In my last post on explaining pitchers’ BABIPs by way of their batted ball rates, I was very careful to say that it was applicable in the long run, as it’s hard to be accurate over a short number of innings pitched, due to all the “noise” in BABIP (Batting Average on Balls In Play).  I only used pitchers with a qualifying number of innings pitched (IP) in the calculations, for that reason.  After writing the post, I did some messing around with the data, to find out just how much of an effect IP had on the predictability of BABIP.

Hold on to your propeller beanies, fellow stat geeks: the correlation between xBABIP and BABIP went from 0.805 when the minimum IP was set to 1500, to 0.632 at a 200 IP minimum, down to 0.518 at 50 IP.  OK, maybe it’s not that surprising.  Still, I thought I’d better show you how confident you can be in my xBABIP formula’s accuracy when you take the pitcher’s innings pitched into account.

The formula, again: xBABIP = 0.4*LD% – 0.6*FB%*IFFB% + 0.235

And remember, that formula is primarily meant to be a backwards-looking estimator of “true,” defense-neutral BABIP.  My next article will (probably) discuss another formula I’ve come up with that’s more forward-looking.

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Hi everybody, this is my first post here. Today, I’ll be sharing some of my BABIP research with you. There will probably be several more in the near future.

Now, I don’t know about you, but Voros McCracken’s famous thesis stating that pitchers have practically no control over their batting average on balls in play (BABIP) always seemed counterintuitive to me, ever since I heard it about 10 years ago. Basically, my thought this whole time was that if an Average Joe were pitching to an MLB lineup, the hitters would rarely be fooled by the pitches, and would be crushing most of them, making it very tough on the fielders. Think Home Run Derby (only with a lot more walks). Now, the worst MLB pitcher is a lot closer in ability to the best pitcher than he is to an Average Joe, but there still must be a spectrum amongst MLB pitchers relating to their BABIP, I figured. After crunching some numbers, I have to say that intuition hasn’t completely failed me.

This is going to be a long article, so if you want the main point right here, right now, it’s this: in the long run, about 40% or more of the difference in pitchers’ BABIPs can be explained by two factors that are independent of their team’s defense: how often batters hit infield fly balls and line drives off of them. It is more difficult to predict on a yearly basis, where I can only say that those factors can predict over 22% of the difference. Line drive rates are fairly inconsistent, but pop fly rates are among the more predictable pitching stats (about as much as K/BB). I’ll explain the formula at the very end of the article.

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Stated in as simplest terms as possible, the goal of pitching is to get batters out without allowing runs to score. There are three ways any given pitch can get a batter out. A pitch can either be swung on and missed, taken for a called strike, or batted in such a way that the batted ball does not result in the runner reaching base. Batted balls involve the defence and are therefore less directly related to the pitch’s effectiveness at getting outs. That leaves us with swinging strikes and called strikes as the two best ways to measure a pitch’s effectiveness.

In Part I of my research on curveballs, I looked at what makes a curveball effective from a swinging strike perspective. I used an outcome variable that I like to call: ratio of effectiveness. Ratio of effectiveness is simply a ratio between swinging strikes and home runs hit. In Part II of my research, I will look at the effectiveness of curveballs from a called strike perspective. This work will aim to answer two basic questions: 1) are curveballs taken for strikes more often than fastballs? And 2) what are the characteristics of curveballs most often taken for strikes?

Are curveballs taken for strikes more often than fastballs?

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117 years ago, in response to an epidemic of infielders intentionally dropping popups to attempt double plays instead, the National League adopted the infield fly rule, and with some minor adjustments, the rule has survived to the present. Like many remedies from the 1800s, the intent- protecting the offense from chicanery- was good, but the implementation- calling their batter automatically out- was fraught with problems.

First, and most obviously in light of recent events, even when the defense can’t make the play, the rule intended to protect the offense punishes them by giving the defense the out anyway. Second, any time a fly ball can be intentionally dropped for a good shot at a double play, the offense should be protected from that, but because the play requires calling the batter automatically out, the rule as written can’t be invoked liberally. Third, and related to the second, the umpires have to make a judgment call based on the trajectory of the ball, the position of the fielder, environmental factors, and anything else they consider relevant to determining “ordinary effort”. That leads to late calls and inconsistent application.

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The curveball is often used as an ‘out’ pitch. This implies either it is difficult to hit or is often taken for a called strike. I was interested in exploring both of those possibilities, and as such, I have decided to present research addressing both. Part I, presented below, addresses the questions of how difficult the curveball is to hit and what makes it difficult to hit.

Earlier this week, I shared some research about the relative importance of velocity, location, and movement with respects to major league fastballs. The approaches I used to answer the curveball problem were very similar to the approaches I described previously. Again, I used the 2011 MLB season as my dataset, and included only pitches to right handed batters. Since curveballs are thrown far less frequently than fastballs, this time I included both right and left handed pitchers to increase my sample size. Another reason I wanted to include lefties is I wanted to know if the direction of the horizontal break mattered.

Is a curveball more difficult to hit than a fastball?

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Velocity, location, and movement are all unquestionably important when we try and compare ‘good’ pitches to ‘bad’ pitches. My particular interest lies in how important each are. I’ve often wondered if a 98 mph cutting fastball can be thrown right down the middle and still have little chance of being hit for a home run? Or conversely, is an 88 mph fastball that’s straight as an arrow still likely to get a swinging strike if it paints the bottom outside corner of the zone?

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This article is the second of a two part series evaluating 2011 baseball player forecasts. The first looks at hitters and found that forecast averages outperform any particular forecasting system. For pitchers, it appears as though the results are somewhat reversed. Structural forecasts that are computed using “deep” statistics (k/9, hr/fb%, etc.) seem to have done particularly well.

As with the other article, I will look at two main bases of comparison: Root Mean Squared Error both with and without bias. Bias is important to consider because it is easily removed from a forecast and it can mask an otherwise good forecasting approach. For example Fangraphs Fan hitter projections are often quite biased, but are very good at predicting numbers when this bias is removed.

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This article is an update to the article I wrote last year on Fangraphs.

This year, I’m going to look at the forecasting performance of 12 different baseball player forecasting systems. I will look at two main bases of comparison: Root Mean Squared Error both with and without bias. Bias is important to consider because it is easily removed from a forecast and it can mask an otherwise good forecasting approach. For example, Fangraphs Fan projections are often quite biased, but are very good at predicting numbers when this bias is removed.

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One of the hallmarks of the annual Hall of Fame debates is the comparison to players already enshrined. It can be a very good exercise in determining the merits of a particular player, especially because after so many years, there are now a lot of players in the Hall of Fame. There are plenty of players at every single position. There are pitchers. There are power hitters, average hitters. There are great fielders. One area where the present Hall of Fame lacks in providing a good comparison is the Closer situation.

As Wendy Thurm’s post indicated in evaluating Lee Smith’s candidacy*, it is difficult to judge because the only full-time relief pitchers in the Hall of Fame are Rollie Fingers, Goose Gossage and Bruce Sutter. Hoyt Wilhem is not an apt comparison, having retired in 1972 with 500 more innings pitched than even Rollie Fingers. Wendy reached the conclusion that Smith was better than Sutter, not as good as Fingers and Gossage, and put Smith just on the other side of the Hall of Fame. It feels like the right call, but if Sutter is in the Hall what exactly is the standard for relief pitchers?

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This past October, David Laurila conducted an interview with Zach Britton, the 23-year-old lefty who just finished up his rookie season with the Orioles. As a highly touted prospect, Britton didn’t put up impressive strikeout totals, but his groundball-inducing heavy sinker allowed him to enjoy much success in the minors. When Laurila asked Britton for his thoughts on his underwhelming major league 1.56 K/BB ratio, Britton responded as follows:

“I know that it could be better, obviously. I’m not going to be a guy who strikes out a ton of people; I’ll never lead the league in strikeouts. And with the movement I have, I’m going to walk guys. That’s something I can improve upon as I get older and more experienced, though. I can learn to make better adjustments… I pitch to contact. If I get a guy 0-2, I’m not necessarily looking to strike him out; I’m looking to get him to hit a ground ball. It’s a mindset. I’m not a huge believer in having to strike guys out in order to be successful. I’d rather keep my defense on their toes and get outs. Most times, when I strike guys out, it’s not on three or four pitches; it usually takes five, six or seven. Pitching to contact allows me to be more efficient.”

My first instinct was to be a bit skeptical of the effectiveness of this “mindset.”  Numerous studies have indicated that is issuing walks, not striking batters out, that ultimately increases pitch count to the point of being “inefficient.” Yet in sabermetric analysis, it is not uncommon to find outliers in these aggregate models — some players simply don’t fit the mold of generally accepted principles. Britton, after all, ought to know his own tendencies better than anyone else.

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