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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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.
A look at three starters who illustrate the predictive strength of Baseball Prospectus' new pitching metric.
A helpful comment in one of the previous articles alerted us that our park-adjustment method was not quite correct, so we have updated the formula accordingly for this article and in the glossary. The formula only changes slightly and the tests from the previous article are very close to the same as well. However, for the sake of transparency, we are highlighting this at the beginning of the article. The new formula:
A look at the data used in creating SIERA, Baseball Prospectus' new pitching metric.
Earlier this week we introduced the run estimator SIERA, providing a general summary of its purpose as well as the evolution of its development. Today, in Part 3, our focus will shift to the quantitative side of the metric, offering a detailed look at the data used to derive the formula as well as specifics pertaining to the regression analysis techniques used. The transparency should provide a better understanding of the integrity of such a process as well as a few insights into the SIERA-laden approach towards pitcher valuations.
Building on last week's work and reader feedback, an expansion on the subject of pitcher performance in double-play situations.
In last week's column, I took an initial look at the question of whether pitchers in general-or specific pitchers-are able to successfully tailor their approach to be more effective in certain game situations, or to be exact, during double-play (DP) situations and situations with a runner on third and fewer than two outs (R3). To do this, I performed some cursory analysis of pitching data for the 2005-09 seasons to see whether ground-ball, walk, and strikeout rates differ in these situations compared to the norm. The numbers I ran for the DP situation showed an increase in ground balls (which are often, but not always, good for the pitcher in double-play situations), a decrease in strikeouts (which are always good for the pitcher in any situation) and a decrease in walk rate. Lastly, I looked at individual pitchers to get some idea of which ones improved the most during the DP situation, based on a quick and dirty measure I called PRIDE, which summed the changes in ground-ball and K rates and subtracted the change in walk rate.
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.
An initial look at the extent of the home-field advantage in terms of its incidence on in-game results.
In every sport and at every level, the home team wins more games than the visiting team. While this is true in baseball, it's less the case than in other sports. Throughout baseball history, the home team has won approximately 54 percent of the games played. Nearly every aspect of the game has changed drastically over the last century, but home-field advantage has barely changed at all. Consider the home-field advantage in each decade since 1901:
But will it also stand for victory at some point in the reliably contentious argument over the Captain's fielding skills?
This week's installment of YCLIU is a follow-up to the Derek Jeter-inspired piece on aging shortstops that ran in this space on March 31. This time around, we doff the historian's cap for a look at the problem of evaluating shortstop defense. For those of us in New York, this is an evergreen topic, because Jeter is a subject of a tug of war between those who want to celebrate him as a future Hall of Famer, albeit one with flaws, and those who want to canonize him, refusing to admit the possibility of weakness in any phase of his game.
Fueled by reader feedback, Dan makes some adjustments to his new defensive metric.
"The subject may appear an insignificant one, but we shall see that it possesses some interest, and the maxim 'de minimis lex non curat'--the law is not concerned with trifles--does not apply to science." --Charles Darwin, from the preface of his book The Formation of Vegetable Mould, Through the Action of Worms, With Observations on Their Habits (1881)