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Harry Pavlidis 

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01-26

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5

Prospectus Feature: Unlocking Kyle Hendricks
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-25

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3

Prospectus Feature: Two Ways to Tunnel
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-24

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17

Prospectus Feature: Introducing Pitch Tunnels
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-23

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17

Prospectus Feature: Command and Control
by
Jeff Long, Jonathan Judge and Harry Pavlidis

02-12

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5

Baseball Prospectus Book News: Baseball Prospectus 2016 Stats and Projections
by
Harry Pavlidis and Rob McQuown

01-12

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16

Prospectus Feature: Catching Up
by
Jonathan Judge and Harry Pavlidis

01-12

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6

Catchella
by
Harry Pavlidis

11-25

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18

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

02-05

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33

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

10-13

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11

Fox's Fresh Format
by
Harry Pavlidis

07-09

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1

Pitcher Profile: Jake Arrieta
by
Harry Pavlidis

05-15

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0

BP Announcements: Pizza and Prospects: Chicago, May 24
by
Harry Pavlidis

04-07

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0

BP Unfiltered: Pineda is Back
by
Harry Pavlidis

04-03

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2

Pitcher Profile: New Arms (and Pitches) of the Week
by
Harry Pavlidis

03-31

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4

Framing the Future
by
Harry Pavlidis

03-25

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18

Prospectus Preview: NL Central 2014 Preseason Preview
by
Ken Funck and Harry Pavlidis

03-08

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9

BP Announcements: Prospects, Pizza, and More: March 8 at Monti's in Chicago
by
Harry Pavlidis

03-03

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47

Framing and Blocking Pitches: A Regressed, Probabilistic Model
by
Harry Pavlidis and Dan Brooks

12-19

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16

Baseball Prospectus News: A New Direction for Stats at BP
by
Harry Pavlidis

08-30

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5

What Makes a Good Changeup?
by
Harry Pavlidis

07-17

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1

Pitcher Profile: Speeding Up at the Break
by
Harry Pavlidis

05-30

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0

BP Unfiltered: Jeff Samardzija's Mysterious Splitter
by
Harry Pavlidis

05-24

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17

What Makes a Good Changeup?
by
Harry Pavlidis

05-10

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2

What Makes A Good Changeup
by
Harry Pavlidis

05-03

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5

New Arms: Flamethrowers, Hammers, and Knucklers
by
Harry Pavlidis

04-19

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3

Pitcher Profile: Rookie Rotation Arms
by
Harry Pavlidis

04-12

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0

Pitcher Profile: A Pair of Astros
by
Harry Pavlidis

04-03

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6

Dissecting Darvish's Opening Day
by
Jason Cole, Zachary Levine, Ben Lindbergh and Harry Pavlidis

04-01

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1

BP Unfiltered: The Velocity Gainers and Losers of Spring 2013
by
Harry Pavlidis

03-28

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8

Prospectus Preview: These Questions Three: The Maybe-Next-Years
by
Bradford Doolittle and Harry Pavlidis

03-22

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0

Pitcher Profile: A Prospect, a Non-Prospect, and a Blast From the Past
by
Harry Pavlidis

03-15

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5

Pitcher Profile: New Arms of the World
by
Harry Pavlidis

03-08

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2

Pitcher Profile: Four Growing Giants
by
Harry Pavlidis

03-02

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2

BP Unfiltered: Sloan Q&A: Harry Pavlidis On f/x Tracking Data
by
Zachary Levine and Harry Pavlidis

03-01

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3

Pitcher Profile: New Arms of the Week, First Edition
by
Harry Pavlidis

02-22

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10

Pitcher Profile: Aroldis Chapman
by
Harry Pavlidis

02-21

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14

BP Unfiltered: Home Run Rates and Elbow Injuries UPDATED
by
Corey Dawkins, Ben Lindbergh, Harry Pavlidis and Doug Thorburn

02-15

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10

Pitcher Profile: Milwaukee's Rotation Brew
by
Harry Pavlidis

02-06

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0

Pitcher Profile: Johnny Cueto
by
Harry Pavlidis

08-08

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9

PITCHf/x Mailbag: Swing Tendencies on 3-0 Counts
by
Dan Brooks and Harry Pavlidis

07-13

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5

BP Announcements: Normalized Hitter/Pitcher Profiles Have Arrived
by
Dan Brooks and Harry Pavlidis

07-12

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24

Baseball Prospectus News: Introducing the BP Pitcher Profiles
by
Dan Brooks and Harry Pavlidis

07-09

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22

Baseball Prospectus News: Introducing the BP Hitter Profiles
by
Dan Brooks and Harry Pavlidis

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Kyle Hendricks might be a lot closer to Greg Maddux than he thinks.

One of the challenges of bringing BP's new pitching data to light is figuring out whether it’s useful and how we can leverage it to better understand what is happening on the field. As mentioned previously, we look at this in much the same way we look at pitch movement or velocity; we need to figure out how these tunnels data points interact with other components of a player’s performance to unlock a deeper understanding of what is happening.

Cubs right-hander Kyle Hendricks is a perfect subject to start with. As we mentioned in "Two Ways to Tunnel," Hendricks has some of the smallest pitch tunnels in all of baseball. Hendricks is often compared to Greg Maddux (including by us!), and we can see how he is in fact like Maddux in certain respects. It gives us an idea of how he’s successful, but only an abstract one. That is, we rationalize Hendricks’ success because we’ve seen Maddux do it before, but we don’t really know how all of the moving pieces come together.

In order to better understand how Hendricks is successful, we’ll have to dig into some of our new data to see what that can tell us about how he pitches.

Hendricks has steadily learned how to strike out opposing batters, increasing his K% by 55 percent from 2014 to 2015 and 2016, and it’s clear the effect that has had on his game. In fact, Hendricks’ new-found ability to strike batters out has resulted in him becoming one of the best pitchers in baseball as he has posted a sub-3.50 DRA over each of the past two seasons despite getting dinged for pitching (and winning an ERA title) in front of an elite defense.

Read the full article...

Tunneling from Greg Maddux and Barry Zito to Kyle Hendricks and Rich Hill, and everything in between.

The new pitch tunnels data released by Baseball Prospectus gives us a new glimpse into the repertoires of pitchers across the major leagues. Of course, this data is only as useful as the analysis it helps produce. To showcase how pitch tunnels data can help us better understand the success, or lack thereof, of certain pitchers, we’ll need to better understand how pitch tunnels manifest themselves in the real world.

The title of this article— “Two Ways to Tunnel”—already signals that there isn’t a one-size-fits-all approach to this new data. While game theory might suggest that each individual pitcher has an optimal approach (or approaches), there can be dramatic differences in how different pitchers attack major-league hitters. As such, we should look at this tunnels data much like we would PITCHf/x data. It’s descriptive, and there are many ways to interpret and utilize the data.

We’ll use modern pitchers to explain these concepts with requisite data, but first it’s worth revisiting a historical example. Jeff Long's very first post for BP over two years ago included the following quote about Greg Maddux, the patron saint of tunneling (yes, we know the majority of this quote is included in the introductory post about pitch tunnels, but it’s so good that it merits inclusion once again):

Read the full article...

Greg Maddux was on to something, whether he knew it or not.

One day I sat a dozen feet behind Maddux’s catcher as three Braves pitchers, all in a row, did their throwing sessions side-by-side. Lefty Steve Avery made his catcher’s glove explode with noise from his 95-mph fastball. His curve looked like it broke a foot-and-a-half. He was terrifying. Yet I could barely tell the difference between Greg’s pitches. Was that a slider, a changeup, a two-seam or four-seam fastball? Maddux certainly looked better than most college pitchers, but not much. Nothing was scary.

Afterward, I asked him how it went, how he felt, everything except “Is your arm okay?” He picked up the tone. With a cocked grin, like a Mad Dog whose table scrap doesn’t taste quite right, he said, “That’s all I got.”

Then he explained that I couldn’t tell his pitches apart because his goal was late quick break, not big impressive break. The bigger the break, the sooner the ball must start to swerve and the more milliseconds the hitter has to react; the later the break, the less reaction time. Deny the batter as much information—speed or type of last-instant deviation—until it is almost too late.

- "Greg Maddux used methodical approach to get to Cooperstown" by Thomas Boswell

Greg Maddux may have known about the concept of pitch tunnels. He may not have. Regardless, he knew how to put the concept into practice, and really that’s the important part. Maddux:

Read the full article...

Introducing new tools to evaluate command and control through the lens of strikes.

About a year and a half ago, Baseball Prospectus revealed a suite of catching stats that formed the basis for our industry-leading valuation of catchers. These new stats would shape how we perceived and discussed catcher value, but they also opened the door to better understanding the performance of pitchers.

Two key statistics—CSAA and CS Prob—serve as the basis for the pitch framing portion of our catching metrics. Today, we’ll show how those same statistics can tell us a great deal about pitching as well. CS Prob was initially introduced in 2014 with Harry Pavlidis and Dan Brooks’ first catcher framing model. Early the next year, Jonathan Judge joined the effort and the team introduced CSAA, officially moving our framing models beyond WOWY.

Of the two, CS Prob—short for Called Strike Probability—is the more straightforward: the likelihood of a given pitch being a strike. CS Prob goes beyond what the strike zone ought to be and instead reflects what it is: a set of probabilities that depends on batter and pitcher handedness, pitch location, pitch type, and count. Good pitchers understand that while the strike zone is a dynamic construct, it nonetheless has some consistencies depending on which combinations of these factors are present. We calculate CS Prob for every pitch regardless of the eventual outcome.

The other statistic, CSAA, stands for Called Strikes Above Average; a measure of how many called strikes the player in question creates for his team. In the case of catchers, we isolate the effects of the pitcher, umpire, and other situational factors which allows us to identify how many additional called strikes the catcher is generating, above or below average. For catchers, this skill is commonly described as “framing” or, in more polite company, “presentation.”

For pitchers, we can apply a similar methodology—controlling for the catcher, umpire, etc. to identify the additional called strikes created by the pitcher. CSAA is calculated only on taken pitches, an important nuance. A pitch must be taken in order to be eligible to be called a strike by the umpire, so while CS Prob looks at all pitches, CSAA only takes into account pitches where the outcome is left up to the umpire.

What can these two statistics tell us about pitcher performance and skill? First, we should define a few important things:

Read the full article...

A closer look at some of the statistics and projections that are new in Baseball Prospectus 2016.

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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...

January 12, 2016 6:00 am

Catchella

6

Harry Pavlidis

The head of our stats team dives into his favorite gems within this new world of data.

As we prepared for Catchella, I spent a fair amount of time sifting through the data—65 years of it, to be exact. There are many stories and analyses to run, but there are just a bunch of fun things that I noticed while playing in the sandbox.

Brad Ausmus is one of the most durable and valuable receivers on record: 1st in blocking runs, 2nd in framing runs, 11th in throwing runs, 1st in total runs...suddenly on the fringe of the Hall of Fame. Is he more Mark Belanger or Ozzie Smith? Either way, the man managing the Tigers these days hasn’t completely given up the tools of ignorance.

Read the full article...

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.

Read the full article...

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.


Read the full article...

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.

Introduction
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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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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October 13, 2014 6:00 am

Fox's Fresh Format

11

Harry Pavlidis

An experimental broadcast with a sabermetric slant got off to a slow start, but some 'in-game' adjustments gives us hope.

The news of a saber-oriented broadcast option for Game One of the NLCS gave me some mixed feelings. While it is always promising when a major broadcaster embraces "advanced" metrics, it's a little disheartening for it to be a separate offering, rather than something integrated with the primary broadcast.

Host Kevin Burkhardt was joined by a solid panel, including some of our friends. Padres manager Bud Black had the least broadcast experience of the group but offered the perspective of how advanced metrics are actually applied or understood by the men in the uniforms. Well known saber-scribe Rob Neyer was there, a man well-versed in communicating the subject matter at hand, along with two former big leaguers with a strong curiosity and appreciation of sabermetrics, Gabe Kapler and C.J. Nitkowski. Kapler, the former position player, has managing experience in pro ball. Nitkowski was a well traveled pitcher whose career included time in Japan.

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