Glossary: Baseball Prospectus Exclusive

The 1-day injury risk is a part of the CHIPPER injury projection system. It assesses how likely it is that a player will miss one or more days due to injury.

Associated colors represent the probability of risk: green for low probability, yellow for moderate, and red for high.

The 15-day injury risk is a part of the CHIPPER injury projection system. It assesses how likely it is that a player will miss 15 or more days due to injury.

Associated colors represent the probability of risk: green for low probability, yellow for moderate, and red for high.

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.

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

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:

Fair Run Average differs from FIP in a few ways. While FIP is concerned only with what a pitcher is believed to control-typically strikeouts, walks, and home runs, though Prospectus includes hit batsmen in our FIP calculation-Fair Run Average takes things a step further. Pitchers receive credit for good sequencing, thus rewarding pitchers who seem to work out of jams more often than usual. Fair Run Average also considers batted ball distribution, base-out state, and team defensive quality (as measured by Fielding Runs Above Average).

Here is an example of the Fair Run Average spectrum based on the 2011 season:

Excellent - Clayton Kershaw 2.90
Great - Brandon McCarthy 3.42
Average - Ivan Nova 4.36
Poor - Brett Cecil 5.14
Horrendous - Jake Arrieta 5.88

The biggest difference between Fielding Runs Above Average and similar defensive metrics comes in the data and philosophy used. Whereas other metrics use zone-based fielding data, Fielding Runs Above Average ignores that data due to the numerous biases present. Fielding Runs Above Average instead focuses on play-by-play data, taking a step back and focusing on the number of plays made compared to the average number of plays made by a player at said position. The pitcher's groundball tendencies, batter handedness, park, and base-out state all go into figuring out how many plays an average player at a position would make.

Here is an example of the Fielding Runs Above Average spectrum based upon the 2011 season-for the sake of consistency, the players featured below all play the same position (center field):

Excellent - Jacoby Ellsbury 11.6
Great - Nyjer Morgan 5.5
Average - Marlon Byrd 0.6
Poor - Roger Bernadina -5.2
Horrendous - Melky Cabrera -13.2

WARP components can be found in this article, which also describes 2015 changes to FRAA: http://www.baseballprospectus.com/article.php?articleid=27944

Abbreviation for "Jaffe WARP Score." System invented by Jay Jaffe to assess a player's worthiness for enshrinement in the Hall of Fame. Equal to the average of a player's peak WARP and total career WARP. Jaffe used the system before, but the term was coined here.

The methodology is explained in full here.

A player's JAWS score is the average of his career WARP total and his peak total [(Career WARP + Peak WARP) / 2], where Peak is a player's best seven seasons (early versions of the system used best five consecutive, but this method was abandoned starting with the 2006 BBWAA ballot). This JAWS score is then compared to a modified average of the enshrined Hall of Famers at each position, with a slight adjustment made for positional scarcity among enshrinees.

Because the WARP data undergoes minor tweaks from time to time, JAWS standards at each position need occasional re-computation. The standards for the 2012 BBWAA ballot are:

POS WARP  Peak  JAWS
C    51.7  33.9  42.6
1B   61.1  40.8  51.4
2B   64.7  43.2  53.8
3B   68.6  45.3  55.0
SS   60.6  40.3  50.7
LF   65.1  42.0  53.5
CF   72.8  46.8  58.3
RF   66.2  40.9  53.6
SP   51.1  36.0  43.5
RP   29.1  17.5  23.3

Opponent's Quality, True Average -- the aggregate True Average of all batters faced (by a pitcher), or allowed by all pitchers faced (for a batter).

Based off of Bill James' Defensive Efficiency idea, PADE calculates how well a team performed on defense, while adjusting for their park environments. Certain parks make it easier for the defense to turn a ball in play into an out and this adjusts for that fact.

Introduced by James Click here and updated by Click here.

Here is an example of the Park-Adjusted Defensive Efficiency spectrum based on the 2011 season:

Excellent - Tampa Bay 4.30
Great - Los Angeles of Anaheim 1.47
Average - Atlanta -0.02
Poor - Chicago (A) -1.41
Horrendous - Minnesota -2.41

Stands for Player Empirical Comparison and Optimization Test Algorithm. PECOTA is BP's proprietary system that projects player performance based on comparison with historical player-seasons. There are three elements to PECOTA:

1) Major-league equivalencies, to allow us to use minor-league stats to project how a player will perform in the majors;
2) Baseline forecasts, which use weighted averages and regression to the mean to produce an estimate of a player's true talent level;
3) A career-path adjustment, which incorporates information about how comparable players' stats changed over time.

Check out the PECOTA section of the glossary for more on the system's intricacies.

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.

True Average incorporates aspects that other linear weights-based metrics ignore. Reaching base on an error and situational hitting are included; meanwhile, strikeouts and bunts are treated as slightly more and less damaging outs than normal. The baseline for an average player is not meant to portray what a typical player has done, but rather what a typical player would do if given similar opportunities. That means adjustments made for parks and league quality. True Average's adjustments go beyond applying a blanket modifier-players who play more home games than road games will see that reflected in their adjustments. Unlike its predecessor, Equivalent Average, True Average does not consider baserunning or basestealing.

Here is an example of the True Average spectrum based upon the 2009-2011 seasons:

Excellent - Miguel Cabrera .342
Great - Alex Rodriguez .300
Average - Austin Jackson .260
Poor - Ronny Cedeno .228
Horrendous - Brandon Wood .192
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See: http://www.baseballprospectus.com/article.php?articleid=11717

0.9 (from the article) is no longer a stationary number, but a scale based on current season runs. It's all the way up to almost 1.07 now, due to run scoring being so much lower than when Colin wrote this (from the link above):

From 1993 to 2009, you can figure TAv simply as:

0.260 + (RAA/PA)*.9

Now, we will be tuning those values slightly to match the batting average for that season, but other than
that, that’s the formula for TAv we will be using once the new stat reports are rolled out.

[...]

All that matters essentially is the computation of the initial R/PA values. When people ask about wOBA, most
of the time what they really care about is the values presented on Fangraphs, derived from this set of
linear weights developed by Tom Tango.

True Average Against is to True Average what Batting Average Against is to Batting Average. In other words, True Average Against will tell you how well opposing batters have hit a pitcher. Do note that while True Average Against takes the pitcher's park, league, and situational-based hitting into account, it does not exclude data where the pitcher faced an opposing pitcher. Because of that, National League pitchers should possess lower True Average Against than their American League counterparts.

UPSIDE is determined by evaluating the performance of a player's top-20 PECOTA comparables. If a comparable player turned in a performance better than league average, including both his batting and fielding performance, then his wins above average (WARP minus replacement value) are counted toward his UPSIDE. A base of two times wins above average is used for position players, and an adjustment is made to pitcher values such that they are comparable. If the player was worse than average in a given season, or he dropped out of the database, the performance is counted as zero.

Value Over Replacement Player. The number of runs contributed beyond what a replacement-level player at the same position would contribute if given the same percentage of team plate appearances. VORP scores do not consider the quality of a player's defense.

Here is an example of the Value Over Replacement Player spectrum based on the 2011 season:

Excellent - Matt Kemp 95.2
Great - Robinson Cano 51.4
Average - Eric Hosmer 19.9
Poor - Derrek Lee 3.2
Horrendous - Adam Dunn -22.6

VORP for position players consists of batting runs above average (BRAA), position adjustment (POS_ADJ), baserunning runs above average (BRR - which includes - but is not limited to - stolen bases and times caught stealing ), and an adjustment for replacement level (REP_ADJ).

Perhaps no sabermetric theory is more abstract than that of the replacement-level player. Essentially, replacement-level players are of a caliber so low that they are always available in the minor leagues because the players are well below major-league average. Prospectus' definition of replacement level contends that a team full of such players would win a little over 50 games. This is a notable increase in replacement level from previous editions of Wins Above Replacement Player.

Here is an example of the Wins Above Replacement Player spectrum based on the 2011 season:

Excellent - Jose Bautista 10.3
Great - Hunter Pence 5.2
Average -Gaby Sanchez 2.0
Poor - Adam Lind 0.5
Horrendous - Adam Dunn -1.7

WARP components can be found in this article, which also describes 2015 changes to FRAA: http://www.baseballprospectus.com/article.php?articleid=27944