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

As noted in the article, computing DRA is a five-step process:

  • Step 1: Compile the individual value of all baseball batting events in a season.
  • Step 2: Adjust each batting event for its context.
  • Step 3: Account for base-stealing activity. TRAA and SRAA
  • Step 4: Account for Passed Balls / Wild Pitches.EPAA
  • Step 5: Calculate DRA (Deserved Run Average).

What It Means
So there you have it: DRA, explained. Most of you really don’t care how we got there; you just care that DRA will be easy to look up and be a good evaluator of pitcher performance. In both respects, you are in luck.

As for the first issue, past DRA is available on our leaderboards right now. In-season DRA during 2015 will be calculated each night after the previous day’s games have concluded. You will be able to use DRA not only to put past pitching performances in context but also to monitor the value of pitchers as we progress through the 2015 season, and beyond. As with our other statistics, DRA will be available for you to download and use for your own comparisons and work.

As for the second issue, rest assured that your time spent reading this article was not in vain. DRA does a very good job of measuring a pitcher’s actual responsibility for the runs that scored while he was on the mound—certainly better than any metric we are aware of in the public domain. And only DRA gives you the assurance that a pitcher’s performance is actually being considered in the context of the batter, catcher, runners on base, as well as the stadium and stadium environment in which the baseball game occurred.

The detailed explanation of DRA’s effectiveness is saved for the accompanying In Depth article.


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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.

Unlike cFIP (which measures true talent), DRA– (which measures true talent plus luck) 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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Type the definition of the term here, or leave the text as it is if you don't want to add a new term.


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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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(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



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Zone Rate is calculated using PITCHf/x data and shows the percentage of pitches seen (by hitters) or thrown (by pitchers) that are in the rule-book strike zone.

Hitter Examples (2012):

Very few: Pablo Sandoval, 0.4005
Few: Kirk Nieuwenhuis, 0.4833
Around average: Andres Torres, 0.5054
Many: Bobby Abreu, 0.5244
Very many: Chone Figgins, 0.5787

Pitcher Examples (2012):

Very few: Jared Hughes, 0.33554
Few: Jared Burton, 0.4775
Around average: Jeremy Accardo, 0.4879
Many: Joe Blanton, 0.5217
Very many: Jake Mcgee, 0.5897


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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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