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Jeff Long 

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04-12

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2

Tools of the Trade
by
Jeff Long

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

12-30

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1

Best of BP 2016: What We Know About Spin Rate
by
Jeff Long

11-25

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0

Pitching Backward: Reviewing Rapsodo
by
Jeff Long

10-05

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1

Pitching Backward: Closing the Window
by
Jeff Long

09-27

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5

Pitching Backward: What We Know About Spin Rate
by
Jeff Long

09-14

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1

Pitching Backward: What's In A Name
by
Jeff Long

08-25

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1

Pitching Backward: Zach With No K
by
Jeff Long

07-27

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4

Pitching Backward: Valuing Relievers, in July and Otherwise
by
Jeff Long

05-13

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14

Tools of the Trade
by
Jeff Long and Bryan Cole

04-14

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3

Tools of the Trade
by
Jeff Long

03-08

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11

Pitching Backward: Starting Pitching Depth, Ranked
by
Jeff Long

02-25

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0

Pitching Backward: The Superest Utility
by
Jeff Long

02-03

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3

Pitching Backward: Bringing the Heat
by
Jeff Long

01-22

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3

Pitching Backward: A Refresher on Changeups
by
Jeff Long

01-13

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2

Tools of the Trade
by
Jeff Long

01-12

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2

Pitching Backward: Pudge, Preserved
by
Jeff Long

12-17

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6

Pitching Backward: The Rise of the LiRPA
by
Jeff Long

12-10

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2

Pitching Backward: The Real-Life Closer Report
by
Jeff Long

12-07

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4

Tools of the Trade
by
Jeff Long

12-04

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1

BP Unfiltered: 'It's nearly useless'
by
Jeff Long

12-01

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7

Pitching Backward: The Bundy Conundrum
by
Jeff Long

11-17

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12

Pitching Backward: So, Hey, What if the Mets Had Intentionally Walked Wade Davis
by
Jeff Long

11-04

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Pitching Backward: How Chris Sale Learned A Third Dynamite Pitch
by
Jeff Long

10-23

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1

Pitching Backward: How The Mets Got Here
by
Jeff Long

10-15

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3

Pitching Backward: Some Signatures Are Forgeries
by
Jeff Long

09-29

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10

Pitching Backward: TINSTAABOPP
by
Jeff Long

09-17

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3

Pitching Backward: There is No Such Thing as TINSTAAPP
by
Jeff Long and Jeff Quinton

09-04

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16

Pitching Backward: On Manager Analysis
by
Jeff Long

08-27

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2

Pitching Backward: Casey Fien Makes No Sense
by
Jeff Long

08-20

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14

Pitching Backward: Cribs, Dorms, Ballplayers in Swarms
by
Jeff Long

08-17

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0

Pitching Backward: Mother May I?
by
Jeff Long

08-09

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0

Pitching Backward: The Next Collin McHugh?
by
Jeff Long

07-31

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0

Transaction Analysis: Relievers, Left and Right
by
R.J. Anderson, Christopher Crawford, Matthew Trueblood, Jeff Long, Dustin Palmateer and Kate Morrison

07-23

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8

Pitching Backward: Spin That Curveball
by
Jeff Long

07-14

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3

Pitching Backward: Advance Scouting the All-Star Game: American League
by
Jeff Long

07-14

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0

Pitching Backward: Advance Scouting the All-Star Game: National League
by
Jeff Long

07-03

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4

Pitching Backward: Manny Happy Returns
by
Jeff Long

06-30

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8

Pitching Backward: Chaz Roe and the Mechanics of Aesthetics
by
Jeff Long

06-17

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8

Pitching Backward: Reflections on a Golden Changeup
by
Jeff Long

06-12

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0

Pitching Backward: To Rush or Not to Rush
by
Jeff Long

05-28

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Pitching Backward: Started From the Bottom, Added a Forkball, How About Now
by
Jeff Long

05-21

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1

Pitching Backward: How Offense is Created
by
Jeff Long

05-11

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8

Pitching Backward: Who'll Throw The Greatest Pitch Ever?
by
Jeff Long

05-01

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0

Pitching Backward: PITCHf/xing and Pitcher Fixing
by
Jeff Long

04-23

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24

Pitching Backward: Every Player In Its Right Place
by
Jeff Long

04-10

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1

Pitching Backward: The Future of Leadership
by
Jeff Long

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April 12, 2017 6:00 am

Tools of the Trade

2

Jeff Long

Reviewing Ball Coach, the new, more accessible radar gun from Pocket Radar.

With the most recent version of its Ball Coach radar unit, Pocket Radar set out to do one thing: make radar accessible. As Steve Goody, the CEO of Pocket Radar, put it:

The radar gun hadn’t really been touched in 50 years because it’s a hard thing to do from scratch, so we quickly identified it as a place where we could innovate. Convenience was a big thing for us.

Read the full article...

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...

Diving into the treasure trove of data finds as many questions as answers.

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Can a $3,000 investment revolutionize amateur baseball and maybe interest MLB teams as well?

On a warm Saturday morning I pulled into the parking lot of a big industrial warehouse in Knoxville, TN. Driving past row after row of cars for an “inflatable party zone” I made my way to the side of the building, parking between a set of stairs and a long set of rusty railroad tracks. Tucked back in the corner of this industrial complex, just a short drive from the University of Tennessee and the vibrant downtown of Knoxville, was a hidden jewel for baseball nerds like myself.

I'd arrived at RBI Baseball, a premier facility where amateur athletes pursue improvement in the hopes of perfection. It’s here, and places like it across the country, where forward-thinking coaches pursue the future of amateur player development. More on that in a bit, though.

Read the full article...

Buck Showalter and the Orioles may have lost more than just one game with Zach Britton on the sidelines.

As writers and analysts, we often discuss, with the benefit of convenient removal from the situation, the merits of decision-making within major-league organizations. We wonder why a GM makes a certain trade or if the owner pushed for a particular player to be signed. We critique lineups and defensive positioning. We lampoon bunt proponents, and loathe bullpen mismanagers.

We do all of this, of course, because we know better. We have data and proofs and theories and algorithms. And more often than not, we’re not wrong. We might overstate the magnitude of these transgressions, or make a minor mistake seem like a life-or-death decision. This is why it’s so easy to criticize Buck Showalter for his decision-making related to inarguably the most dominant pitcher in baseball this season. Matthew Trueblood put it exceptionally well in his postgame recap:

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Diving into the treasure trove of data finds as many questions as answers.

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It's not enough to have a database. Teams need to have a named database.

Humans name everything. We name our progeny, our pets, our cars, our software, our hardware, our boats, and many other things. It is believed that people have named things, inanimate or not, in order to assert their dominance over the object in question. Since there have been tools and machines people have been giving them names. Peter McClure of the Oxford English Dictionary suggest that machines are named for two reasons. The first being that it gives the owner/operator some sense of ownership over the machine and the second being purely anthropomorphic in nature.

The idea being that these machines are helpful and so we bestow names upon them, which allows us to greater appreciate their contributions to our work and goals. It’s all about comfort for the owner. Adrienne LaFrance of The Atlantic had a prescient observation in a profile of this phenomenon:

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Exploring a hypothetical: What if Zach Britton, extreme batted-ball outlier, didn't strike anybody out?

Hypotheticals are fun. If they weren’t fun, nobody would put any time into thinking about them because, well, they’re hypothetical. Recently, hypothetical scenarios have gotten a lot of press, what with Lebron learning handball, and Tim Tebow figuring out how to waste the time of scouts.

It was that sort of thinking that led the BP Stats team down an interesting path on the afternoon of August 24th. The question at the heart of the matter was equal parts absurd and vexing:

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The real world doesn't care about partial wins.

It’s that time of year again. That last week in July when we swear that teams lose all sense of things big and small and ship otherwise valuable prospects in exchange for late-inning relievers who will pitch a few dozen innings over the balance of the season. It’s a formula that the sabermetric community sometimes finds difficult to rationalize. Relievers pitch so few innings and are so volatile that their value is almost certainly lower than that of the prospects dealt for them.

It’s difficult to look at the trade returns for late-game specialists and understand the thought process. The Cubs seemingly traded a king’s ransom to acquire Aroldis Chapman, a pitcher whose performance is only marred by the domestic violence charges that hang over him. Let’s not mince words either; that marring very real and deserved. For the purposes of this article, though, we're be ignoring that component of this trade, not because it doesn’t matter—it matters immensely—but because it didn’t dramatically diminish his value in the baseball world, which is what this article is about.

Read the full article...

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