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05-29

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13

Prospectus Feature: The Ervin Santana Disagreement
by
Jonathan Judge

05-19

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0

Player Profile: Joe Biagini
by
Wilson Karaman

04-24

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2

The Buyer's Guide: Chase Anderson
by
Eric Roseberry

03-09

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27

Prospectus Feature: DRA 2017: The Convergence
by
Jonathan Judge

08-26

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Fantasy Freestyle: DRA Do-Gooders
by
Wilson Karaman

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

06-26

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0

Team Chemistry: Explaining the DRA-Beaters
by
John Choiniere

05-23

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7

Prospectus Feature: Overcoming Negativity
by
Jonathan Judge

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

12-29

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1

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

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

09-10

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1

Fantasy Freestyle: The DRA vs. ERA Divide
by
George Bissell

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

08-10

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3

Rubbing Mud: What Are the Different Pitcher WARs Good For?
by
Matthew Trueblood

07-23

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2

Rubbing Mud: Cole Hamels' Hapless Helpers
by
Matthew Trueblood

07-06

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6

The Buyer's Guide: Brett Anderson
by
J.P. Breen

06-16

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8

Fantasy Freestyle: Projecting Buy-Low and Sell-High Pitchers by cFIP and DRA-
by
Wilson Karaman

06-01

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2

The Buyer's Guide: Carlos Carrasco
by
J.P. Breen

05-14

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5

Fantasy Freestyle: Danny Salazar: On the Cusp of Stardom?
by
Greg Wellemeyer

05-01

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2

Painting the Black: Truth-Testing TRAA
by
R.J. Anderson

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

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DRA, examined through the lens of MLB's surprising ERA leader.

As you know, different pitching estimators tend to agree on which pitchers are good and which ones are not. The interesting cases are when they disagree—strongly. In those situations, the proper response is not to decide which one is “correct” (to the extent there is such a thing), but rather to look at why they disagree.

On a related note, people have recently asked for us to do more explaining of how Deserved Run Average (DRA) works. Often, it’s easiest to do that with an example.

Today, our example is Twins right-hander Ervin Santana. Santana has a 1.80 ERA, a 1.80 RA9, a 4.00 FIP, and a cFIP of 102, but a DRA of 2.74. He is striking out 6.4 batters per nine innings, walking 3.5 batters per nine innings, and giving up just under one single home run per nine innings.

ERA and RA9 suggest an extraordinary pitcher; FIP and cFIP see an average pitcher, and DRA sees him somewhere in between, as a very good, but not-as-good-as-his-RA9 pitcher. Why the difference in opinion?

FIP and cFIP, as you know, look only at home runs, strikeouts, hit batsmen, and walks. Santana gives up a below-average number of home runs, generates fewer strikeouts than average, and gives up a tad more walks than average. Home runs count for more than the other aspects, so he comes out as an average-ish pitcher overall.

DRA sees a more interesting profile. Santana has a left-on-base percentage of 91 percent and a batting average on balls in play of .136. If you look at Santana’s player card, you’ll see that he has played in hitter-friendly stadiums (pitcher park factor, or PPF, of 107), faced roughly average opponents (oppTAv of .258), and most importantly of all, has held batters to a True Average of .173. Since the league TAv is .260, this is an incredible amount of damage control on contact.

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May 19, 2017 6:00 am

Player Profile: Joe Biagini

0

Wilson Karaman

Improvement on his fastball and curveball, and excellent location spotting no matter the type of pitch, put Biagini in position to succeed. The Jays have given him an opportunity to start and he's running with it.

Joe Biagini put on a couple of clinics the other night, first in how to let it snowball right quick, and then another in how to regain control of a lost cause. After ceding six runs without the benefit of an out—and, we must mind for context, unleashing the most fantastical of crescendos in the form of a three-run shot by Kurt Suzuki—he quietly proceeded to send 12 straight back to the pine and grind out four tidy innings that the bullpen didn’t have to.

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April 24, 2017 6:00 am

The Buyer's Guide: Chase Anderson

2

Eric Roseberry

He has posted a 1.13 ERA in four starts for the Brewers. Will this success sustain?

The Buyer’s Guide is a weekly column designed to help fantasy owners assess a player who sees an increased level of interest on a given week. This column focuses on players who generally have lower than 40 percent ownership rates across various leagues.

Since making his major-league debut in 2014, Chase Anderson has been a slightly below-average starting pitcher. So why is he the subject of this week’s “Buyer’s Guide?” Because Anderson is off to a surprisingly good start, and fantasy owners are beginning to take notice. Anderson saw the 10th-highest jump in ownership rate in ESPN leagues this week (from 5.6 percent to 35.3 percent). The increase was even greater in CBS leagues, which saw him go from 24 percent owned to 51 percent. In Yahoo’s latest “Transaction Trends,” Anderson was the ninth-most added pitcher to rosters.

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What if you could have a metric that accurately describes what a pitcher did while also reliably forecasting the skills that pitcher would bring to the future?

Two years ago, I wrote the first DRA essay, focusing on the challenge of modeling descriptive versus predictive player performance. At the time, my prognosis for threading that needle was rather grim:

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?

The reader’s likely response is: “I’d like one metric that excels at all three!” Sadly, when it comes to composite pitcher metrics, this might not be possible.

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August 26, 2016 10:53 am

Fantasy Freestyle: DRA Do-Gooders

0

Wilson Karaman

Our advanced pitching metric suggests brighter days ahead for some of these hurlers.

As we head into the homestretch of the season some of you are angling for a title run, or a challenge for the money, or waiting in vain for your one-category “Perfect Games” league to get more interesting. I, however, play in at least two full keeper leagues in which mine eyes are affixed squarely upon the great horizon beyond 2016. And that means using this time of the year to start searching for potentially undervalued acquisition targets, either for your end-of-year FAAB queue or your off-season trade list. So let’s start in a basic and logical place with some pitchers who have performed much worse than their underlying metrics suggest they should have performed to date. Below is a table of the hurlers with the biggest gaps between their DRA and ERA. I’ve isolated guys who have performed at least a run and a half worse by ERA than their DRA suggests. And for the sake of weeding out some additional riffraff I’ve limited the pool to those arms who have performed as at least a roughly league-average level.

So without further ado, here’s our list:

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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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The Cubs' pitchers are on a historical DRA-beating pace. Are there some factors that explain why some teams do this?

I’m certainly not the first person, and maybe not even the first person whom you’ve read today, to point out that the Cubs are having an incredible season. As of the moment this sentence is being written, their third-order winning percentage is an insane 0.750, and they sit in first place on both the batting and overall WARP leaderboard (and in fourth on the pitching one). As was pointed out by Rob Arthur and Ben Lindbergh at FiveThirtyEight last week, their pitching staff’s BABIP allowed is historically low. They also are among the best all-time in outperforming their DRA, the best pitching skills estimator currently available.

It was even more extreme a few days ago, but as of Friday evening the Cubs’ RA9-DRA was -0.95—almost a full run difference over nine innings. That’s the 12th-biggest difference in the entirety of what you might call the “DRA era,” which begins in the early 1950s. Also of note, both their DRA and RA9 are lower than any team above them on that list.

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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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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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