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Articles Tagged Ground-ball Rate 

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06-05

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0

Overthinking It: Derek Lowe's Deadball Era
by
Ben Lindbergh

04-19

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12

Raising Aces: Downhill from Here
by
Doug Thorburn

11-22

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30

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

01-17

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Ahead in the Count: Situational Pitching
by
Matt Swartz

01-03

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2

Between The Numbers: Ground-ball Rates in the Minors and Majors
by
Clay Davenport

12-15

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27

Ahead in the Count: Ground-ballers: Better than You Think
by
Matt Swartz

10-10

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Playoff Prospectus: Sunday LDS Pitching Matchups
by
Matt Swartz

10-07

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Playoff Prospectus: Thursday LDS Pitching Matchups
by
Matt Swartz

09-17

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13

Ahead in the Count: High BABIPs and True Skill Level
by
Matt Swartz

09-10

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19

Ahead in the Count: The Biggest ERA-SIERA Divides of 2010
by
Matt Swartz

07-22

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6

Changing Speeds: Cold Fusion
by
Ken Funck

06-23

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1

Manufactured Runs: Batted Balls
by
Colin Wyers

06-22

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20

Expanded Horizons: Popups Get Me Through The Night
by
Tommy Bennett

05-28

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2

Fantasy Beat: Weekly Planner #9
by
Craig Brown

03-25

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26

Ahead in the Count: Predicting BABIP, Part 3
by
Matt Swartz

03-24

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33

Ahead in the Count: Predicting BABIP, Part 2
by
Matt Swartz

03-23

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26

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

02-08

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52

Introducing SIERA
by
Matt Swartz and Eric Seidman

09-29

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9

Ahead in the Count: Pitcher BABIP by Count
by
Matt Swartz

09-15

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17

Ahead in the Count: The BABIP Superstars
by
Matt Swartz

04-16

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17

Checking the Numbers: Keeping Rare Company
by
Eric Seidman

04-09

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8

Checking the Numbers: Cliff and the Gang
by
Eric Seidman

10-12

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Prospectus Today: The Games Go On
by
Joe Sheehan

10-09

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Completely Random Statistical Trivia
by
Keith Woolner

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

10-05

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Prospectus Today: Division Series, Day Two
by
Joe Sheehan

02-06

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From The Mailbag: Special Edition: Pitching and Defense
by
Voros McCracken and Keith Woolner

10-12

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Call It In The Air!
by
Dave Pease

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June 22, 2010 9:00 am

Expanded Horizons: Popups Get Me Through The Night

20

Tommy Bennett

No one can get a batter to hit a home run in an elevator shaft better than Jered Weaver.

The count is 2-2, and the pitcher sets, winds, and delivers. It's a high fastball, and the batter swings—

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May 28, 2010 3:27 pm

Fantasy Beat: Weekly Planner #9

2

Craig Brown

Tons of options this week with many pitchers making two starts who should be available in mixed leagues.

It feels like a virtual buffet of starting pitching as there are quite a few opportunities this week await owners who can juggle their rotations.  If you are in a head to head league and don’t have start limits, this could be a week where you obtain a serious pitching advantage over your opponent.

As always the asterisk next to the pitcher’s name means he’s owned in less than 50% of ESPN or Yahoo leagues. The starters are provided by Heater Magazine and are subject to change.  You can download the weekly pdf file that contains this list here.

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March 25, 2010 6:06 am

Ahead in the Count: Predicting BABIP, Part 3

26

Matt Swartz

There are reasons why E-BABIP's projections don't always agree with those of PECOTA.

In Part One of this series, I updated my model for projecting BABIP with new 2009 data, and in Part Two, I explained what makes BABIP Superstars and BABIP Trouble-Makers. In this final part, I will discuss some of the hitters where my Expected BABIP (E-BABIP) projections and PECOTA’s BABIP projections differ most, and discuss which number you might want to trust. PECOTA incorporates a lot of information that my model simply does not, but the batted-ball information can be particularly important for certain hitters, and those are the ones where you should place some faith in E-BABIP.

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March 24, 2010 12:11 pm

Ahead in the Count: Predicting BABIP, Part 2

33

Matt Swartz

Who ranks among the best and worst in this seemingly unpredictable yet key metric?

In Part One of this series, I updated a model for projecting BABIP, continuing on my previous work from last year. I showed that BABIP can be predicted successfully by looking at batted-ball rates and BABIP on those individual batted-ball types.

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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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February 8, 2010 12:00 pm

Introducing SIERA

52

Matt Swartz and Eric Seidman

Unveiling a new statistic that provides a clearer picture of pitcher performance.

Baseball fans who have no use for advanced metrics can realize the flaws in evaluating pitchers by their won-lost records, but may struggle to understand the inherent flaws in the more commonly used earned run average. Henry Chadwick invented ERA in the 19th century to measure the effect of defense on pitching performance, but not until Voros McCracken explained the concept of Defense Independent Pitching Statistics (DIPS) did our understanding of the relationship between pitching and defense take a big step forward. McCracken explained that pitchers controlled the rates of whiffing, walking, and getting walloped with home runs, showing that the correlation between these statistics in consecutive years was strong. Though he inferred an ability for hurlers to control these numbers, another finding suggested little persistence in their Batting Average on Balls in Play (BABIP), leading to the conclusion that ERAs were dependent on defense (or luck), and therefore very volatile.

Armed with this information, sabermetricians began to develop methods of estimating ERA by controlling for the factors that can muddy the proverbial waters. These estimators enable the evaluation of pitching performance based on what pitchers actually control, rendering more accurate the tracking of their abilities. Watching trends in actual skills that pitchers control can help us better grasp whether shifts in ERA are the result of changes from the individual or from external factors. Since then, many competing estimators have emerged with their accompanying strengths and weaknesses. Perhaps the most popular ERA estimator is Fielding Independent Pitching (FIP), which uses the following straightforward formula: FIP = 3.20 + (3*BB - 2*K + 13*HR)/9, where the 3.20 is a constant dependent on the league and year, used to place the outputted number on the ERA scale.

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September 29, 2009 2:19 pm

Ahead in the Count: Pitcher BABIP by Count

9

Matt Swartz

Looking for patterns in BABIP variance suggests that some battles are best won at home plate.

It is well established that pitchers who have high strikeout rates one year tend to have high strikeout rates the following year, and that walk rates and ground-ball rates have similar year-to-year correlations as well. However, pitchers do not exhibit the same kind of consistency in their Batting Average on Balls In Play (BABIP). Although pitchers' BABIP depends on the defense behind them, they do not appear to control this very much. As I have pointed out before, that does not mean they do not control BABIP, but rather that they make adjustments constantly in such a way that BABIP does not exhibit any significant year-to-year correlation. The lack of year-to-year correlation in BABIP was originally discovered by Voros McCracken and discussed here at Baseball Prospectus. Even McCracken later realized that pitchers do exhibit some persistence in their BABIP, even net of team effects, but the year-to-year correlation is approximately .12, or hardly anything to trust. Given the small variations in pitcher skill at preventing BABIP as compared with the larger variations in BABIP (only two-thirds of starters will end up with a BABIP that year that is within .020 points of their true skill level), there is not much information contained in a pitcher's BABIP one year. Most of the variation is going to be luck and defense.

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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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April 16, 2009 12:18 pm

Checking the Numbers: Keeping Rare Company

17

Eric Seidman

Ground-ball percentages go up for some pitchers, unintentional passes go down for others, but both at once, almost never.

While revisiting the 2008 dominance of Cliff Lee last week, we investigated how pitchers with similarly large spikes in ground-ball rates have fared in subsequent seasons. The results weren't pretty, and showed that massive rate increases in this area have been few and far between since 1954, and a very low percentage of these pitchers have been able to sustain these higher rates. The research in no way invalidated Lee's success, but rather suggested that factors outside of a change in approach could have an influence on his 2009 performance. Hurlers intent on inducing grounders tend to follow a different set of rules when it comes to HR/FB rates and their percentage of unearned runs than do their fly-balling colleagues, and the group relies on defense more than those whose skill is missing bats. Combine all of these ingredients, and it becomes evident that even if Lee were to become just the fourth post-1994 pitcher to increase his ground-ball rate by at least eight percent and then see it fall no lower than three percent over the following three seasons-all while meeting respectable playing time qualifiers-he is still not guaranteed even a fraction of the success he experienced last season.

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April 9, 2009 11:50 am

Checking the Numbers: Cliff and the Gang

8

Eric Seidman

Do spiking ground-ball rates make for a sustainable source of improvement for the pitchers who achieve them?

Taken as a group, ground-ball pitchers are fascinating, in that they can succeed at the major league level with what might be seen as relatively average stuff. The group doesn't miss many bats, but they do tend to have the benefit of both command and control, hovering around the strike zone in order to pitch to contact, and preventing balls from being hit in the air. Ground-ball rates themselves are stable and immune to large fluctuations; take any five-year span and run an intraclass correlation-testing the year-to-year stability on the individual pitcher level-and it will likely show a strong relationship somewhere in the 0.55-0.70 range. When a large fluctuation does surface, due diligence would require asking if a change in approach or skill has been observed, instead of a knee-jerk reaction that might dismiss the statistical shift as a luck-based indicator that is bound to regress.

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October 12, 2006 12:00 am

Prospectus Today: The Games Go On

0

Joe Sheehan

The death of Cory Lidle cast a pall over the League Championship Series, but baseball marches on.

\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.'; xxxpxxxxx1160675929_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.'; xxxpxxxxx1160675929_19 = 'Support Neutral Lineup-adjusted Value Added (SNVA adjusted for the MLVr of batters faced) per game pitched.'; xxxpxxxxx1160675929_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).'; xxxpxxxxx1160675929_21 = 'The percentage of double play opportunities turned into actual double plays by a pitcher or hitter.'; xxxpxxxxx1160675929_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. '; xxxpxxxxx1160675929_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).'; xxxpxxxxx1160675929_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. '; xxxpxxxxx1160675929_25 = 'Batting average (hitters) or batting average allowed (pitchers).'; xxxpxxxxx1160675929_26 = 'Average number of pitches per start.'; xxxpxxxxx1160675929_27 = 'Average Pitcher Abuse Points per game started.'; xxxpxxxxx1160675929_28 = 'Singles or singles allowed.'; xxxpxxxxx1160675929_29 = 'Batting average; hits divided by at-bats.'; xxxpxxxxx1160675929_30 = 'Percentage of pitches thrown for balls.'; xxxpxxxxx1160675929_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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Keith checks in with all kinds of fun facts from the completed season.

\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.'; xxxpxxxxx1160407218_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.'; xxxpxxxxx1160407218_19 = 'Support Neutral Lineup-adjusted Value Added (SNVA adjusted for the MLVr of batters faced) per game pitched.'; xxxpxxxxx1160407218_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).'; xxxpxxxxx1160407218_21 = 'The percentage of double play opportunities turned into actual double plays by a pitcher or hitter.'; xxxpxxxxx1160407218_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. '; xxxpxxxxx1160407218_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).'; xxxpxxxxx1160407218_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. '; xxxpxxxxx1160407218_25 = 'Batting average (hitters) or batting average allowed (pitchers).'; xxxpxxxxx1160407218_26 = 'Average number of pitches per start.'; xxxpxxxxx1160407218_27 = 'Average Pitcher Abuse Points per game started.'; xxxpxxxxx1160407218_28 = 'Singles or singles allowed.'; xxxpxxxxx1160407218_29 = 'Batting average; hits divided by at-bats.'; xxxpxxxxx1160407218_30 = 'Percentage of pitches thrown for balls.'; xxxpxxxxx1160407218_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.

The Baseline forecast is also significant in that it attempts to remove luck from a forecast line. For example, a player who hit .310, but with a poor batting eye and unimpressive speed indicators, is probably not really a .310 hitter. Its more likely that hes a .290 hitter who had a few balls bounce his way, and the Baseline attempts to correct for this.

\nSimilarly, a pitcher with an unusually low EqHR9 rate, but a high flyball rate, is likely to have achieved the low EqHR9 partly as a result of luck. In addition, the Baseline corrects for large disparities between a pitchers ERA and his PERA, and an unusually high or low hit rate on balls in play, which are highly subject to luck. '; xxxpxxxxx1160407218_32 = 'Approximate number of batting outs made while playing this position.'; xxxpxxxxx1160407218_33 = 'Batting average; hits divided by at-bats. In PECOTA, Batting Average is one of five primary production metrics used in identifying a hitters comparables. It is defined as H/AB. '; xxxpxxxxx1160407218_34 = 'Bases on Balls, or bases on balls allowed.'; xxxpxxxxx1160407218_35 = 'Bases on balls allowed per 9 innings pitched.'; xxxpxxxxx1160407218_36 = 'Batters faced pitching.'; xxxpxxxxx1160407218_37 = 'Balks. Not recorded 1876-1880.'; xxxpxxxxx1160407218_38 = 'Batting Runs Above Replacement. The number of runs better than a hitter with a .230 EQA and the same number of outs; EQR - 5 * OUT * .230^2.5.'; xxxpxxxxx1160407218_39 = 'Batting runs above a replacement at the same position. A replacement position player is one with an EQA equal to (230/260) times the average EqA for that position.'; xxxpxxxxx1160407218_40 = 'Breakout Rate is the percent chance that a hitters EqR/27 or a pitchers EqERA will improve by at least 20% relative to the weighted average of his EqR/27 in his three previous seasons of performance. High breakout rates are indicative of upside risk.

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