-1 Great Zach Duke (72), Jon Lester (75), Mark Melancon (75), Zack Greinke (82)
85–95 <-.33 Above Avg. Hyun-jin Ryu (87), Francisco Rodriguez (88), Johnny Cueto (89), Joba Chamberlain (90)
95–105 -.33 < 0 < +.33 Average Tyson Ross (95), Sonny Gray (96), Matt Barnes(99), Brad Ziegler (104)
105–115 >.33 Below Avg. Brian Wilson (106), Tanner Roark (107), Nick Greenwood (111), Ubaldo Jimenez (112)
115–130 >1 Bad Edwin Jackson (116), Jim Johnson (120), Kyle Kendrick (124), Aaron Crow (125)
130+ >2 Awful Brad Penny (130), Paul Maholm (131), Mike Pelfrey (132/worst), Anthony Ranaudo (132/worst)

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