xSLASH: An Introduction

As can be seen in our About page, my “research is usually conducted on the margins of what is both relevant and socially acceptable.” In that vein, what I am presenting to you here is both marginally relevant, and if you bring any of this stuff up in serious baseball circles you’ll find that it’s also marginally socially acceptable. Try to quote this research in any sort of serious forum, and you will likely be laughed at. However, if you bring this up to less intelligent baseball fans they will likely be impressed with what they mistakenly believe to be a firm grasp on statistics and player analysis. Champagne will fall from the heavens, doors will open, doves will fly around or something like that. If you, like me, have no idea what the hell I’m talking about at this point, please continue!

If you are familiar with advanced baseball statistics, you should be aware of the noise inherent in a player’s batting average on balls in play (BABIP). Because of the large factor of luck involved in BABIP, formulas for expected BABIP (xBABIP) have been created, which use a player’s batted ball profile (line drive rate, ground ball rate, etc.) to determine what a player’s BABIP should be, had luck not been involved.  This can then be used to determine who have been the luckiest and unluckiest batters over the course of a season.

When perusing the #content of xBABIP leaderboards myself, I always felt like something was missing. Yes, xBABIP could quantify how lucky one’s BABIP has been, but as with any baseball statistic, what we ultimately want to know is how many runs this is earning or costing the player’s team. This brings me to the subject of this article. One of the features of this site that I will regularly keep up with will be the xSLASH leaderboards. xSLASH is a group of toy statistics I created because I had too much free time wanted to be able to quantify how much a given player’s luck had affected the number of runs he had produced. This basically was just an extension of the xBABIP work that had previously been done, but rather than stopping at correcting a player’s BABIP, xSLASH corrects all of his offensive statistics. It uses a player’s xBABIP to determine the expected number of hits he should have, then uses his career 2B/H and 3B/H rates to determine how many of those hits should be doubles and triples. Once his expected numbers of 1B, 2B, and 3B have been calculated, one can calculate his expected AVG, OBP, SLG, wOBA, and wRC+ using his current number of HR, BB, and K. In short, xSLASH acts to determine a player’s overall offensive production adjusted for luck.

There’s two things you should remember when looking at these numbers. One, there are HUGE error bars associated with doing this. xBABIP is in no way perfect. It doesn’t take into account how hard a player is hitting the ball, the distance of his batted balls, whether the balls are being pulled or not, etc. Second, this is absolutely not a tool to predict how a player should perform going forward. I repeat, if you take this data to be projections for player performance, you are what Talking Chop writer and noted ignorance expert Dan Simpson would call “the height of ignorance.” This is merely a tool to look back to accurately and precisely half-assedly assess how well a player has performed to date, excluding batted ball luck.

One final note: this data is only as good as the xBABIP calculator that powers it. If you’re aware of a more accurate version of the xBABIP calculator than the one I’m using (linked above) please let me know.

So, without further ado, here is the link:

xSLASH Leaderboard

If you have questions about any of this, or would like to see the actual calculations and whatnot, contact me on twitter at @srbrown70.

Stephen came up with the idea for this blog shortly after graduating from Tech. Realizing that life is ephemeral, he decided to put (metaphorical) pen to paper and catalogue his thoughts. His thoughts are series of numbers and spreadsheets, casually categorized as “research,” and said research is usually conducted on the margins of what is both relevant and socially acceptable.

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