As a projectionist, I am seemingly on a never-ending quest to develop equations for every result statistic. By result statistic, I mean home runs, for example, which are fueled by such skills as hitting the ball far, among others, which itself is summarized by the average batted ball distance we reference here quite often.

Another one of those result statistics is batting average. A hitter’s batting average is derived from two underlying skills — his ability to make contact (strikeout rate) and turn balls in play into hits (batting average on balls in play). While a hitter’s strikeout rate is quite stable from year to year, unfortunately his BABIP is not. It’s one of the metrics we still struggle to explain, with luck considered to play a major role.

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A year ago, I discovered how highly correlated a hitter’s average home run and fly ball distance is to his HR/FB rate. Chad Young and I then embarked on a quest to use an assortment of data, including this batted ball distance, to construct an expected HR/FB, or xHR/FB rate, metric. Unfortunately, we failed to find an equation much better than the one that used just distance, of which the R-squared was just 0.54. While this was an excellent start, it simply wasn’t good enough to use in place of plain old HR/FB rate.

Thanks to Jeff Zimmerman, whose Baseball Heat Maps site inspired this quest to be undertaken to begin with, I have been provided with a wealth of additional data. The hope was that it included another piece or set of pieces to the HR/FB rate puzzle.

I began with a player population set that included 4,985 hitter seasons from 2008-2013, which also included pitchers during their times to the plate. In order to prevent the results from being skewed due to the randomness occurring in the smaller samples, I removed all player seasons with fewer than 20 total home runs and fly balls. This left me with a pool of 2,645 ready for analysis.

Let us begin with a correlation table:

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ZiPS. Marcel. Bill James. Cairo. PECOTA. Steamer. These are not the names of my pet hamsters. They are the names of some of the most well-known baseball player projection systems out there. All of them derive their forecasts by throwing various historical data into a blender and spitting out a projected performance line. These systems are pretty darn good, but for the most part, very little is shared about the formulas that make up their guts. So at some point, you might get that urge to begin projecting players yourself, rather than rely on these systems that are unaware of injury issues or changes in a pitcher’s repertoire, for example.

For over 10 years, I have projected players myself for use in my local fantasy leagues. As such, I have never projected any non-fantasy statistics such as hitters walks, doubles, triples, etc (which is why I don’t mention them below!). This is how I do it.
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We have come to accept over the past couple of years that for the most part, pitchers have the most control over their strikeout, walk and ground-ball rates. It is true that they do have some influence over a few of the metrics we lump into the luck category, but we can still be fairly accurate with our evaluations by just focusing on the three aforementioned core skills.

As fantasy players, we thrive on trying to find this season’s breakouts. Winning your league basically depends on it. The easiest way to identify these pitchers is to look at their peripherals and determine who has the ability to improve upon any of them. Better skills equal better results, assuming all else equal of course.

In my experience playing fantasy baseball and reading studies about the effect aging has on the various peripherals, it seemed pretty clear that pitchers improve their control more frequently than the other underlying skills. Luckily, we don’t have to guess anymore if this is actually the case, as Jeff Zimmerman, researcher extraordinaire, has done the work and the results can be found in this nifty graphic below.

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