SkaP: A New Metric to Measure Hitting Prowess

Before I explain to you what this new metric – SkaP – does, I am first going to warn you that I can’t provide you with a formula or individual statistics for it.  It’s a theory right now, and something for which I need access to data I don’t have in order to find a formula.

This statistic was inspired in part by Colin Dew-Becker’s article the other day here on FanGraphs Community Research.  In his article, he argued that the the way a hit or out is made matters – not just the result of the hit or out.  A single to the outfield, for example, is more likely to send a runner from first to third or from second to home than an infield single.  Likewise, a flyout is more likely to advance runners than a strikeout is.

This statistic was also inspired in part by UZR.  UZR attempts to quantify runs saved defensively by a player partially by measuring if they make a play that the average fielder would not.  In the FanGraphs UZR Primer, Mitchel Lichtman explains that

“With offensive linear weights, if a batted ball is a hit or an out, the credit that the batter receives is not dependent on where or how hard the ball was hit, or any other parameters.”

This means that a line drive into the gap in right-center that is a sure double but is caught by Andrelton Simmons ranging all the way from shortstop (OK, maybe that was an exaggeration) will only count for an out, even though in almost any other situation it would be a double.  The nature of linear-weight based hitting statistics (and most other hitting statistics as well) is that they are defense-dependent.  Hitters have been shown to have much more control over their batted balls than pitchers do, which is why so far only pitchers have commonly used defense-independent statistics, but it would probably be useful for hitting too, no?

Now, if we want a defense-independent and linear weights-based hitting statistic, it would not be possible to formulate something similar to the hitting equivalent of the current model  of tERA (or tRA) because that generalizes all batted balls into categories such as grounders, line drives, or fly balls, because hitters can control where and how hard and at what angle their batted balls are hit at least to some extent.  Instead, what I would use is something more similar to a hitting equivalent of this version of tERA I found on a baseball blog.  What that article proposes is something much more detailed than what we have now (by the way, tERA has been supplanted by SIERA, but is still an interesting theory).  Their idea is that instead of finding expected run and out values for grounders, line drives, and fly balls, find the expected run value for a ball, to use their words, “with x velocity and y trajectory [that] lands at location z.”  This is similar to UZR in that exact (or as close to exact as possible) batted-ball data is processed and the expected run/out values are calculated.

So now for the statistic:  SkaP, or Skill at (the) Plate, is a number that uses all that batted-ball data to find the expected run and out values of each at-bat.  It would weight the following things:  home runs (although maybe a regressed version could use lgHR/FB%*FB instead), walks, strikeouts, HBP, and each ball put in play by the player.  This makes it so that it is not defense-dependent, and so that Andrelton Simmons catching that sure double does not penalize the hitter.  I haven’t calculated this statistic, though, so I don’t know if this would be best as a rate, counting, or plus-minus statistic (maybe all three?).

There’s one catch to this, however:  Skill at the Plate is really only a measure of skill at the plate.  It doesn’t account for some batters’ ability to stretch hits or beat out infield singles.  Billy Hamilton is going to be more likely to reach on an infield single than Prince Fielder.  However, this stat would treat them both the same, and not reward Hamilton’s speed for allowing him to reach base on what might have most likely been an out.  It would be very hard to separate defense independence and batter-speed independence for hitting statistics, though, and I’m not sure it’s possible to do without an extreme amount of effort.  Maybe a crude solution would be to quantify a player’s speed using Spd, UBR or BsR and add it somehow to this statistic.

I can’t calculate this myself, as I don’t have access to Baseball Info Solutions’s (or some other database that tracks batted balls) data.  FanGraphs does, however, and I would love to see this looked into further.




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Jonah is a baseball analyst and Red Sox fan. He would like it if you followed him on Twitter @japemstein, but can't really do anything about it if you don't.


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scotman144
Member
Member
2 years 10 months ago

wicked good band too: http://en.wikipedia.org/wiki/Ska-P

triple_r
Member
2 years 10 months ago

So if I understand you, it’s basically xFIP for hitters? Because I had a similar idea, and I was also unable (i.e. too lazy) to calculate it.

Leo
Guest
Leo
2 years 10 months ago

Ultimately, this is where baseball analysis needs to go right? Assigning batted balls some kind of defense-independent value based on speed and angle off the bat. In theory such a value should greatly improve our understanding of hitters (and pitchers) by eliminating a huge source of random variance.

Stephen
Guest
Stephen
2 years 10 months ago

Couldn’t you do this by correcting their BABIP according to their xBABIP, based on their batted ball profile? Essentially, you could use their xBABIP to determine how many hits were “taken away” or “given.” That’s something I did earlier this year, and it seems to work fairly well.

philosofool
Guest
philosofool
2 years 10 months ago

I’m not actually sure that these sort of stats give a better picture of a batter’s true talent than what we already have. Joey Votto put 450 balls into play last year. Assuming that this is a random sample of his hypothetical true talent, that basically gives you a +/- .04 margin of error for the distribution of his true talent results. If you start chunking the results into very small boxes (hard hit line drives up the middle, for example) you’re basically talking about a sub-set of his outcomes that happens less often than the margin of error for your estimate of the distribution.

My opinion is that we don’t need metrics with more variables as inputs, but metrics that depend on the most important correlates of success. So, for example, I think we need a metric that’s based on the most important elements of batted ball results but which ignores lots of stuff because it’s predictive usefulness isn’t large. Speed and ball in air hang-time might be all you need to know.

Also, you have a serious interaction problem in measuring the relevant stuff because batters may approach things differently with runners on base. With a runner on first, a hitter may TRY to hit to right field more, but the number of times he’s in that situation may affect the appearance of his ability to do it. Lead off hitters will be in the situation rarely compared with all others.

dan
Guest
dan
2 years 10 months ago

Check out UMass’s research around BABIP and batted ball profile under “Modern Techniques for Evaluating Hitting” http://baseball.physics.illinois.edu/

Alan Nathan
Guest
2 years 10 months ago

UMass??

Brandon Firstname
Member
2 years 10 months ago

This is pretty much precisely what should be done. Hitf/x is the data we need I think, but the problem is that we have pretty much no hope of that data becoming publicly available any time soon.

I wonder if we could use some sort of hybrid viewpoint to make this happen by using results statistics to “weight” a batter’s ability with different batted ball types. So basically just using batter hitting stats that are heavily regressed to batted ball types. This would probably be a pretty good predictive view of batters, but there are a lot of kinks to work out to make this method feasible.

rgeryhtr
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rgeryhtr
2 years 10 months ago

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