Exploring Key Metrics and Methodology for Analyzing Offensive Performance
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Evaluating a hitter’s offensive performance can be one of the most comprehensive forms of baseball analysis, as multiple facets of a hitter’s approach such as plate discipline, batted-ball quality, and luck factors all need to be analyzed in order to fully understand how a hitter’s offensive production is created. With help from advanced metrics, baseball analysts now have more tools at their disposal than ever to both accurately evaluate hitters, and to identify how a hitter can improve their offensive approach in order to reach their full potential. In this article, I break down my process for evaluating offensive players and explain how I utilize (publically-available) advanced metrics in order to analyze hitters more effectively.
Descriptive Metrics:
In my opinion, Weighted Runs Created Plus (wRC+) is the most powerful, all-encompassing metric for evaluating offensive players. As described by FanGraphs, “Weighted Runs Created Plus (wRC+) is a rate statistic which attempts to credit a hitter for the value of each outcome (single, double, etc.) rather than treating all hits or times on base equally, while also controlling for park effects and the current run environment.” wRC+ is typically the first offensive metric I look at while evaluating a Major League player because it is the best statistic at evaluating a player’s total offensive production, due to the statistic’s strong correlation with runs scored.
While wRC+ provides great insight into how well an offensive player is performing, there are other metrics available that better describe why an offensive player is performing at a certain level. Two statistics I use to determine whether or not a player is experiencing “batted ball luck”, are xwOBA (Expected Weighted On-Base Average) and BABIP (Batting Average on Balls in Play).
BABIP (Batting Average on Balls in Play) was one of the first sabermetrics created in order to identify whether or not a batter was experiencing “batted ball luck” and producing a higher/lower batting average than expected. Similar to the mathematical concept of regression to the mean, the league average for BABIP remains at or around .300 each season. Therefore, it is assumed that an individual player’s BABIP will stabilize at .300 over the course of a full season. If a player’s BABIP is at .450 for example, it can be expected that the player’s batting average will sharply decline over the remainder of the season as the BABIP regresses to the league average. However, there are some flaws to BABIP, as some players do not consistently average a .300 BABIP. Players that hit a lot of line-drives, such as Freddie Freeman, or have high sprint speeds, such as Trea Turner, tend to have BABIP’s above league-average; while players that hit an excessive amount of fly balls tend to have BABIP’s below league-average.
xwOBA (Expected Weighted On-Base Average) attempts to remove the subjectivity of interpreting BABIP when it comes to evaluating the “luck factor” of offensive players. xwOBA is formulated by using exit velocity, launch angle, and sprint speed in order to predict a hitter’s wOBA (Weighted On-Base Average). Similar to wRC+, wOBA is “a rate statistic which attempts to credit a hitter for the value of each outcome (single, double, etc.) rather than treating all hits or times on base equally”, as described by FanGraphs. If a player’s xwOBA is higher than their wOBA, then it can be assumed that the hitter is experiencing “batted ball luck”, while if a player’s xwOBA is lower than their wOBA, then it can be assumed that the hitter has been “unlucky”. Comparing a hitter’s xwOBA to their wOBA is an effective method for determining how “lucky” a hitter has been, since it removes the element of subjectivity that is involved in evaluating a player’s BABIP. I typically prefer the xwOBA/wOBA method when evaluating Major League players, and the BABIP method when evaluating players at levels where Statcast is not available (such as public Minor League data, college players, etc.).
Predictive Metrics:
With the advent of the Statcast era in 2015, the public now has access to data such as Barrel%, Hard Hit%, and Exit Velocity, which serve as predictive metrics in order to determine an offensive player’s potential. Maximum Exit Velocity is one of the most important and most discussed predictive metrics for hitters. Simply put, I view Maximum Exit Velocity as a means to measure a player’s power potential. If a player has a high Max. EV, then they have greater potential to hit for power than a player with a low Max. EV.
By looking at the Statcast leaderboards on Baseball Savant, it is easy to see a correlation between Max. EV and total offensive production. Hitting the ball hard typically produces better results! If a player has a high Max. EV and low power numbers, it is usually an indicator that the player needs to make an adjustment regarding where they are hitting the ball. Typically, this means the player is hitting a lot of ground balls or tends to not pull the ball a lot.
While some may argue that Max. EV doesn’t matter if a batter hits a very hard ground ball, I disagree with this viewpoint for two reasons. First, I would prefer that a batter hit a hard-hit ball on the ground as opposed to a soft-hit ground ball, as the hard-hit ball has a much better chance of turning into a base hit. Second, for the reasons mentioned earlier, if a player hits a lot of hard ground balls, it is an indicator that they can hit for power if they are able to lift the ball more often. One example of this is Yandy Díaz of the Tampa Bay Rays, who for years would hit for a high Max. EV while hitting a lot of ground balls, resulting in a low power output despite a high wRC+. In 2023, Díaz has hit the ball in the air more often, nearly doubling his Isolated Power simply by lifting the ball more and tapping into his raw power, living up to the power potential that his Maximum Exit Velocity suggested he had.
Launch Angle is one of the most misunderstood Statcast metrics by the general public, and while it has numerous effective uses, the metric can often be misutilized. One general misconception about Launch Angle is that hitters should always attempt to make contact at a specific high angle, which will in turn guarantee offensive success due to the amount of home runs a batter will hit. By looking at the Statcast leaderboards on Baseball Savant however, it is easy to see that there is little to no correlation between Average Launch Angle and total offensive production. In other words, Maximum Exit Velocity is much more predictive of offensive production than Average Launch Angle. This is largely because different launch angles are ideal for different parts of the zone (ex. a “smaller” launch angle is ideal for a pitch at the top of the zone), and recent research indicates that pitchers have a large amount of control over a hitter’s launch angle. Yes, do not get me wrong, it is very very good for a hitter to hit the ball in the air (and even better if they can consistently pull the ball in the air), however I place more focus in analyzing a hitter’s mechanics and approach as opposed to “chasing” a specific average Launch Angle when I attempt to analyze an offensive player who can tap into more of their power potential.
Barrel Rate and Hard Hit Rate are two more metrics that have been created in the Statcast era, which I use to analyze offensive players. As defined by Statcast, a “Hard Hit” is a ball that is hit at an exit velocity of 95 mph or higher, and a “Barrel” is a “batted ball event whose comparable hit types (in terms of exit velocity and launch angle) have led to a minimum .500 batting average and 1.500 slugging percentage since Statcast was implemented Major League wide in 2015.” In simpler terms, “to be Barreled, a batted ball requires an exit velocity of at least 98 mph. At that speed, balls struck with a launch angle between 26–30 degrees always garner Barreled classification. For every mph over 98, the range of launch angles expands.”
Research has shown that a player’s Hard Hit Rate typically remains stable throughout the duration of the career, which indicates to me that most (if not all) teams have not figured out a way to increase one’s Hard Hit Rate as part of player development. Therefore, a player’s Minor League Hard Hit Rate is often a good indicator of whether or not a player is able to have success at the Major League level. Since Minor League Statcast data is not easily accessible to the public, it can be difficult for public analysts and observers to draw conclusions about Minor League players in the same manner as Major League organizations.
*Update (10/23): For further reading on how a player can improve their Hard Hit Rate, check out this article I wrote about The Future of Hitting:*
On the other hand, a player’s Barrel Rate is “less sticky” from season-to-season, and can often be used as another way to analyze how much a player is tapping into their power potential. For example, if a player has a high Hard Hit Rate and a high Maximum Exit Velocity but is still not hitting for power, improving the Barrel Rate is an ideal goal for the hitter to achieve his power potential. Referring back to the Yandy Díaz example, not only did Díaz lift the ball in the air more this season to tap into his raw power, but he also increased his Barrel Rate from 4.8% to 12.9%.
Plate Discipline Metrics:
Having good plate discipline is a skill I look for in offensive players for a variety of reasons. One, swinging at “good” pitches and not swinging at “bad” pitches often results in higher-quality contact. Two, having the ability to walk at a high rate not only adds to a player’s total offensive production, but also provides the hitter with a “safety net” to fall back on during a slump, as they are still able to consistently get on-base.
There are a few methods I use for evaluating plate discipline. One method I use is simply analyzing the player’s Walk Rate. Since a player obviously needs to take 4 pitches out of the zone in order to draw a walk, a high Walk Rate is a clear indicator that a hitter has good plate discipline. Even better, if a player has a high Walk Rate along with a low Strikeout Rate, then I would consider that player to have elite plate discipline. I frequently use Walk and Strikeout Rates to analyze Minor League prospects, as these metrics are usually quite “sticky” from level-to-level. While some regression can be expected between levels (I would not expect a player to strikeout less in MLB than they did in AA, for example), if a player displays high Walk Rates and low Strikeout Rates in the Minors, it is typically an indicator that the player has good plate discipline and will experience Major League success.
Another method of measuring a hitter’s plate discipline is by analyzing their O-Swing%, which measures how often a player swings at pitches outside of the strike zone. O-Swing% can be a better measure of plate discipline than simply looking at a hitter’s Walk Rate because this metric takes into account all swing decisions, as opposed to Walk/Strikeout Rate which only takes into account the swing decision made on the last pitch of a given plate appearance. Since Minor League plate discipline data is not accessible by the public, O-Swing% can only be used to evaluate Major League players.
Concluding Thoughts:
With help from advanced metrics, baseball analysts now have more tools at their disposal than ever to both accurately evaluate hitters, and to identify how a hitter can improve their offensive approach in order to reach their full potential. Descriptive metrics such as wRC+, wOBA/xwOBA, and BABIP adjust a player’s offensive production relative for their environment, and attempt to quantify how much “luck” a hitter is encountering; while predictive metrics such as Barrel Rate and Hard-Hit Rate use Statcast technology in order to better project a player’s future offensive production. While scouting a player in-person is still a very critical element of the player evaluation process, the usage of both descriptive and predictive metrics can help “paint a better picture” when determining how much offensive production a player produces, and aids heavily in forecasting a player’s potential future value.
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