I have essentially stripped the terms “buy low” and “sell high” from my vocabulary, so I now prefer to call the former buy low guys acquisition targets. One would think that offering for a player off to a slow start would have to come at some sort of discount, even if a minor one. And since slow starts are usually just that and have little predictive value for the rest of the season, getting anyone at a discount to his pre-season value should yield a nice profit.

As usual, the easiest way to identify your targets is to calculate the difference between a pitcher’s ERA and SIERA and then sort. Those pitchers with SIERA marks most below their ERAs are typically your targets, though that’s not automatically the case. Often times a pitcher could be carrying an ERA over 7.00, but still sporting a 4.50 SIERA. Sure, he’s been unlucky, but he also hasn’t been very good either! So you still don’t want him on your team.

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Last week, I introduced xISO, a metric that calculates a player’s expected isolated power based on his batted ball profile (per FanGraphs’ recently added batted ball data courtesy of Baseball Info Solutions). Having looked at a handful of underachieving National League outfielders for its induction, I’ll expand the analysis of xISO here today.

I’ll reiterate some key points. I used all 12 years’ worth of batted ball data for all player-seasons in which a hitter qualified for the batting title. The OLS regression specified pull rate (Pull%), hard-hit rate (Hard%) and fly ball rate (FB%) as explanatory variables and produced the following equation, which I deliberately omitted last week:

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Today’s deep league wire features a pair of fresh outfielder call-ups. Both were one-time promising prospects, but have struggled at the big league level. Are these two post-hype sleepers?

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Yesterday, I took our first look at the batted ball distance leaderboard to identify hitters who could enjoy a surge in HR/FB rate. Today I’ll check in on the potential decliners. This could perhaps be your sell candidates. I’m going to ignore the obvious ones that have still posted strong distance marks, like Bryce Harper at at 35.5% HR/FB rate, backed by a 323 foot distance. Obviously, he’s going to regress, but that big distance suggests a career year in the power department.

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We’re not a month into the season, so it’s finally time to start putting the hitter batted ball distance leaderboard to work. Back in late January, I unveiled my xHR/FB rate equation, which included three components, batted ball distance being one of them. Unfortunately, I don’t have the data for angle and standard deviation, so I cannot calculate xHR/FB rate marks yet and share the biggest discrepancies. However, simply looking at batted ball distance could do a reasonably decent job at identifying those who might be in for a HR/FB rate surge or decline. We’ll start with the potential surgers. These are the hitters whose distance is top notch, but for whatever reason, have posted mediocre or poor HR/FB rates.

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Episode 226

The latest episode of “The Sleeper and the Bust” is live!

In this episode, Paul Sporer and Eno Sarris discuss the injury news surrounding Coco Crisp, Jayson Werth, J.J. Hardy, and Nick Swisher. They dive into the performances of Corey Kluber, Chris Sale, Drew Pomeranz, Mike Fiers, James Shields, Joc Pederson, Yasmani Grandal, and Andrelton Simmons before closing with a couple of Arizona transactions.

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Man, I am having a blast with the Baseball Info Solutions batted ball data that was recently added to the batted ball leaderboards. Sure, there are reasons to complain: the batted ball spray and contact quality statistics lack context, leaving you in the dark about how spray and contact intersect. For example, there’s Hard%, and there’s LD%, but how many of a hitter’s balls in play are hard line drives? (You can actually find this data on individual player pages under the “Splits” tab — just not on the leaderboards.)

Just because the available data aren’t as granular as one might wish they were doesn’t make them worthless or unusable. Yesterday, I demonstrated that we can still achieve small gains in our understanding of batting average on balls in play (BABIP) using the new data.

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The title of the article is an allusion to Schott’s Miscellany, which you should definitely check out if you never have and feel compelled to know that a group of larks is called an exaltation or that a member of the 32nd degree of Freemasonry is known as a Sublime Prince of the Royal Secret.

–Shift Candidates by Oppo%–

The question of whether teams should use defensive shifts against certain batters is complex. Even applying the term defensive shift fails to do the decision justice because defensive positioning is not limited to either yes or no. However, we do know that teams are more and more willing to deploy defensive shifts. According to the Fielding Bible—Volume IV, shifting has nearly doubled every season since 2011, and early indications are that trend will continue this season.

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It’s no secret that CC Sabathia has struggled mightily since 2013. It’s also no secret that his troubles have coincided with a swift decline in fastball velocity. Check out his velocity trend since 2011:

Last season he lost over two miles per hour off his fastball to a mark that dipped below the important 90 mph threshold. And yet despite the obvious signs of decline, I remained stubbornly optimistic, thanks to a still respectable SIERA. I (foolishly?) boldly predicted that Sabathia “reminds us of his glory days and earns top 40 starting pitcher value” this year.

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You may have noticed that FanGraphs now feeds batted ball data, courtesy of Baseball Info Solutions, into its leaderboards. The day the data appeared, my mind buzzed with ways they could be useful in improving our understanding of a hitter’s batting average on balls in play (BABIP).

Mike Podhorzer already augmented previous attempts at devising an equation for expected batting average on balls in play (xBABIP) for hitters by incorporating elements of a hitter’s power, speed, plate discipline and batted ball tendencies. So, with fresh numbers in hand, I embarked on a journey to further improve the ever-evolving xBABIP. However, I sought to do so by using only batted ball data. Basically, I intended to develop a convenient xBABIP equation, one that can be computed using almost entirely variables found on the same page.

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