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Jonathan Judge 

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07-22

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1

BP Unfiltered: DRA and Groundball Bias
by
Jonathan Judge

07-22

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0

Prospectus Feature: DRA 2016: Challenging the Citadel of DIPS
by
Jonathan Judge

05-23

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7

Prospectus Feature: Overcoming Negativity
by
Jonathan Judge

05-17

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17

Prospectus Feature: The Need for Adjusted Exit Velocity
by
Jonathan Judge, Nick Wheatley-Schaller and Sean O'Rourke

05-06

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5

DRA 2016
by
Jonathan Judge

05-06

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3

DRA 2016
by
Jonathan Judge

05-06

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10

DRA 2016
by
Jonathan Judge

01-12

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16

Prospectus Feature: Catching Up
by
Jonathan Judge and Harry Pavlidis

12-29

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Best of BP 2015: DRA: An In-Depth Discussion
by
Jonathan Judge and BP Stats Team

11-25

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18

Prospectus Feature: Updates to FRAA, BP's Fielding Metric
by
Harry Pavlidis, Rob McQuown and Jonathan Judge

11-09

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12

Prospectus Feature: Passed Balls and Wild Pitches: Getting It Right
by
Jonathan Judge

10-12

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6

Prospectus Feature: DRA and Linear Weights. And Justin Verlander.
by
Jonathan Judge

09-25

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4

BP Unfiltered: Classifying Park Factors for DRA
by
Jonathan Judge

09-08

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12

DRA and the Cy Young Award
by
Jonathan Judge

08-26

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22

DRA Run Values
by
Jonathan Judge

06-10

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10

Prospectus Feature: DRA: Improved, Minused, and Demonstrated
by
Jonathan Judge, Robert Arthur, Harry Pavlidis, Dan Turkenkopf and Gregory J. Matthews

04-29

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76

Prospectus Feature: Introducing Deserved Run Average (DRA)'And All Its Friends
by
Jonathan Judge, Harry Pavlidis and Dan Turkenkopf

04-29

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16

Prospectus Feature: DRA: An In-Depth Discussion
by
Jonathan Judge and BP Stats Team

02-05

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33

Moving Beyond WOWY
by
Jonathan Judge, Harry Pavlidis and Dan Brooks

11-12

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15

The Best Roster Cores
by
Jonathan Judge

04-10

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0

Baseball ProGUESTus: Projected Roster Core Strengths for 2014
by
Jonathan Judge

11-26

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9

Baseball ProGUESTus: The Strongest (and Weakest) Roster Cores of 2013
by
Jonathan Judge

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More inner-workings of DRA 2016.

A few weeks ago, BP author Rob Mains inquired about what he saw as a possible bias in Deserved Run Average (DRA) values in favor of fly-ball pitchers, and against groundball pitchers. Specifically, he observed that ground-ball pitchers were doing worse in DRA, on average, than they were in Runs Allowed per 9 innings (RA9).

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With DRA, solving BABIP--and other reasons to be excited about what we're measuring.

As many of you know, we updated the formulation of Deserved Run Average (DRA) once again for the 2016 baseball season. We gave you the overview of the changes here, discussed the innards here, and talked about the new run-scaling mechanism here.

This last article deals with arguably the most important question of all: What, exactly, is DRA trying to tell you? And what does it mean?

Last year, DRA was focused on being a “better” RA9. After running one overall mixed model to create a value per plate appearance for each pitcher, we ran a second regression, using multi-adaptive regression splines (MARS), to model the last three years of relationships between all pitcher value rates and park-adjusted pitcher linear weights allowed. The predictions from this second regression took each season’s mixed model results, forced them back into a runs-allowed framework, and then converted PAs to IPs to get DRA.

This approach did succeed in putting DRA onto an RA9 scale, but in some ways it was less than ideal.

First, having moved one step forward with a mixed model, we arguably were taking a half step back by reintroducing the noisy statistics—raw linear weights and, effectively, RA9—that we were trying to get away from in the first place. The results were generally fine: Good pitchers did well, bad pitchers did poorly, and there were defensible reasons why DRA favored certain pitchers over others when it disagreed with other metrics. But, the fact that something works reasonably well is not, by itself, sufficient to continue doing it.

Second, this approach forced us to make DRA an entirely descriptive metric with limited predictive value, since its yardstick metric, RA9, is itself a descriptive metric with limited predictive value. This did allow DRA to “explain” about 70 percent of same-season run-scoring (in an r-squared sense), which was significantly more than FIP and other metrics, but also required that we refer readers instead to cFIP to measure pitcher skill and anticipated future production.

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DRA in depth: Finding a run-expectancy curve that would eliminate the negative DRA.

This is the second in a series of articles explaining in depth the updated formulation of Deserved Run Average. The overview can be found here, and Part I of the in-depth discussion of the revised approach can be found here.

Call me Jonathan.

For most of this offseason, my (entirely metaphorical) White Whale was baseball’s run expectancy curve; the distribution, if you will, between the minimum and the maximum number of runs yielded by pitchers per nine innings of baseball. Why would something so seemingly arcane be so very important to me? Let’s start with some background on run expectancy.

In 2015, for pitchers with at least 40 innings pitched, their ERAs ranged from .94 (Wade Davis) to 7.97 (Chris Capuano). In more prosperous times, such as the 2000 season, pitcher ERAs at the same threshold ranged from 1.50 (Robb Nen) to 10.64 (Roy Halladay). For something more in the middle, we can turn to 1985, when a starter (!), Dwight Gooden, had the lowest ERA at 1.53, and Jeff Russell topped things off at 7.55.

Here’s what those seasons look like on a weighted density plot, side by side:

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What you need to know before your sweeping take about a player's exit velocity.

Note: Baseball Prospectus has removed the leaderboards mentioned in this article. Thank you for your interest in our work and for your patience as we attempt to resolve this issue.

Last year, the folks at MLB Advanced Media started publishing what is commonly described as “exit velocity”: the pace at which the baseball is traveling off the bat of the hitter, as measured by the new Statcast system.

As a statistic, exit velocity is attractive for several reasons. For one thing, it is new and fresh, and that’s always exciting. It also makes analysts feel like they are traveling inside the hitting process, and getting a more fundamental look at a hitter or pitcher’s ability to control the results of balls in play.

However, we’ve seen many people take the raw average of a player’s exit velocities and assume it to be a meaningful indication, in and of itself, of pitcher or batter productivity. This is not entirely wrong: Raw exit velocity can correlate reasonably well with a batter’s performance.

But this use of raw averages also creates some problems. First, if you use exit velocity as a proxy of player ability, then you must also accept that one player’s exit velocity is a function of his opponents, be they a batter or pitcher. Put more bluntly, a player’s average exit velocity is biased by the schedule of the player’s team.

Second, and much more importantly, we have concluded Statcast exit velocity readings, as currently published, are themselves biased by the ballpark in which the event occurs. This goes beyond mere differences in temperature and park scoring tendencies. In fact, it appears that the same player generating the same hit will have its velocity rated differently from stadium to stadium, even if you control for other confounding factors.

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May 6, 2016 6:00 am

DRA 2016

5

Jonathan Judge

While it'd be tempting to clickbait Arrieta's current DRA, we prefer to take a closer look at it.

Of all the headlines you want for your updated pitcher run estimator, one of the more undesirable would be “new metric claims Cy Young winner not very good.”

And yet, that is what DRA, even in its revised form, seems to be saying about Jake Arrieta so far in 2016. You know, the guy who beat out Clayton Kershaw for the Cy Young last year; the same Jake Arrieta who has already thrown a no-hitter this year and who currently sports a 0.84 ERA. Of all the stat lines to pick a fight with, DRA chooses this one. Fantastic.

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May 6, 2016 6:00 am

DRA 2016

3

Jonathan Judge

The extremely detailed dive into this year's DRA, for the extremely detailed among you.

If you’ve gotten this far, you’re interested not only in what DRA purports to do this year, but also in how it works. Here, we’ll get into some of the details, although I’ll continue to avoid math and speak about the issues conceptually instead.

* * * * * *

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May 6, 2016 6:00 am

DRA 2016

10

Jonathan Judge

Deserved Run Average is ready to check in for the 2016 season. This year brings a few fun changes.

The 2016 baseball season is underway, and now that we are a month into it, Deserved Run Average (“DRA”) is out of the gate as well.

Introduced last year, Deserved Run Average sets out to explain the runs a pitcher should have given up, rather than those that happened to cross the plate and be charged to him. (Because DRA relies on context, we have to wait a few weeks for that context to start establishing itself.) Other pitching run estimators tend to focus on the outcomes of plays, but DRA focuses on the likelihood that the pitcher was actually responsible for those outcomes. DRA does this by looking at the outcomes of each play in light of the particular players on the field, the type of event, and various factors that can influence those events, such as: stadium, catcher framing, temperature, and base-out state.

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Today, we take our catching-defense offerings to some pretty exciting places.

It’s Catcher Day at Baseball Prospectus, as we celebrate the expansion—both in method and in scope—of our new catching statistics. Given the age and breadth of some of these stats, we truly feel as if we are debuting our large adult child.

The statistics both apply to and measure players other than catchers, but they are all perhaps most important to catchers as we measure their total value to a team. The statistics are four-fold, covering three critical catching skills:

1. Running Game

a. Swipe Rate Above Average (SRAA) – the effect of the player on base-stealing success;

b. Takeoff Rate Above Average (TRAA) – the effect of the player on base-stealing attempts;

2. Blocking Pitches

Read the full article...

A further explanation of the reasons Deserved Run Average is designed as it is and works as it does.

With the year winding to a close, Baseball Prospectus is revisiting some of our favorite articles of the year. This was originally published on April 29, 2015.

This is the #GoryMath portion of the DRA rollout. If you proceed, don’t say we didn’t warn you.

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To make things better.

Our objective at BP Stats is to provide sophisticated and useful metrics. Our mission is to do this while working transparently. Part of this involves reviewing what we have. As much—if not more—is about building new things.

Today we arrive at an update to FRAA—Fielding Runs Above Average—that represents both lanes of the process. FRAA is the metric we use at BP to measure the defensive contributions made by players in the field. In reviewing what we had, we found something we really needed to fix—outfield assists were not being counted in FRAA. We’re also adding some new metrics for catcher throwing skills (SRAA and TRAA) along with the framing and blocking metrics (CSAA and EPAA) we already were adding. This will allow FRAA to reflect player defense across a variety of important spectrums.

All of these improvements are happening even while we work on the next generation of FRAA using some of the techniques that have produced things like DRA and CSAA. This new version of FRAA will be the next “old” version if our offseason projects go well.

Zoom Out: WARP

A quick review of how we make our Wins Above Replacement Player (WARP) metric. Here’s how we generate WARP, in two parts.

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How a mystery that began with R.A. Dickey ended with a new, more precise way of measuring catcher performance.

Recently, we overhauled our approach to how we evaluate passed balls and wild pitches here at Baseball Prospectus. It started innocently enough, as an attempt to make our data better-behaved, but progressed to a gradual recognition that we—and as far as we can tell, plenty of others—have been taking the wrong approach to these events for quite some time. Today, we’ll talk about what we’ve learned, and how our models are much the better for it.

Background
It’s no secret that some catchers are better at blocking pitches than others. Yadier Molina seems to be pretty good at it, and Mike Zunino does not. But raw wild pitch and passed ball numbers can be unfair. The catcher, after all, is not the one throwing the pitch, and some pitching staffs are wilder than others, particularly if those pitchers like to throw certain pitches in certain places. The sabermetric community’s longstanding skepticism of official scoring has also led to the practice of combining passed balls and wild pitches for modeling purposes, even though the former are judged by the scorer to be the catcher’s fault, and the latter to be the fault of the pitcher.

As with all things sabermetric, the means of adjustment for these factors have become more sophisticated over time. At the simplest level, we could simply trust the official scorer, and assume the other factors largely balance out. A more sophisticated approach is the “With or Without You” method, which grades a catcher based on how he does without certain pitchers, or how pitchers do without various catchers. Going one step beyond, researchers have tried to identify relevant factors driving passed balls and wild pitches, incorporated them into models of “likely” passed ball/wild pitches versus “actual” such events, and then grading a catcher on the difference. FanGraphs has adopted a model created by Bojan Koprivica as the basis for its Runs per Passed Pitches (RPP) metric. (The parameters of that model appear to be proprietary, although Bojan does describe the relevant aspects). Finally, we unveiled our own blocking model last year, called “RPM WOWY”: a combination of PBWP likelihood, as determined by PitchInfo, followed by a WOWY assignment of credit among catchers and pitchers.

When we began incorporating mixed models into our catcher metrics earlier this year, we converted our catcher blocking model over as well, since it made sense to have all our catcher metrics on the same basic method. And so, throughout the 2015 season, we combined all of what we now call “errant pitches” into a linear mixed model, specified as follows in the R programming environment:

glmer(PBWP ~ log(prob) + (1|pitcher) + (1|catcher), family=binomial(link=’probit’)

The model was, frankly, a bit of an afterthought. We were focused more on converting RPM WOWY into our new framework rather than thinking it over from scratch. The log transformation of errant pitch probability (the former “RPM,” and the “prob” in the new blocking model) was added as much to assist convergence as anything else.


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Opening the black box--which isn't a black box at all--to illuminate Justin Verlander's brilliance this year.

Justin Verlander has been through an interesting few years. How interesting, exactly?

Using Deserved Run Average (DRA), our new metric to describe pitcher performance here at Baseball Prospectus, we can track the trend. Because we want to evaluate Verlander across several seasons, we’ll also go one step further and use DRA–. DRA– is based on DRA, but is normalized to an average of 100 for each season, with lower being better. This allows you to compare pitchers across different seasons and different run-scoring environments.

Now that we’ve got our scorecard, let’s look at Verlander’s recent seasons.

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