
BaseballSavant is a valuable resource packed with useful information, but some of the choices they’ve made with which statistics to put foremost on their site leave something to be desired. One of these I encounter frequently is Hard Hit rate, or the rate of batted balls a hitter has at 95+ mph. Or from a pitcher’s perspective, the number of 95+ mph batted balls they allow. There are already valid concerns about the changing denominator involved here – batted balls rather than plate appearances, placing it on a different scale from Strikeout or Walk rate – but in addition to this there’s also a missing element of skill involved. That is to say: a 95 mph ball in the air has a very different value to a hitter than a 95 mph ball on the ground.
This is not a new idea – Connor Kurcon developed a hit rate statistic based on this concept in 2018. But what if we took that concept one step further? I was able to approximate Kurcon’s Dynamic Hard Hit rate as a function of launch angle by producing a curve where the exit velocity threshold to be considered “hard hit” varies depending on the extremity of the launch angle. It was based on the idea of approaching the limits of human ability: hitting a ball extremely hard at an extreme angle is a feat only a few can manage. If we took this idea and married it with baseball-specific skill – in this case spray angle or hit direction – then that could be an even greater skill indicator than DHH. Much like a 95 mph ball on the air is not the same as a 95 mph ball on the ground, a 95 mph line drive to the opposite field is not the same as a 95 mph line drive pulled down the line. EDIT: Alex Chamberlain explored this same idea in his own article in July 2021.
Intuitively, it stands to reason that since hitters generate more power to their pull side we would expect them to also generate higher exit velocities on contact to their pull side as well. In turn, this means hitters capable of consistently hitting the ball to the opposite field with authority must have rare pop. Once a hitter demonstrates that ability it shows that he owns that skill, which – if quantified correctly – could help us predict future production.
The first step in finding out how to measure a skill like this is to find a meaningful threshold to denote what was “hard hit”. When I think of a hard or well hit ball, I think of extra base hits. No-doubters that significantly add to a team’s scoring chances. We aren’t chasing results here, but rather looking at the combination of features that show an innate ability to produce those results so we can find those features in hitters. My best guess here was to look for patterns in the exit velocities and hit directions for batted balls from 2016-2021 and their respective wOBA values:

There are clearly 2 bands that contribute significantly here, but the one between ~ 50-75 mph is comprised of mostly singles – not what we’re after, but maybe something interesting enough to examine at a different time since that also could be skill-based. Instead, we will focus on the upper band where all the red is tightly concentrated. It looks like we can fit a curve to match that band:

That bright green line isn’t modeled after the band of red & orange, believe it or not; it’s the 80th percentile exit velocity at each degree of the spray angle spectrum, from -45 degrees (the pull side foul line) to 45 degrees (the opposite field foul line). It tracks nicely, and there’s a good reason for that: a little more than 4/5 of total wOBA comes from just over 20% of the league’s total batted balls. Conveniently for our purposes, that 80th percentile exit velocity figure is also “stickier” year-to-year for each hitter than their annual average exit velocity: the measure of correlation (Pearson’s r, for the statistically inclined) is .734 for 80th percentile EV vs .641 for average EV, where an r of 1 is a perfect linear relationship. This means it is a more reliable, consistent measure of a hitter’s inherent ability. There are stickier measures – generally the higher up one goes in terms of exit velocity threshold, the stickier the figure becomes – but we want to also capture a significant chunk of each hitter’s batted balls to make the number both predictive and descriptive, so we’ll stick to the slightly lower percentile.
This brings us to this simple concept: what percentage of a hitter’s batted balls clear that 80th percentile threshold at each spray angle? Each batted ball that does is an indicator of power that only a small subset of hitters possess. Stretching the idea further, how many batted balls does that hitter have that clear the same threshold applied to the launch angle spectrum, which is another indicator for rare power (and similar to the idea behind DHH)? Using the clearing of either of those 2 markers as our definition of “hard hit” while limiting the balls counted to those hit in the air – to weed out the mostly harmless grounders hit at higher velocities – we can come up with our own well hit rate and use it as a tool for evaluating player potential. Since this boils down to an attempt to measure a player’s ability to hit for power, we’ll call it Damage Rate.
This is what the 2021 leaderboard for Damage Rate looks like on a per batted ball basis, so as to put it on the same scale as Hard Hit rate or Dynamic Hard Hit rate (min. 50 batted balls):

At first glance, this seems to pass the sniff test. The league leaders are names you would expect near the top of a hard hit/quality of contact leaderboard, but there are some interesting risers like Higashioka, Rooker, and Rogers. Since this measure considers the quality of contact by both launch and spray angle, traditional hard hit rate leaders like Aaron Judge and Giancarlo Stanton find themselves lower down in the leaderboards, though still in very strong positions overall. This is because some of their loudest contact is made on relatively low percentage batted balls, like grounders or popups. For the same reason, these numbers are lower than the hard hit rate numbers you would find on BaseballSavant; the league leader in that statistic is 17 percentage points higher.
Since it seems like it may hold some credence at first glance, the next step here is statistical validation. The returns there also seem encouraging, as it has a higher correlation to both current and next season’s wOBA and wOBACON – in addition to any of the other advanced power metrics – than DHH%, wOBACON itself, and HH%. Additionally, it has a stronger relationship with itself year-to-year than any of those statistics bar DHH%:
Pearson’s r with next season statistics
(min. 100 batted balls in both years)

Pearson’s r w/current year wOBAcon:

The statistic has a tougher barrier to entry than hard hit rate does because it measures raw power in addition to the ability to apply that power, so there were no players who “improved” in raw percentage compared to hard hit rate. However, some players were favored more than others by the changes. A few of the biggest gainers in terms of league ranking in damage rate vs hard hit rate were Jordan Luplow, Cody Bellinger, and Gleyber Torres, suggesting that perhaps those players may be due for stronger seasons in 2022 based on the nature of their batted ball profiles in 2021, if the predictive qualities of damage rate are to be trusted.
The most notable drops in league standing came from players considered to have traditionally strong bat-to-ball skills whose hard contact did not ultimately result in many extra base hits. This could be a blind spot on the part of the statistic, and something I suspect has to do with my discarding of hard hit grounders due to their relative inefficiency – ground balls hit at 95+ mph produced only a .331 wOBA in 2021, compared to the .882 mark on fly balls hit at 95+ mph. This is a group of players that includes Nick Gordon, Amed Rosario, Yuli Gurriel, and Michael Brantley. Some average to pretty good hitters, but not ones that produce much in the slugging department.
Here are the biggest gainers and losers by league finish in damage rate compared to league finish in hard hit rate (the rankings_jump column shows how many places were gained/lost between the 2 metrics):
Largest risers

Largest drops

If you’d like to take a look at the leaderboards in damage rate on your own, here is a sheet containing the 2021 season.
In the future I think I will look to create a simple projection system making use of damage rate and examine some of the larger differences in the way the metric values certain players compared to other methods. I also plan on examining the 2nd band of wOBA values from the first exit velo + spray angle graph since it seems like a sizeable enough portion to hold some significance. Overall, I’m pleased with the outcome of this foray into hitting metrics and look forward to updating the statistic and monitoring its performance in the upcoming season.






















