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What will happen to baseball statistics as X approaches zero?

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Exploring the last stop for Denton True Young, and looking at the advanced statistics of some of your syndicated television viewing options.

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March 2, 2017 9:34 am

Fantasy Freestyle: Robbie Ray and Statcast


Tim Finnegan

Pitching metrics disagree when it comes to the D'backs lefty, but does his Statcast data reveal his true skill level?

Robbie Ray has been much talked about by both fantasy owners and baseball fans. Sam Miller profiled Ray fantastically in this ESPN piece from November. Depending on which pitching stats you place the most emphasis on, Ray’s 2016 season varied anywhere from significantly below average (run prevention) to above average (Fielding Independent Pitching, Deserved Run Average). Ray’s 4.90 ERA ranked fourth-worst among the 64 starting pitchers who threw at least 170 innings last year, and his park-adjusted ERA- of 112 ranked 54th out of 64. His 3.88 DRA ranked 31st. His 3.76 FIP ranked 21st.

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January 4, 2017 6:00 am

Fantasy Freestyle: Using Statcast Analytics for Fantasy Decisions


Tim Finnegan

How exit velocity, spin rate, and other new metrics can help guide your player choices this season.

As a fantasy owner, I like to look at what the progressive front offices inside the game are doing in their player evaluation. What are the brightest minds in baseball doing to pick players? I often find concepts and ideas that I carry over to my player evaluation in my leagues to help me construct my fantasy rosters.

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'If 20 years ago, baseball understanding was a puddle, it's now a mineshaft, not a pool.'

Last Tuesday, the Phillies’ official Twitter made a pair of tweets. It made a number of tweets, including this specific pair of tweets:

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The Pirates' struggles this year might have more to do with the entire rest of the league than Pittsburgh's pitchers themselves.

Francisco Liriano has been a Pittsburgh Pirates success story. Signed as a free agent for $1 million after compiling a 5.34 ERA, 4.29 FIP, and 4.02 DRA in 156 2/3 innings split between the Twins and White Sox in 2012, he became a hero in Travis Sawchik’s book about the 2013 Pirates and their embrace of analytics, Big Data Baseball. In Liriano’s case, the approach was to junk his four-seam fastball, focus on his sinking two-seam fastball, and generate a lot of groundballs for shifted Pirates infielders to gobble up. The success of this strategy was evident through last year:

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What you need to know before your sweeping take about a player's exit velocity.

Note: Baseball Prospectus has removed the leaderboards mentioned in this article. Thank you for your interest in our work and for your patience as we attempt to resolve this issue.

Last year, the folks at MLB Advanced Media started publishing what is commonly described as “exit velocity”: the pace at which the baseball is traveling off the bat of the hitter, as measured by the new Statcast system.

As a statistic, exit velocity is attractive for several reasons. For one thing, it is new and fresh, and that’s always exciting. It also makes analysts feel like they are traveling inside the hitting process, and getting a more fundamental look at a hitter or pitcher’s ability to control the results of balls in play.

However, we’ve seen many people take the raw average of a player’s exit velocities and assume it to be a meaningful indication, in and of itself, of pitcher or batter productivity. This is not entirely wrong: Raw exit velocity can correlate reasonably well with a batter’s performance.

But this use of raw averages also creates some problems. First, if you use exit velocity as a proxy of player ability, then you must also accept that one player’s exit velocity is a function of his opponents, be they a batter or pitcher. Put more bluntly, a player’s average exit velocity is biased by the schedule of the player’s team.

Second, and much more importantly, we have concluded Statcast exit velocity readings, as currently published, are themselves biased by the ballpark in which the event occurs. This goes beyond mere differences in temperature and park scoring tendencies. In fact, it appears that the same player generating the same hit will have its velocity rated differently from stadium to stadium, even if you control for other confounding factors.

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The weirdest thing happened to the Reds' ace. Meanwhile, good job Chris Sale, good job Orioles, good job Jordan Zimmermann, great job Aaron Hicks.

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And bring on the partial hit.

Enrique Hernandez worked hard for first base in the seventh inning in San Diego on Tuesday. Though there were two outs and nobody on, and although the Dodgers led 3-0, and although Hernandez was facing Kevin Quackenbush (a right-handed reliever, which is usually enough to neutralize Hernandez), and although Hernandez was just coming off the bench to take a low-leverage plate appearance in the pitcher’s spot, he hung in there. He fell behind 0-2, but then took a pair of tough pitches, fouled off a good fastball on the outside corner, fouled off a good curveball that ended up just below his knees. On a 2-2 count and the seventh pitch of the plate appearance, Quackenbush left a fastball over the middle of the plate (though it was above Hernandez’s letters), and Hernandez hit it hard. Specifically, he hit it 102.8 miles per hour, according to Baseball Savant. Unfortunately, it was right at shortstop Alexei Ramirez. Ramirez gathered in the sizzling topspin grounder, set himself… and threw low to first base. Wil Myers couldn’t pick the short hop, and Hernandez had his base.

Of course, Hernandez won’t get credit for that anywhere. The play was a textbook throwing error, and it was called that way, so despite his tough at-bat and very hard contact, Hernandez got an 0-for-1 on the night.

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September 21, 2015 6:00 am

Painting the Black: The Aroldis Profession


R.J. Anderson

Why doesn't Aroldis Chapman get groundball double plays?

Say you were tasked with creating a pitcher who must lead the majors in double plays over the course of a simulated season, or else aliens would destroy Earth. What attributes would you provide him if you were limited to three at most? You'd probably start by making him an extreme groundball pitcher, for reasons that are easily understood. Then you'd ensure he held baserunners well, so he'd keep all his double-play chances in order. What else? How about controlling his quality of contact? You definitely wouldn't want a bunch of hard-hit balls that leak through the infield, but you wouldn't want him dealing exclusively in tappers and high-choppers either, lest you get only one out instead of two.

The last part sounds counterintuitive—weak contact is good contact—but consider the case of Aroldis Chapman, who is, for all intents and purposes, the antithesis of the pitcher created above. Chapman is such a non-threat to lead the majors in double plays (even on a rate basis) that he hasn't coerced a standard groundball double play in more than a year. Here's his most-recent one, from August 1, 2014:

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August 17, 2015 6:00 am

Pitching Backward: Mother May I?


Jeff Long

Could Trevor May be a hidden gem? What improvements does he need to make to be successful?

For the last month or so we've been going through Statcast data and applying it to player evaluation, much like the Astros purportedly did in acquiring Collin McHugh. Alan Nathan helped me introduce some work that took an in-depth look at pitch spin. We explored the concepts of gyrospin versus useful spin, a topic that Nathan wrote about previously here at BP. Last week we revealed part one of applying that analysis, wherein Tyler Lyons was identified as a breakout candidate based on his slider spin rate and opportunities for improvement in his other offerings.

This is part two of the application, this time focusing on curveballs rather than sliders. After all, McHugh's curveball was supposedly a big part of the reason the Astros fell in love with him. Of course, the application of this analysis is contingent on understanding how exactly McHugh went from riding the waiver wire to a top-50 pitcher in baseball. From last week's article:

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Statcast gives us a view on pitch spin that we haven't had before

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