If you've watched a baseball broadcast in the so-called Statcast Era, you have undoubtedly noticed the broadcasters commenting on a batted ball's exit velocity, or EV. Many have taken to using stats like Hard% and Soft% to forecast how a player should be performing, expecting larger Hard% rates to produce larger BABIP and HR/FB figures. There is a relationship there, but it is not as clear-cut as you might think.

The hardest batted ball of the 2017 season was struck by Giancarlo Stanton. It was clocked at 122.2 mph but only recorded a single. Eric Hosmer's best hit traveled 118 mph (15th highest in the league), but he only received a ground out for the effort. It is possible to torch a baseball only to make an out. The best way to get a feel for how hard a given batter usually hits the ball is to look at his average exit velocity. The league average mark in 2017 was 91.9 mph, but that stat is of little value. Exit velocity on airborne balls (both flies and line drives) is all you need when evaluating a player's HR/FB rate, while ground ball exit velocity is the best indicator of a high BABIP on ground balls.

The two metrics should almost never intersect, but a lot of analysts ignore context and use overall average exit velocity (or its even worse approximation, Hard%) to evaluate HR/FB and BABIP. You really shouldn't do that unless you believe that Hosmer's grounder above had a chance of going over the fence. Baseball broadcasts will cite Launch Angle (LA) to complement their EV figures, but it is given in terms of degrees. Am I evaluating a baseball player or trying to find the hypotenuse of an isosceles triangle? Let's simplify things a bit to see how these numbers can actually benefit our analysis.

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How to Interpret Batted Ball Statistics

They do not do a good job of publicizing it, but LA is actually fairly simple to understand. Here is the batted ball type produced by the various degree measurements:

Most batters want to live in the 10-50 degree range, as grounders rarely produce power while pop-ups rarely produce anything other than easy outs. Well-struck balls in this range of launch angles are the batted balls that fantasy owners are most interested in. A new stat called "Barrels" filters out everything else, allowing us to evaluate who is hitting the most of these high-value batted balls.

A Barrel is defined as "a ball with a combination of exit velocity and launch angle that averages at least a .500 batting average and 1.500 slugging percentage." It should be noted that the numbers above are only a minimum threshold, as Barrels produced an .835 batting average and 2.937 slugging in 2017. In this respect, the stat is like a Quality Start. It is possible to register a QS with an ERA of 4.50, but the actual average ERA of all MLB Quality Starts falls well below 4.50.

The range of EVs and LAs that combine to form Barrels is called the Barrel Zone. This means that higher EVs can compensate for less ideal LAs to produce the .500/1.500 minimum. At a minimum, it must have an EV of at least 98 mph and fall within the 10-50 degree LA range. We care about fantasy production, not the intricacies of a mathematical relationship. You don't need to worry about the math.

With this in mind, Aaron Judge led baseball in Barrels last year with 86. He was followed by Stanton (76), Khris Davis (65), and J.D. Martinez (60). This group passes the sniff test, as it seems like a collection of guys who consistently make high quality contact. Likewise, Billy Hamilton managed only two Barrels all year, living up to his reputation of weak contact. Still, we already knew this. What do Barrels add to the equation?

They become more instructive when you stop looking at them as a counting stat and start examining them as a rate stat. By taking the number of Barrels and dividing by the total number of Batted Ball Events (BBE), we get a percentage that tells us how frequently a player's batted balls are Barrels. Judge topped this list in 2017 with a 25.4% Brls/BBE figure, followed by Joey Gallo (21.7%), Martinez (19.5%), and Stanton (17.4%). Gallo didn't have the raw BBEs to crack the Barrels leaderboard (253 in all), but the rate stat suggests that he's an intriguing sleeper this year.

This data helped identify sleepers in every year of its existence. Chris Carter had an 18.7% Brls/BBE in limited 2015 playing time. He led the NL in homers the next year with 41, so he was a sleeper worth owning based on the prior year's Brls/BBE. Yankees catcher Gary Sanchez ranked eighth in the league with a 15.8% Brls/BBE in 2016, foreshadowing his ascension to the top of the catcher rankings after a strong 2017. Comparable names on the 2017 list include Matt Olson (16.3%, 7th), Matt Davidson (15.4%, ninth), and Randal Grichuk (14.9%, 11th).

Like BABIP, Brls/BBE also seems prone to random fluctuation. Giancarlo Stanton's amazing 2015 (he hit 27 bombs in 318 PAs) was fueled by a 32.5 percent Brls/BBE, over 10 points higher than the league's second best performance that year (Miguel Sano's 22.4 percent rate in limited time). A rate that high was almost certainly an outlier. Sure enough, he regressed to a still strong 17.3% Brls/BBE in 2016 before his 17.4% rate last season. This suggests that Judge won't be able to completely replicate his 2017 season, but he'll come pretty close.

Conclusion

Statcast is an interesting tool, but it's not yet enough to form the sole basis of your analysis. Exit velocity is one thing that goes into BABIP, but many other factors also play a part. Batted ball distribution is one of the most important among them, which we'll take a closer look at in Part 4!

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