Last season, MLB released Statcast, an innovative tool used to evaluate player movements and athletic skill. Defensively, it can track how efficiently a player’s line to the ball was, how much ground he covered, arm strength, top speed, and many other factors. It also can track baserunning metrics, such as lead distance, grabbing an extra base, max speed, and home-run trot, among other things. Statcast also tracks pitching and hitting metrics. MLB teams can now use iPads in the dugout, meaning they have an endless supply of information at the touch of a finger.
Recently, Albert Chen of Sports Illustrated wrote a piece on various teams’ use of Statcast. The article notes how Pirates hitters would review a pitcher’s spin rate before an at-bat. If the spin rate was high, they would expect something lower in the zone. Even Kris Bryant credits Statcast, saying he improved his launch angle, aiding in his breakout, possibly MVP season. All teams have been using the data, says Chen, and teams have used the data in different ways. Daren Willman, who heads BaseballSavant, describes the use of Statcast as an “arms race,” as teams now have this bank of information at their disposal. Willman analyzes this Statcast data himself, looking at player comparisons and evaluations. The tricky thing, according to Willman, is knowing what information to look at. He claims “It’s so massive, it’s just about asking the right questions . . . the answers are all there.”
The Tampa Bay Rays, a forward-thinking club, tell their players on the first day of spring training that the Rays value their batted-ball velocity, rather than batting average. Similarly, the New York Mets decided to take Lucas Duda over Ike Davis to be their 1st baseman of the future. Duda soon started to mash the ball, before struggling with injuries. Davis, on the other hand, is still looking for major-league employment.
Some of the highest exit velocities belong to sluggers like David Ortiz, Josh Donaldson, Miguel Cabrera, and Giancarlo Stanton. Perhaps this is not surprising. There are, however, some players who are not in the upper echelon of MLB, such as Chris Carter or Khris Davis. Both of these sluggers have low batting averages, but high exit velocities. At the same time, both of these players have solid slugging percentages, both fluttering around .500. What can this data tell us? Is exit velocity related to batting average? Slugging percentage? wOBA?
My initial thoughts pointed me towards BABIP (batting average on balls in play). My thinking was that if these players hit the ball harder, on average, then their contact will more likely than not will find its into being a hit. If the ball is hit harder, the defense has less time to react and make a play. I was looking at BABIP instead of just batting average, since BABIP will overlook a player’s tendency to strike out. A lot of the guys with high velocities are big swingers, so it would make sense if they tend to swing and miss. So I set out to test these hypotheses, and the results may surprise you.
At first, I looked at the relationship between BABIP and exit velocity by performing a linear regression between the two. Here is the result:
No relationship, at all. R-squared of 0.03. Looks like I’m 0 for 1 so far. My theory that harder-hit balls would result in more hits, on average, looks to be proved incorrect, as there is no relationship between the two in the data. Perhaps this aligns with the idea that a pitcher really has no control of a ball once it is put in play (unless it is a HR), as unless the batter hits a HR, he still has little or zero control over the result (as a reminder, HR is not included in BABIP since the ball is not in play).
So, I will continue to my next ideas. If these players are big swingers, they probably strike out more, right? Well, sort of; a weak correlation exists, if any at all. I’ll take the loss on this one — 0/2. With a correlation of 0.11, it is hard to say a relationship exists. Here is the graph:
I then looked at other hitting metrics to see if a relationship exists. Specifically, I looked at the stats generally associated with exit velocity: Home runs, slugging percentage, and isolated power.
First, I’ll show the relationship between the two. A relationship definitely exists here. It may not be a direct relationship, but players with high exit velocities had more home runs. Now, some of this is tied to other factors, such as how often they could make contact with a pitch, what their fly-ball and ground-ball rates are, and how often they strike out. These various factors will also play a role in the amount of home runs hit, as will exit velocity. Nonetheless, as one might expect, a relationship exists. The R-squared on the regression is 0.37. Here is the graph:
Next, I looked at slugging percentages as well as isolated power. The difference between these two metrics is that isolated power equals batting average subtracted from slugging percentage. It tracks how often a player hits for extra bases, since singles are subtracted out of the equation. Nonetheless, both of these metrics track total bases and include more information about the hitter’s power.
After running my regression between slugging percentage and exit velocity, the graph shows another relationship. Again, it is a weaker relationship, but a relationship exists. The R-squared on the regression again was 0.37, so about the same value as home runs and exit velocities. So again, players with higher exit velocities are more likely to have a higher slugging percentage. Here is the graph:
Isolated power again shows a similar relationship, as the R-squared on the regression was 0.39. Other factors explain isolated power, just as they do with slugging percentage and home runs, which goes to show that other factors are important as well, such as strikeout rate. Nonetheless, isolated power is related to exit velocity in a positive notion.
For those wondering, I left out metrics such as OBP and wOBA because they incorporate how often a player walks, which has nothing to do with how hard a player hits the ball. I did run the regressions, and the R-squared values were around 0.30 for both metrics.
So what does this all mean? Should teams focus on exit velocity? What about launch angle?
For the record, launch angle did seem to have a weak relationship with HR, with an R-squared value of 0.25, so another relationship seems to exist.
Wrapping it all up, it seems that exit velocity is a good way to determine the power of a player. Yes, there are other things, such as launch angle, strikeout rate, fly-ball and ground-ball rate, and other factors. Is it the end-all, be-all of a player? No, of course not, but it may be better able to tell a player’s true power than a recent stretch of hot play. Also, players must also learn to work the count and draw walks, which is separate from exit velocity.
Nonetheless, it is smart to look at exit velocities. There are other important factors, and teams should not neglect these factors, but focusing on exit velocities is a good way to determine the raw power of a player. Also, it can show the potential in an undervalued player, who may have a low batting average, but has an ability to hit for power that is hiding beneath a cold stretch.
Anyways, it looks like major-league baseball teams do know more than me. Oh well, I’m working on it.