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05-27

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Fantasy Freestyle: Batted-Ball Trajectory and BABIP Overachievers
by
Wilson Karaman

04-20

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Fantasy Freestyle: Batted-Ball Profile and Team Defensive Context, Part 2
by
Wilson Karaman

04-13

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2

Fantasy Freestyle: Batted Balls and Team Defensive Context
by
Wilson Karaman

07-09

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11

Pebble Hunting: The Blind BABIP Test: Results and Revelations
by
Sam Miller

11-22

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30

Spinning Yarn: How Does Quality of Contact Relate to BABIP?
by
Mike Fast

11-16

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41

Spinning Yarn: Who Controls How Hard the Ball is Hit?
by
Mike Fast

06-23

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1

Manufactured Runs: Batted Balls
by
Colin Wyers

03-23

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26

Ahead in the Count: Predicting BABIP, Part 1
by
Matt Swartz

09-15

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17

Ahead in the Count: The BABIP Superstars
by
Matt Swartz

10-16

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Future Shock: Monday Morning Ten-Pack
by
Kevin Goldstein

10-16

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Prospectus Today: LCS, Day Six
by
Joe Sheehan

10-14

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Future Shock: Where Did the Tigers and the Athletics Come From?
by
Kevin Goldstein

10-14

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Prospectus Today: LCS, Day Four
by
Joe Sheehan

10-14

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Playoff Prospectus: The Best and Worst of Mets and Cardinals Postseason Pitching
by
Jim Baker

10-13

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Prospectus Today: LCS, Day Three
by
Joe Sheehan

10-12

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Prospectus Today: The Games Go On
by
Joe Sheehan

10-12

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Player Profile
by
Marc Normandin

10-11

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Remembering Buck O'Neil
by
Alex Belth

10-11

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Prospectus Today: LCS, Day One
by
Joe Sheehan

10-09

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Completely Random Statistical Trivia
by
Keith Woolner

10-09

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Prospectus Today: Division Series, Day Six
by
Joe Sheehan

10-07

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Prospectus Today: Division Series, Day Four
by
Joe Sheehan

10-06

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Prospectus Today: Division Series, Day Three
by
Joe Sheehan

10-06

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Prospectus Matchups: October Musings
by
Jim Baker

10-05

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Prospectus Today: Division Series, Day Two
by
Joe Sheehan

10-12

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Call It In The Air!
by
Dave Pease

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May 27, 2016 6:00 am

Fantasy Freestyle: Batted-Ball Trajectory and BABIP Overachievers

0

Wilson Karaman

A look at several players who might be well equipped to sustain lofty batting averages on balls in play.

There’s a pretty well-established correlation between hitting the ball hard and successfully reaching base. Line drive performance tends to bear this out, insofar as batting average and slugging percentage on this kind of batted ball far outstrip the other two main batted ball types: fly and ground balls.

In the current era of advanced outfield positioning, flyballs have suffered the gravest of recessions recently. The number of fly-ball doubles and triples has declined over the past few years, driving an overall deterioration in fly-ball slugging percentage from .613 as recently as 2012 to its current .528 (which should be noted has rebounded significantly early on this year from two straight years of sub-.450 marks). Fly-ball batting average, meanwhile, remains buried well under the Mendoza line for a fourth consecutive season.

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Wilson examines a few worm-burners who benefit from the glovemen behind them and a few flyball hurlers whose outfielders cramp their style.

Last week I took a look at some groundball pitchers whose tendencies are wasted to a degree in front of poor infield defenses, as well as some flyball pitchers built fairly well for their outfield defenses and park contexts. This week (and with the added benefit of more current data!) we’ll turn the tables and look at the other half of the equation: groundball guys in good places and flyball guys in bad places. The additional week-plus of games allows us to at least peak at some of the early season trends that, while far from definitive, are at least starting to take some shape now. This won’t be nearly as helpful of a list, from the standpoint that a lot of the grounder guys are well-known and the fly ball culprits are all pretty comfortable on “Do Not Start!” lists near and far. Still, with the clearer early-season trends I think there’s some value in incorporating these returns into a list of fringier guys who may be somewhat more or less interesting given how their particular skills set jive with their supporting contexts.

Groundball Guys with Good Infield Defenses

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April 13, 2016 6:00 am

Fantasy Freestyle: Batted Balls and Team Defensive Context

2

Wilson Karaman

Examining the pitchers whose worm-burning and flyballing styles mesh best and worst with their clubs.

We tend to operate with certain assumed axioms in the world of fantasy baseball, one of which is that pitchers who generate ample groundball contact and avoid the tightrope of excessive flyball contact are preferable. And the risk-reward is certainly apparent in the numbers: Last year big-league hitters mustered just a .144 average on flyballs but slugged .443, compared to a .243/.263 line on grounders. Sure, you give up more base hits on the ground, but they tend to be singles with limited potential to really do stand-alone damage. Flyballs, on the other hand, leave yards and lead to runs.

But all pitchers, and all pitching contexts, are not created equal; there are some guys whose stellar groundball rates mean less because they pitch in front of porous infield defenses, while others who walk on the wilder side in the sky are better bets on account of stellar fly-catching troupes patrolling the grass behind them. Now, the variance here isn’t extreme for most pitchers, but it isn’t insignificant either. Major-league leader Brett Anderson induced 380 grounders last year, and had he done so in front of the most efficient infield unit (the Giants) he’d have benefitted from an extra 26 out conversions over the course of his 180 innings relative to the worst unit (Philadelphia).

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The results of the blind BABIP test are in. How did you do? And what can we learn from your answers?

On Friday, many of you took the blind BABIP test. I gave you 18 GIFs, in nine sets of two, each set comprising two batted balls. One was a hit. The other was an out. You guessed which was which, but you couldn’t see the outcome; the GIFs cut off at the frame just as contact was made, or just before contact was made. This was supposed to tell us something. I’ll get to the big result first: We’re the worst at this!

I tallied 82 full sets of answers, which is 738 individual guesses, of which 387 were correct. That is 52 percent correct. Closing our eyes and pointing would theoretically have earned us 369 correct answers.  All the wisdom of the 82 of you was worth 18 extra correct answers. So that's the big thing first.

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Mike continues his investigation of HITf/x data to glean more insights into whether pitchers can prevent hits on balls in play.

In the first part of this study, I used detailed batted ball speed information from HITf/x to examine the degree of skill that batters and pitchers had in quality of contact made or allowed. Here, I will look deeper into the question of why some batted balls fall for hits and others do not.

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When a batter and pitch face off, which has a greater effect on how hard the ball is hit, and what can that tell us about pitcher BABIP?

The last decade has seen much discussion and evolution in sabermetric thought around the relative abilities of batters, pitchers, fielders, and Lady Luck to control the outcome of batted balls. Data collected by Sportvision and MLBAM sheds new light on this question, but before we tackle that data, let’s review some of the history of how we came to our current state of knowledge.

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Park adjustments show than line-drive and fly-ball rates can be affected by the scorers.

Let’s talk about batted balls.

I’m sure we’re all familiar with the category labels that we use to describe batted balls—ground balls, line drives, fly balls, and popups. Precise definitions vary, but David Cortesi gives a succinct set of criteria:

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March 23, 2010 10:42 am

Ahead in the Count: Predicting BABIP, Part 1

26

Matt Swartz

BABIP isn't as luck-driven as many suggest, not after you drill down into the numbers.

If you don’t put your bat on the ball, you’re not going to get a hit, and if you don’t hit the ball over the wall, someone might catch it. This series begins with what happens the rest of the time as I develop a model to predict a hitter’s Batting Average on Balls in Play (BABIP). In Part 2, I will explain some of the current BABIP superstars then some of the players where my system differs from PECOTA will be the topic of Part 3.

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September 15, 2009 2:24 pm

Ahead in the Count: The BABIP Superstars

17

Matt Swartz

Projecting who's reliably good at getting happy outcomes on balls in play.

It is well known that pitchers have little control over Batting Average on Balls In Play (BABIP), and that an easy way to know a pitcher is due to improve or regress is to look at his BABIP and see whether it is significantly different than the league average of about .300. If he has surrendered hits on balls in play at a rate significantly above .300, he is probably going to see that come down, and if his BABIP is significantly below .300, he is probably going to return from whatever depths. It is also well known that hitters have more control over their BABIP, but not that much; a starting position player will hit about 500 balls in play per season, meaning that the standard deviation of his yearly BABIP is probably about .020, meaning that much of observed differences in hitter BABIP are fleeting, as one-third of players' BABIP marks belie their true skill level by .020 points or more. Even still, there are many hitters who consistently put up high BABIP rates.

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October 16, 2006 12:00 am

Future Shock: Monday Morning Ten-Pack

0

Kevin Goldstein

Kevin checks out the newsmakers in the winter leagues.

\nMathematically, leverage is based on the win expectancy work done by Keith Woolner in BP 2005, and is defined as the change in the probability of winning the game from scoring (or allowing) one additional run in the current game situation divided by the change in probability from scoring\n(or allowing) one run at the start of the game.'; xxxpxxxxx1160988517_18 = 'Adjusted Pitcher Wins. Thorn and Palmers method for calculating a starters value in wins. Included for comparison with SNVA. APW values here calculated using runs instead of earned runs.'; xxxpxxxxx1160988517_19 = 'Support Neutral Lineup-adjusted Value Added (SNVA adjusted for the MLVr of batters faced) per game pitched.'; xxxpxxxxx1160988517_20 = 'The number of double play opportunities (defined as less than two outs with runner(s) on first, first and second, or first second and third).'; xxxpxxxxx1160988517_21 = 'The percentage of double play opportunities turned into actual double plays by a pitcher or hitter.'; xxxpxxxxx1160988517_22 = 'Winning percentage. For teams, Win% is determined by dividing wins by games played. For pitchers, Win% is determined by dividing wins by total decisions. '; xxxpxxxxx1160988517_23 = 'Expected winning percentage for the pitcher, based on how often\na pitcher with the same innings pitched and runs allowed in each individual\ngame earned a win or loss historically in the modern era (1972-present).'; xxxpxxxxx1160988517_24 = 'Attrition Rate is the percent chance that a hitters plate appearances or a pitchers opposing batters faced will decrease by at least 50% relative to his Baseline playing time forecast. Although it is generally a good indicator of the risk of injury, Attrition Rate will also capture seasons in which his playing time decreases due to poor performance or managerial decisions. '; xxxpxxxxx1160988517_25 = 'Batting average (hitters) or batting average allowed (pitchers).'; xxxpxxxxx1160988517_26 = 'Average number of pitches per start.'; xxxpxxxxx1160988517_27 = 'Average Pitcher Abuse Points per game started.'; xxxpxxxxx1160988517_28 = 'Singles or singles allowed.'; xxxpxxxxx1160988517_29 = 'Batting average; hits divided by at-bats.'; xxxpxxxxx1160988517_30 = 'Percentage of pitches thrown for balls.'; xxxpxxxxx1160988517_31 = 'The Baseline forecast, although it does not appear here, is a crucial intermediate step in creating a players forecast. The Baseline developed based on the players previous three seasons of performance. Both major league and (translated) minor league performances are considered.

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October 16, 2006 12:00 am

Prospectus Today: LCS, Day Six

0

Joe Sheehan

Our servers, like the Cardinals bullpen and the A's, crashed. Only two of those get to come back.

\nMathematically, leverage is based on the win expectancy work done by Keith Woolner in BP 2005, and is defined as the change in the probability of winning the game from scoring (or allowing) one additional run in the current game situation divided by the change in probability from scoring\n(or allowing) one run at the start of the game.'; xxxpxxxxx1161098296_18 = 'Adjusted Pitcher Wins. Thorn and Palmers method for calculating a starters value in wins. Included for comparison with SNVA. APW values here calculated using runs instead of earned runs.'; xxxpxxxxx1161098296_19 = 'Support Neutral Lineup-adjusted Value Added (SNVA adjusted for the MLVr of batters faced) per game pitched.'; xxxpxxxxx1161098296_20 = 'The number of double play opportunities (defined as less than two outs with runner(s) on first, first and second, or first second and third).'; xxxpxxxxx1161098296_21 = 'The percentage of double play opportunities turned into actual double plays by a pitcher or hitter.'; xxxpxxxxx1161098296_22 = 'Winning percentage. For teams, Win% is determined by dividing wins by games played. For pitchers, Win% is determined by dividing wins by total decisions. '; xxxpxxxxx1161098296_23 = 'Expected winning percentage for the pitcher, based on how often\na pitcher with the same innings pitched and runs allowed in each individual\ngame earned a win or loss historically in the modern era (1972-present).'; xxxpxxxxx1161098296_24 = 'Attrition Rate is the percent chance that a hitters plate appearances or a pitchers opposing batters faced will decrease by at least 50% relative to his Baseline playing time forecast. Although it is generally a good indicator of the risk of injury, Attrition Rate will also capture seasons in which his playing time decreases due to poor performance or managerial decisions. '; xxxpxxxxx1161098296_25 = 'Batting average (hitters) or batting average allowed (pitchers).'; xxxpxxxxx1161098296_26 = 'Average number of pitches per start.'; xxxpxxxxx1161098296_27 = 'Average Pitcher Abuse Points per game started.'; xxxpxxxxx1161098296_28 = 'Singles or singles allowed.'; xxxpxxxxx1161098296_29 = 'Batting average; hits divided by at-bats.'; xxxpxxxxx1161098296_30 = 'Percentage of pitches thrown for balls.'; xxxpxxxxx1161098296_31 = 'The Baseline forecast, although it does not appear here, is a crucial intermediate step in creating a players forecast. The Baseline developed based on the players previous three seasons of performance. Both major league and (translated) minor league performances are considered.

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Jim digs back and looks at the best starting efforts by the Mets and Cardinals in the era of divisional play.

\nMathematically, leverage is based on the win expectancy work done by Keith Woolner in BP 2005, and is defined as the change in the probability of winning the game from scoring (or allowing) one additional run in the current game situation divided by the change in probability from scoring\n(or allowing) one run at the start of the game.'; xxxpxxxxx1160845280_18 = 'Adjusted Pitcher Wins. Thorn and Palmers method for calculating a starters value in wins. Included for comparison with SNVA. APW values here calculated using runs instead of earned runs.'; xxxpxxxxx1160845280_19 = 'Support Neutral Lineup-adjusted Value Added (SNVA adjusted for the MLVr of batters faced) per game pitched.'; xxxpxxxxx1160845280_20 = 'The number of double play opportunities (defined as less than two outs with runner(s) on first, first and second, or first second and third).'; xxxpxxxxx1160845280_21 = 'The percentage of double play opportunities turned into actual double plays by a pitcher or hitter.'; xxxpxxxxx1160845280_22 = 'Winning percentage. For teams, Win% is determined by dividing wins by games played. For pitchers, Win% is determined by dividing wins by total decisions. '; xxxpxxxxx1160845280_23 = 'Expected winning percentage for the pitcher, based on how often\na pitcher with the same innings pitched and runs allowed in each individual\ngame earned a win or loss historically in the modern era (1972-present).'; xxxpxxxxx1160845280_24 = 'Attrition Rate is the percent chance that a hitters plate appearances or a pitchers opposing batters faced will decrease by at least 50% relative to his Baseline playing time forecast. Although it is generally a good indicator of the risk of injury, Attrition Rate will also capture seasons in which his playing time decreases due to poor performance or managerial decisions. '; xxxpxxxxx1160845280_25 = 'Batting average (hitters) or batting average allowed (pitchers).'; xxxpxxxxx1160845280_26 = 'Average number of pitches per start.'; xxxpxxxxx1160845280_27 = 'Average Pitcher Abuse Points per game started.'; xxxpxxxxx1160845280_28 = 'Singles or singles allowed.'; xxxpxxxxx1160845280_29 = 'Batting average; hits divided by at-bats.'; xxxpxxxxx1160845280_30 = 'Percentage of pitches thrown for balls.'; xxxpxxxxx1160845280_31 = 'The Baseline forecast, although it does not appear here, is a crucial intermediate step in creating a players forecast. The Baseline developed based on the players previous three seasons of performance. Both major league and (translated) minor league performances are considered.

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