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Articles Tagged Ground Balls 

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April 19, 2012 3:00 am

Raising Aces: Downhill from Here

12

Doug Thorburn

How much does pitching on a downhill plane affect a pitcher's ability to get ground balls?

Here we are in the middle of the Information Age, with access to more data than the human mind can possibly process, and yet the dissemination of baseball information has been muted by a language barrier. Baseball fans are becoming increasingly savvy about the nuances of the game, with sophisticated analytical tools at their disposal, but access to the dynamics of play on the field is often clouded by a filter of scout-speak. If we were playing poker, then the dealer would need to remind the scouts in seats eight and nine of the “English only at the table” rule in order to prevent them from trading secrets that fly under the radar of other players. 

There are dozens of entries in the pitching section of the scout-speak dictionary, from “command” and “control” to “arm action.” One of these buzzwords is “downhill plane,” a term that refers to pitch trajectory that has a steep slope on its approach toward the hitter. It seems to follow that pitchers who possess a high release point would induce a higher rate of ground balls. The logic behind the idea is simple enough, as anyone who has thrown a tennis ball against a wall can attest, but the statistical evidence paints a different picture.

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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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January 17, 2011 10:00 am

Ahead in the Count: Situational Pitching

0

Matt Swartz

Are pitchers able to apply certain skills when a game calls for it?

One of the pitchers I enjoyed watching the most while I was growing up was Tom Glavine. Even though I was a Phillies fan and frequently saw him victimize my favorite team, I was impressed by the expertise he demonstrated on the mound, and how he perfected his craft. Glavine remains the premier example of a pitcher who out-pitched his peripheral statistics; he was greater than the sum of his parts. For the amount of strikeouts, walks, and ground balls that Glavine got in his career, he should never have been able to keep runs off the scoreboard as well as he did.

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December 15, 2010 9:00 am

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

27

Matt Swartz

Ground-ball pitchers have several skills that traditional statistics do not account for.

There are two more important reasons why Skill-Interactive Earned Run Average's (SIERA) is so successful at predicting the following year's ERA. First, most other Defense-Independent Pitching Statistics, like FIP and xFIP, assume that pitchers have no control over their Batting Average on Ball in Play (BABIP), but we know that they do have some control. I have shown before that pitchers with high strikeout totals and low ground-ball rates tend to allow fewer hits per ball in play, and thus lower BABIPs. Of course, BABIP is subject to so much luck that it is nearly impossible to discern a pitcher's true ability to prevent hits on balls in play from his historical BABIP. That is why last year's FIP is much better at predicting this year's ERA than last year's ERA is. It strips ERA of BABIP (and sequencing) altogether and assumes league-average BABIP for all pitchers and random sequencing.

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September 17, 2010 8:00 am

Ahead in the Count: High BABIPs and True Skill Level

13

Matt Swartz

Look at which direction some hitters with high batting averages on balls in play are likely headed in 2011.

Last week, I discussed several pitchers who were pitching well in front of or well behind their peripherals using SIERA. This week, I will discuss several hitters who have particularly high BABIPs, and how much of that performance is skill versus luck.

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September 10, 2010 8:00 am

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

19

Matt Swartz

A look at some pitchers who have had good luck this season and some who haven't.

When Eric Seidman and I introduced SIERA in February, we were very careful to show that it predicts future ERA better than current ERA does. While Defense Independent Pitching Statistics are not a foolproof way to measure pitchers, using them as a guide to dig further into the numbers can be very helpful. Last October, I spent a couple articles analyzing Cole Hamels’ performance, and I highlighted how little was different between his 2008 and 2009 season, and how I expected his performance to improve as his luck neutralized. Sure enough, Hamels has seen his ERA fall back toward 2008 levels in 2010. In June, I disappointed Rockies fans by explaining the luck that had led to Ubaldo Jimenez’s 1.16 ERA at that time. Sure enough, he has a 4.36 ERA since that article was posted. Eric and I wrote on the Diamondbacks’ starters, stressing the bad luck that Dan Haren had seen to that point in the season. He had a 5.35 ERA, but it has been 3.59 since that article was posed and Haren has also been traded to the Angels. My point is not to cherry pick successes, but to prove that this type of analysis works. I certainly cannot be right every time I say a pitcher’s ERA is likely to fall or rise, because luck plays a role in pitching to a very large degree and luck by its very nature can reoccur. However, this type of analysis will prove prophetic more often than not.

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An attempt to construct a defensive metric without bias.

I have been making something of a ruckus recently about where I feel the state of current defensive analysis is. I have been long on listing problems, and short on proposing solutions.

Well, allow me to make amends there. I don’t pretend to have the problem solved. I’m not sure any of us will ever see it truly solved. But I think—or at least, hope—this can point us in the right direction.

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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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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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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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