Why no two Batted Balls look the same or should be treated as such
It’s no secret Line Drive, Ground Ball (GB), and Fly Ball (FB) classifications are faulty and have some major measurement errors. Until there is a standardization of the stringer data based on hit angle and velocity, we should be cautious with taking batted ball classifications at face value. The fine line between a FB and a LD is in the eye of the beholder, with many different eyes determining the classification among MLB’s 30 ballparks. So, the fact that LD%, GB%, and FB% don’t take into account classification biases by adjusting for park factors is concerning, being that a FB at one stadium can very well look different at another — despite looking the same on our spreadsheet.
Two, by using batted ball percentages we are assuming that all LD’s, FB’s, and GB’s are created equal. Binning each batted ball type into a percentage assumes that we believe they are created equal by not differentiating between a batted ball hit with authority and one blooped over an infielder’s head. While, yes, the public does not have HITf/x, and yes, we cannot differentiate batted balls by velocity—we can use proxies to help us separate batted balls into more accurate groups. Using Gameday data, it is possible to approximate the distance a ball was hit, the angle to the field it was hit, and (less accurately) which type of batted ball it was.
By using the proxy of distance and spray (pull, opposite, center), we see major differences in the sub-groups of each batted ball type. For instance, a pulled LD falls for a hit 8%-10% more than a opposite hit LD—there is a similar differentiation between a pulled and opposite field FB. Take a look at the following tables:
The tables below are pulled from Gameday data, which is known to have its own classification errors. For that reason, I removed all unreasonable batted balls from the data set (for instance balls recorded to be hit over 500 ft, etc.). When comparing the cleaned data set to the raw one, the numbers below were very much similar. Angles range from negative (left field), to 0 (dead center) to positive (right field). Fields vary but on average the foul line is somewhere in between ± 45 – 50, so Pull was defined as any ball hit < -20 degrees, and opposite was > 20 degrees. Of course for a righty hitting the ball at -25 degrees (left-field) is pull, and 25 degrees (right-field) is classified as an opposite field batted ball—whereas a lefty hitting the ball at -25 degrees would be hitting the ball the opposite way.
|Line drive to outfield, not equaling home run, 2008-2014|
|All balls hit to outfield, not equaling home run, 2008-2014|
|All balls hit to an infielder, on ground, not bunts, 2008-2014|
You can see that spray has an obvious effect on the rate at which certain batted ball types fall. For that reason, the field to which a ball was hit should not be ignored when analyzing a player’s batted ball profile. So when analyzing any aggregate of batted ball type, first we should likely stratify by pulled, opposite, and up-the-middle hits. Whether or not spray is a proxy for batted ball velocity, my guess is that it may have just as much to do with defensive positioning as quality contact. So in addition to spray, we have to look at individual run values of the events spawned from each batted ball type. Below, variance in run value of any batted ball type differs between each type:
|Batted ball type and run values|
|Batted Ball Type||runvalue||std(runvalue)||count|
Imagine a line drive, then imagine a fly ball. The picture is simple, a fly ball is more likely to leave the park, and more susceptible to park conditions (wind, heat, positioning). But a fly ball can also look like a pop-up or “room service”. Hence a fly ball has the most variance in its run values of any of the three batted balls.
While, a line drive is also most likely to fall, it is mostly effected by luck (where it was hit, its speed off the bat), but its high run value supports common knowledge that it is the most desirable (not sustainable) outcome of the three batted ball types. Meanwhile, ground balls are mostly dependent on defensive positioning (shifts), surface, and batted ball speed. But if speed off the bat is a factor for all three, which it most likely is, and if in fact spray serves as a proxy for hit-velocity, then the chart below should show that a pulled batted ball should have the highest run value for its specific bin.
|Batted ball type and spray, run values|
|Batted Ball Type||Spray||runvalue||std(runvalue)||count(*)|
Like we expected, a pulled ball leads the way in run value for its batted ball bin, where the exception is ground balls—an opposite-hit ground ball is worth a tad bit more than a pulled one. My guess is that this has to do with shifts and/or the fact that most pulled ground balls are rolled over. Still fly balls have the most variance in run values, in front of line drives and ground balls.
So with obvious differences between batted balls inside their own bins, there has to be a better way of representing how no two batted balls look the same, and how they should not be treated as such. Intuitively, a home run is a home run, context neutral. Meanwhile, A LD is not a LD in any context of the word. A LD can be a single, a double, a triple, a home run, an out — all possibilities equipped with different run values. For this reason, expecting some regression of the amount a player hits line drives and expecting subsequent regression of BABIP and/or wOBA is not wrong, it’s just not quite right. Instead, we should use the empirical data we have on the actual outcome value of a player’s batted ball to supplement the simple rate that outcome occurs. With run values in the picture, I care less about how often a player is hitting a line drive and more about what he is doing with a line drive.
Like I’ve said before, I’ll take 15% LD rate from Giancarlo Stanton before I dare take a 45% LD rate from Juan Pierre. Expecting any regression from Ichiro Suzuki‘s line rate to negatively effect his performance overall is assuming he has a marginally better line drive run value, per line drive, than the average player (after adjusting for park). But can we make that assumption solely based on the rate at which he hits line drives? The answer is clearly no, and it calls for a more transparent measure of batted ball performance based on the actual run values resulting from a player’s batted ball distributions.
Enter Weighted Batted Ball Runs Above Average
We want to create a measure of how valuable a player’s batted ball type is to their production. We can do this using run values for the events created by that batted ball type.
Dave Studeman has tirelessly researched and produced articles revolving around the topic of batted ball production. His work on the area, shows exactly how batted ball rates tend to diverge from their usefulness when we want to describe the actual run production of a player’s batted ball. So what I introduce today, is not news, it’s merely my interpretation of some of the great work from those who came before me.
As for an introduction let’s say for instance a player has 50 line drives, and in those 50 line drives he has created 21.05 runs from 45 singles and 5 outs. We want to adjust this player’s performance by adjusting it for what the average player would do in as many LD’s and then adjust that measure for park factors, or how much more frequent a LD was recorded in the parks where the line drives were hit compared to the relative frequency in all other parks. Since I am using the same data set as Bill Petti’s spray tool , I’ll use the same run values—where: “-.28 – outs, .5 – singles, .79 – doubles, 1.07 – triples, 1.41 – home runs”. The first step is to find a player’s run value per ball in play (BIP). Let’s use fly balls as an example: 2013 Chris Davis had a run value of 0.46 per FB. Basically the value of a single for each fly ball hit, but it does need to be contextualized by the league average run value per FB — which hovers around 0.05-0.06 runs per fly ball. So Chris Davis’ RVfbaa (Run Value per Fly Ball Above Average) was around 0.41. Great. Now we need to account for park so that we can feel better about the possible classification errors and park effects on run values per batted ball type. This will be the last step, so using the following formula will yield wRVfb (Weighted Run Value of Fly Ball):
wRVfb = (RVfbaa – (PF_FB/100 – 1) * AverageRV/FB * Player FB) / (PF_FB/100) )
Or for 2013, Davis’ wRVfb was 0.41 given Camden Yards had a FB park factor of 100 (league average). Follow the same process for line drives and ground balls and you’re all set with values that will assess a player’s batted ball performance in terms of run values. Now we have measures of how valuable a player’s batted ball outcomes are, in addition to the raw probability they occur. Let’s take a look at some of the leaders and losers.
|Best and Worst Fly Ball producers, 2008-2013, min 500 BIP|
|Best and Worst Line Drive Producers, 2008-2013, min 500 BIP|
|Alejandro De Aza||0.13||965|
|Best and Worst Ground Ball producers, 2008-2013, min 500 BIP|
Finally, some facts about the wRV metrics:
- There is very little correlation between wRV to LD%, GB%, and FB% respectively. In fact, LD% had a 0 correlation between itself and wRVld. In other words, batted ball run values are pretty much independent of the rate at which they are hit.
- ISO explains nearly 70% of the variation in wRVfb, so it is a pretty great proxy for the value of a player’s fly ball.
- A simple regression of wRVfb, wRVld, wRVgb explains nearly 70% of wOBA, while adding BB% and K% explains accounts for around 35% of wOBA in year two.
- Below are the year-to-year correlations, and the data can be found here:
|wRV metrics, year-to-year correlations|
So they have similar variances to batted ball rate metrics, being that they seem to be subject to a lot of year-to-year variation—line drive and ground ball production is the hardest to maintain while fly balls remain relatively consistent. So what’s the culprit here? My guess is defensive positioning and shifts. My guess is that for pull hitters and home run hitters, these numbers are pretty consistent once a shift is found to limit their effectiveness, while more balanced hitters are subject to more random variation.
There is a lot more analysis to employ here, however. In my piece tomorrow, I create a shift breakeven point and a metric that isolates players who should be shifted based on their batted ball run values relative to their spray tendencies. In the future, I except to regress run values based on distance hit from the fielder, so that we can isolate for players who have overperformed due to faulty fielding position and could expect regression once better alignment is in place.
References and Resources
- Thanks to Jeff Zimmerman for the Gameday data and distance/angle code. Also to Major League Baseball Advanced Media for publicly providing the Gameday data.
- Studeman, Dave. “Pictures of Batted Balls.” The Hardball Times. Jan. 5, 2006.
- “WRAA For Position Player WAR Explained.” Baseball-Reference.com. June 2, 2014.