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Dan Brooks 

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07-10

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10

Prospectus Feature: Measuring Pitcher Similarity
by
Glenn Healey, Shiyuan Zhao and Dan Brooks

04-05

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1

Prospectus Feature: Estimating Release Point Using Gameday's New Start_Speed
by
Dan Brooks and Alan M. Nathan

05-15

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0

BP Unfiltered: Saberseminar Tickets On Sale Now!
by
Dan Brooks

02-05

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33

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

04-21

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0

BP Daily Podcast: Effectively Wild Episode 432: The Weekend of Unwritten Rules
by
Ben Lindbergh and Dan Brooks

04-08

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0

BP Unfiltered: Mark Appel: A PITCHf/x First Look
by
Dan Brooks

03-03

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47

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

01-29

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28

Throw the Flag
by
Dan Brooks and Russell A. Carleton

01-16

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2

BP Unfiltered: RISPy Business
by
Dan Brooks

09-23

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7

Pebble Hunting: Pedro Hernandez and the Rashomon Project
by
Sam Miller, R.J. Anderson, Dan Brooks and Dan Rozenson

10-05

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3

Advance Scout
by
Dan Brooks

09-27

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3

Baseball Prospectus News: Announcing the PITCHf/x Matchup Analysis Tool
by
Dan Brooks

09-14

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2

Baseball Prospectus News: Introducing Pitch Sequence Visualizations
by
Daniel Mack and Dan Brooks

08-30

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1

BP Unfiltered: Is Stephen Strasburg Wearing Down?
by
Dan Brooks

08-17

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4

BP Unfiltered: Clay Buchholz Does the Splits
by
Dan Brooks

08-14

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11

PITCHf/x Profile: Dissecting the Decline of Josh Beckett
by
Dan Brooks

08-10

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1

BP Unfiltered: A PITCHf/x Companion Piece on Jered Weaver
by
Dan Brooks

08-09

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1

BP Unfiltered: Yu Darvish with Two Strikes
by
Dan Brooks

08-08

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9

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

07-23

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0

BP Announcements: Sabermetrics, Scouting, and the Science of Baseball
by
Dan Brooks

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

06-19

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4

BP Unfiltered: Knuckleballing to the Count
by
Dan Brooks

04-30

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6

BP Unfiltered: Sabermetrics, Scouting, and the Science of Baseball
by
Dan Brooks

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Creating a tool that considers the speed and movement of every pitch, the similarity measure allows the direct comparison of pitchers across various contexts.

The PITCHf/x optical video and TrackMan Doppler radar sensors estimate parameters of pitches, including the speed, horizontal movement and vertical movement. The data recorded by these systems can be used to develop pitcher similarity measures. These measures are valuable not only for comparing major-league pitchers to each other, but also for allowing the direct comparison of pitchers in other leagues (minor, amateur and foreign) to their MLB counterparts.

A pitcher similarity measure can be employed for multiple purposes by analysts. The identification of groups of similar pitchers can be used to generate optimized projection models [18], or to generate larger samples for predicting the outcome of batter/pitcher matchups [3], [20]. In addition, a similarity measure allows for individual pitchers to be monitored over time in order to detect possible changes in pitch characteristics, health and throwing mechanics.

Previous methods for quantifying pitcher similarity have been limited to the comparison of pitches of the same type, which makes these methods highly dependent on the outcome of pitch-classification algorithms. Kalk [8], [9] developed a similarity measure that compared pitches of the same type using variables that included pitch frequency, speed and movement. Loftus [11], [12], [13] improved on Kalk's approach by separating pitchers by handedness while using the Kolmogorov-Smirnov distance to compare distributions. Like Kalk's method, however, this approach only considers comparisons between pitches of the same type.

A difficulty for these methods is that different pitch types for a single pitcher or across multiple pitchers can have similar properties. This causes the pitch-frequency statistics used by similarity algorithms to depend heavily on the classification process; it also prevents the comparison of similar pitches that are classified as different pitch types.

In 2016, for example, Ubaldo Jimenez's sinker averaged 91.12 mph, -7.35 inches of horizontal movement and 8.53 inches of vertical movement, while Jeremy Hellickson's four-seam fastball had nearly identical averages of 90.81 mph, -7.63 inches of horizontal movement and 8.44 inches of vertical movement. Due to this issue, Loftus [13] conceded that his own method is best suited for comparing individual pitches as opposed to comparing pitchers based on their entire arsenal. Gennaro [3] has proposed a more qualitative approach to measuring pitcher similarity by using a hand-selected set of features and weightings. The features used by this method include a pitcher's two most-common pitch types and his most-common two-pitch sequence.

In this work, we develop a pitcher similarity measure that considers the speed and movement of every pitch. We note that other factors that are less indicative of a pitcher's raw stuff such as pitch location [4], sequencing [5], and deception [14] also play a role in determining performance.

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Summarizing the history of the start_speed parameter, including a cautionary note to new pitch-tracking researchers, and describing a method for estimating release point (extension) by taking advantage of Gameday's parameter switch.

According to Dave Cameron and recently confirmed in a blog post by Tom Tango, MLB has changed the meaning of start_speed, a pitch-by-pitch parameter in the MLB Components Data ("Gameday") Files. This brief post summarizes the history of the start_speed parameter, includes a cautionary note to new pitch-tracking researchers, and describes a method for estimating release point (extension) by taking advantage of Gameday’s parameter switch.

The parameter start_speed has, for the better part of 10 years, coded for the velocity at a fixed distance 50 feet from home plate. Although 50 feet is much too close to home plate to actually be a realistic guess at a pitcher release point, this distance was initially chosen to reasonably match the velocities reported by scout’s radar guns. Several websites (including BP and BrooksBaseball.net) quickly realized that 55 feet was actually a better estimate for pitcher release point, and so have used that as convention for much of the PITCHf/x era. Due to technical limitations of the PITCHf/x system, it was not possible to record the actual release point of the pitch, which limited the ability of the system to determine the actual speed at release.

Trackman Doppler Radar, which serves as the pitch tracking hardware for the new MLB Statcast system, has the advantage of being able to measure the actual release point of the ball—and the speed at that point—with excellent fidelity.

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The annual event is a stathead's dream.

Tickets are now on sale for Sabermetrics, Scouting, and the Science of Baseball 2015. They can be purchased through Ticketleap at: http://saberseminar.ticketleap.com/ss15/

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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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Ben and Dan discuss the weekend's news and transactions, including a trio of unwritten rules violations.

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2013 first overall pick Mark Appel made an exhibition start in Houston, and PITCHf/x was watching.

Earlier this week we got our first look from a pitch tracking system at Mark Appel, the first overall selection in the 2013 draft and one of Houston’s (and baseball’s) top prospects. The data come from a preseason exhibition contest that was played on the final day of spring training—but because it was played in Houston, and because the PITCHf/x cameras were operational and outputting information, we got some stats to supplement the scouting reports we’ve read.

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The best blockers and receivers, revealed.

[T]he expected runs produced from each plate appearance starting with a strike decreases by .029 runs and increases by .040 for every ball thrown on a first pitch. In other words, having as many of those 0-0 'striballs' called strikes can greatly impact the outcome of the game.

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January 29, 2014 6:04 am

Throw the Flag

28

Dan Brooks and Russell A. Carleton

Could the manager challenge system sink expanded instant replay?

About that instant replay system that MLB put in place—we found a little problem with it. It started with us asking a pretty easy question. What is the best strategy for a manager to use in deciding when to throw “the flag” to challenge a call? We were sitting around talking about it, and the answer that we came up with is actually kinda scary: Managers should just throw that flag for any close play, the first time that they see one. When we say any close play, we mean just about anything that they have a smidgen of belief could be overturned by consulting a replay. And they shouldn’t fear throwing it even in the first inning, or throwing it to contest something that would give them only a trivial advantage.

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With the Pitch Usage tool on BrooksBaseball, you can see how certain pitchers vary their offerings depending on the situation.

I’m writing this blog post with the knowledge that a lot of people reading this, especially those who are “inside” baseball, will be shaking their heads at the monitor once they finish. We're used to dismissing RISP statistics because the sample sizes are too small. In this case, though, we appear to see real and meaningful differences.

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Four authors used different methods to watch the same pitcher make the same start. These are their reviews of that pitcher's performance.

In the movie Rashomon, a samurai is murdered. Four witnesses give four accounts of the murder, and out of one scenario come four very different narratives and three different killers. Do more angles get you closer to truth, or further from it? It's not clear.

What follows is an experiment. Four of us took a starter that none of us knew anything about: Pedro Hernandez, a Twins lefty making his 12th career start, on Saturday against the A’s. Without doing any research on Hernandez, the four of us watched the start from four different angles:

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October 5, 2012 5:26 am

Advance Scout

3

Dan Brooks

Even if you figure out what Darvish has done, you might not know what he's about to do.

Today brings baseball’s first wild-card play-in games. It also brings another baseball first: Yu Darvish’s first start against the Baltimore Orioles, scheduled to get underway at 8:37 PM ET.

You can bet that the prospect of facing Darvish for the first time in a high-stakes game has the Orioles worked into an advanced scouting frenzy. Their season—a magical one, at that—hinges on their ability to analyze (and effectively attack) a pitcher whom their hitters have never seen.

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A new way to visualize and analyze every batter-pitcher matchup from the PITCHf/x era.

Just in time for the playoffs, we’re bringing you a way to get detailed information on every batter-pitcher matchup via our new Matchup Analysis Tool, found here and also accessible through the “PITCHf/x Matchups” dropdown link on the “Statistics” tab of the navbar at the top of the page.

The Matchup Analysis Tool allows you to select a particular pitcher and batter and visualize every time they’ve faced each other during the PITCHf/x era (partial 2007, complete 2008-2012). As an example, let’s take Prince Fielder vs. CC Sabathia.

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