What does it mean to barrel a baseball?
Whether building a pitch grader or monitoring progress in the cage, sometimes you just want to know whether a batter is finding the sweet spot on the bat.
Let me give you some context before launching into this analysis that ended up longer than I had hoped. At Princeton, we have multiple types of player evaluation that rely heavily on some form of grading batted balls. By grading I mean (generically) measuring the exit velocity and launch angle of a batted ball, and estimating how frequently that ball would be a hit or not. This practice has led to many conversations about what it means to barrel a baseball, and what hit grade(s) would be most effective to feed into our models. This question led me down the rabbit hole you can read below.
To hit the ball on the barrel means finding the ball with the sweet spot of the bat (typically on a hard swing). However, pounding the ball into the ground or hitting it straight into the air does not usually get you on base. So a barreled ball typically is defined in terms of both exit velocity (EV; the speed of the ball off the bat), and launch angle (LA; the angle of the ball off the bat, with 0 being horizontal, -90 being straight down, and +90 being straight up).
Strangely, the MLB rule for a barreled ball is not based on acceptable EV and LA ranges, but is instead defined as the collection of EV and LA pairs that satisfy AVG≥0.500 and SLG≥1.500 (Figure 1).
By this definition, the EV-LA barrel zone is remarkably similar in Division 1 college baseball and MLB (Figure 2). In fact, the mean exit velocity, mean launch angle, mean AVG, and mean SLG within the barrel zone all are within uncertainty of each other for D1 vs MLB (Figure 2). Even comparing the relative fraction of outcomes in the barrel zone shows a virtually identical distribution of play results for D1 and MLB (Figure 2).
On the one hand, I find it interesting that this simple definition of a barreled ball results in virtually identical batted ball and play result statistics in D1 and MLB. On the other hand, why should this seemingly arbitrary definition of barrel rate be an effective statistic for evaluating players? Should a barrel really require a 50% home run rate (Figure 2)? Should laser beam singles also count as barrels even if they did not find gaps, especially in D1 where on base percentage is even more important than in MLB (Maloof, 2024a)?
My impression is that evaluators care a lot about barrel rate, and fold this concept in with expected stats to try to see through the noise/luck and get a sense of what kind of contact a batter makes (Maloof, 2024c). Various definitions of barrel rate also are an essential component of Quality At Bats (QABs), a favorite approach-centered statistic espoused by coaches at all levels.
However, defining barrels in terms of AVG and SLG muddles the barrel rate statistic and just turns it into another expected stat like xBA and xSLG (Note: an expected statistic is computed from probabilistic outcomes based on EV, LA, etc…, as described here). Would barrel rate be more useful as an independent statistic if it were defined strictly in terms of EV, LA, and possibly spray direction (SD)? Do a bunch of line-drive outs with low xSLG suggest you are seeing the ball well and going to hit more barrels that drop for hits? Or do a series of line-drive outs with low xSLG suggest that your triad of {EV, LA, SD} does not profile well for getting hits (or beating a shift)?
Unfortunately, I can’t think of a way to answer this question using college baseball TrackMan data (anyone have any ideas?). However, the 2023 camera upgrade to MLB Hawk-Eye systems allows for tracking of the swing path, and a purportedly accurate measure of bat_speed at contact. These new bat speed data have been available for MLB games since April 4, 2024, and now allow us to determine whether a hit was ‘squared-up’. ‘Squared-up’ really just means hit on the barrel or sweet spot of the bat, so perhaps now I can evaluate the relationship between a xSLG and a “squared-up” ball.
Statcast defines a squared-up contact as a hit that achieves ≥80% of the theoretical maximum exit velocity given a bat velocity and pitch velocity (Equation 1).
The closer a hit is to being squared-up, the higher q will be, and the higher the exit velocity will be for a given pitch velocity and bat speed. I need to know the maximum theoretical collision efficiency, qₘₐₓ, in order to determine the range of observed q values that equate to squared-up hits. However, we do not know the value of qₘₐₓ a priori, which likely depends on the weight of the bat, species of wood, the grain orientation and density of wood, temperature, humidity, etc. I can estimate q empirically for any hit by rearranging Equation 1, and plugging in directly measured values for ExitSpeed, BatSpeed, and ReleaseSpeed.
The maximum theoretical collision efficiency is the right-edge of the distribution of q observations depicted in Figure 3.
Armed with this estimate of qₘₐₓ (Figure 3), I can evaluate the relationship between squaring-up a ball and the raw (exit velocity, launch angle) stats (Figure 4).
Figure 4 illustrates why q/qₘₐₓ ≥ 0.8 might be both a good and bad Statcast definition of squaring-up the ball. In the context of exit velocity, q/qₘₐₓ ≥ 0.8 marks a boundary to the left of which few hits are found (in other words, when exit velocity is above 100 mph, there are virtually always more hits than outs, while when exit velocity drops below 100 mph, even for very high q/qₘₐₓ, outs are still more common than hits). However, in the context of launch angle, hits are found at virtually any q/qₘₐₓ as long as they fall in a narrow range of line drive launch angles. Building on the analysis in Figure 4, now I want to see how squared-up fractions map into MLB barrels, and visualize distributions of exit velocities and launch angles across q/qmax ranges.
Figure 5 tells the story behind the pattern we already intuited from Figures 2 and 4. The MLB definition of a barrel is just a modified version of expected slugging percentage (xSLGfb). This MLB barrel definition includes batted balls with mostly high q/qₘₐₓ, but does not cover the spectrum of hits that were squared-up (Figure 5). Even in the group of most thoroughly squared-up hits (q/qₘₐₓ > 0.9), singles represent more than 25% of the outcomes, and home runs less than 15% of the outcomes (Figure 6).
With all that analysis in mind, I suggest we rescue the term “barrel” from the MLB definition’s emphasis on xSLGfb. Studying exit velocity and launch angle in terms of expected slugging percentage is valuable and predictive, but not an accurate measure of whether a batter is hitting the ball on the barrel of the bat.
For the new college baseball pitch grader I am building right now, xSLGfb is important, but I also want to include information about how often a pitch is squared-up (and we just saw that the MLB definition of a barrel does not help). The new Statcast bat speed data are revolutionary for measuring squared-up fraction… but alas, there are no bat speed data in college. Is there any way to measure squared-up fraction without bat speed data? What if I could estimate q/qₘₐₓ without bat speed?
In Figure 7, I ask how variable a typical player’s bat speed is. It turns out, the majority of players with greater than 100 batted balls in 2024 have bat speeds that vary by 8.5±1.7%.
If individual players have fairly consistent bat speeds (Figure 7), maybe I can design a simulation that estimates the collision efficiency of each hit (and thus estimates whether the ball was squared-up). Here is my setup:
- Figure 8 Left: Identify a hitter’s most squared-up balls by searching for the highest ExitSpeed/ReleaseSpeed ratios. This search is under-constrained without BatSpeed, but if an individual’s BatSpeed does not vary too much (Figure 7), it might be okay.
- Figure 8 Middle: Assume q/qₘₐₓ ≈ 1, and qₘₐₓ = 0.214±0.022 (Figure 3) for these batted balls with the highest ExitSpeed/ReleaseSpeed ratios, and use Equation 2 to estimate BatSpeed. Propagate additional uncertainty in the BatSpeed estimate by adding the 10% random noise typical of MLB hitters (Figure 7).
- Figure 8 Right: Estimate q’ using a Monte Carlo simulation of Equation 2 that draws from the distribution of possible BatSpeeds for each batted ball.
Figure 8 depicts an example of trying to estimate q when there is no bat speed data available. Studying MLB players (for whom we have bat speed data) allows me estimate the typical uncertainty on an estimate of q. Let me illustrate two examples. If I estimate q’=0.2051, then my squared-up fraction calculation would give:
where the total uncertainty of 0.158 comes from this uncertainty propagation for division:
I would conclude that the lowest likely q/qₘₐₓ is 0.80, which still classifies the hit as properly squared-up. In contrast, let’s say I estimate q’ = 0.1722, then:
In this case, if I use the the Statcast threshold for squared-up fraction of 0.8, then I would only be right to call this ball squared-up about half the time. Furthermore, while I have tried to propagate uncertainty as carefully and conservatively as possible, there are significant additional sources of uncertainty that become important if I apply this method to college baseball: (a) BBCOR bats have a higher and more variable qₘₐₓ for which I have no empirical data (except lots of confusing experiences conducting the bat testing before games!), and (b) college players may have less refined approaches and more variable bat speeds.
I think the take home message is that without bat speed data, we can’t reliably measure how completely a batter squared-up the ball. For our pitch grader, and other player evaluations, we are going to have to slum it with intelligent use of xAVGfv and xSLGfb for now. Hawk-Eye, please bring your system to college baseball! While we wait for Hawk-Eye, we will be attempting to constrain bat speed with a stereo-pair of iphone cameras (240 fps vs. Hawk-Eye’s 300 fps) and a little structure-from-motion magic to see whether we can measure squared-up fraction at least for our guys during practice.