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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

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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.

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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And an explanation of random effects vs. fixed effects.

Jared Cross, the esteemed author of the Steamer projection system, asked a very good question over at Tango's blog that I wanted to address in more detail. Referencing Deserved Run Average (“DRA”), our new descriptive pitcher value metric at Baseball Prospectus, Jared asked:

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September 8, 2015 6:00 am

DRA and the Cy Young Award


Jonathan Judge

Why DRA is the best stat for picking Cy Young Award winners and who those winners should be as of now

We are rapidly reaching awards season, when writers and fans alike decide which player has been "the best" in his respective category.

The Cy Young Award has been given since 1956, and is widely viewed as a definitive benchmark for an elite pitching career. The award is conferred by the Baseball Writers' Association of America (BBWAA), different members of which are assigned to vote for each of the BBWAA's various awards.

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August 26, 2015 6:00 am

DRA Run Values


Jonathan Judge

You can now view the individual factors that go into each pitcher's Deserved Run Average.

Deserved Run Average (DRA) is, as you know, the new pitcher-value metric here at Baseball Prospectus. DRA reflects our best estimate of the runs each pitcher "deserved" to allow in a given season. The number of deserved runs above or below average is used to calculate BP's pitcher Wins Above Replacement Player (WARP).

By popular demand, we've now extracted the estimated effect, in runs, that various external factors have had on individual pitchers. You can get a direct link from the main site by clicking "DRA Run Values" from the Statistics pull-down menu, or by clicking here.

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You asked about DRA; we've got answers.

About six weeks ago, we introduced you to Deserved Run Average (DRA),1 our new metric for evaluating past pitcher performance at Baseball Prospectus. We gave you both the overview of why a new pitcher performance metric was needed and explained in detail how the metric worked and the equations we were using to get there. We even subjected one of the authors to intense questioning.

After considering the comments we received and a few additional thoughts of our own, we've made some minor revisions. Many readers also asked us for a "DRA minus" statistic that would allow them compare different pitcher seasons across different years and eras. We've done that too.

Finally, other readers asked that we break down some examples of DRA value calculations so that even if you can't (or don't want to) do the modeling yourself, you at least understand why DRA acts in the way it does, and why it does a better job than ERA and FIP in evaluating pitcher quality. We'll take these topics in order.

A Refresher
Before we begin, let's provide a brief reminder of what DRA is and how it works.

DRA is premised on the notion that while a pitcher is probably the player most responsible, on average, for what happens while he is on the mound, he is not responsible for everything. DRA therefore only assigns the runs a pitcher most likely deserved to be charged with.

DRA works through a multi-step process.

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Baseball Prospectus is excited to announce what we believe to be the best measure of pitching ever, as well as advances in measuring pitchers' effectiveness stifling basestealers and avoiding errant pitches.

Baseball Prospectus' Director of Technology Harry Pavlidis will be chatting with readers Thursday at 1 p.m. ET. If you have any questions after reading this overview of Deserved Run Average, ask them here.

Earned Run Average. Commonly abbreviated as ERA, it is the benchmark by which pitchers have been judged for a century. How many runs did the pitcher give up, on average, every nine innings that he pitched? If he gave up a bunch of runs, he was probably terrible; if he gave up very few runs, we assume he’s pretty good.

But ERA has a problem: it essentially blames (or credits) the pitcher for everything, simply because he threw the pitch that started the play. Sometimes, that is fair. If a pitcher throws a wild pitch, he can’t blame the right fielder for that. And if a pitcher grooves one down the middle of the plate, chances are that’s on him too. Not too many catchers request those.

However, most plays in baseball don’t involve wild pitches or gopher balls. Moreover, things often happen that are not the pitcher’s fault at all. Sometimes the pitcher throws strikes the umpire incorrectly calls balls. Other times they induce grounders their infielders aren’t adept enough to grab. And still other times, a routine fly ball leaves the park on a hot night at a batter-friendly stadium.

ERA doesn’t account for any of that. It just tells us, in summary fashion, how many runs were “charged” to the pitcher “of record.” And so, a starting pitcher who departs with a runner on first gets charged with that run even if the reliever walks the next three batters. The same starter would get charged if the reliever makes a good pitch, but the shortstop can’t turn a double play. And none of these runs count at all if they are “unearned”— an exclusion by which the home team’s scorer decides whether a fielder demonstrated “ordinary effort.”

The list of problems goes on. Pitchers who load the bases but escape are treated the same as pitchers who strike out the side. Pitchers with great catchers get borderline calls. Guys who can’t catch a break for months show immense “improvement.” Guys who are average one year wash out the next. ERA, in short, can be a bit of a mess, particularly when we have only a few months of data to consider.

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

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

A. Introduction
One of the hardest parts of any statistical investigation is defining what question it is you are trying to answer.

This is particularly important when it comes to pitcher performance metrics. What is it, exactly, that you want to know? For example:

(1) Do you care primarily about a pitcher’s past performance?

(2) Are you more worried about how many runs the pitcher will allow going forward?

(3) Or do you want to know how truly talented the pitcher is, divorced from his results this year or next?

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We move ever closer to a catcher-framing metric that captures a player's true value.

Last year, Baseball Prospectus introduced our Regressed Probabilistic Model (or “RPM”) for catcher pitch-framing. RPM uses PITCHf/x data to increase the measured accuracy of the actual contributions made by catchers. But RPM also suffered from two limitations. First, because PITCHf/x data was not publicly available before 2008, RPM could only measure catcher framing from recent seasons. Second, it relied primarily on a piecemeal approach to identifying the individual contributions of pitchers, umpires and catchers.

This year, we are pleased to announce an improvement that will address both limitations. We propose to move RPM from a “With or Without You” (WOWY) comparison method to a mixed model we call “CSAA” —”Called Strikes Above Average.” This new model allows simultaneous consideration of pitcher, catcher, batter, umpire, PITCHf/x, and other data for each taken pitch over the course of a season, and by controlling for each of their respective contributions will predict how many called strikes above (or below) average each such participant was worth during a particular season. Although PITCHf/x data is preferable when available, the mixed model (in a revised, “Retro” form) will allow us to live without it when need be, permitting us to project regressed framing of catchers all the way back to 1988, when pitch counts were first officially tracked.[1] This same technique developed for Retrosheet can also be applied to recent minor-league data to provide an even deeper view into the progression and value of this skill.

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