Examining The Future Of Hitting: Bat Speed, Swing Path, and Biomechanics
Major League Baseball organizations are always attempting to identify the next competitive advantage in order to better position their organization for long-term success. The best organizations are able to take the findings from these breakthroughs and apply them to fields such as player development and scouting in order to make best use of these competitive advantages. In recent seasons, significant progress has been made on the pitching side of the game, with more players being able to reach their maximum velocities via biomechanical analysis and training, and create better pitches to utilize in their pitch arsenal via pitch design sessions and the creation of pitch modeling metrics such as Stuff+.
At the advent of the Statcast era, introduction of terms like Launch Angle, Exit Velocity, and Hard Hit Rate into the public lexicon changed the way that athletes and fans viewed hitting. While these statistics provided greater insight into how offensive production is created, these metrics are descriptive metrics, providing detail on “what happened” as opposed to “how it happened”. While predictive metrics, such as Stuff+, have dominated pitching analysis over the past year, there are few predictive metrics in the public sphere regarding hitting analysis. For these reasons, I believe that predictive metrics for hitters, such as Bat Speed, Swing Path, and Biomechanical data are the new frontier in hitting analysis, and the implementation of these metrics will be the next competitive advantage for Major League Baseball organizations.
Bat Speed:
One metric that is important when conducting predictive metrics regarding a player’s offensive performance is a player’s Bat Speed. Since the introduction of Statcast in 2015, much research has been conducted regarding the correlation between exit velocity and offensive production, as players who hit the ball the hardest tend to create the most offensive production. This is confirmed by a simple look at Baseball Savant’s leaderboards, as the players with the highest Hard Hit Rates are among the best hitters in the entire league. However, as mentioned earlier, Exit Velocity only describes “what happened”, not “how it happened”. Given its relative stickiness year-to-year, most observers would believe that a player’s Hard Hit Rate can not be changed and is an inherent ability that one simply either possesses or not. In fact, a FanGraphs article from last year describes this exact line of thinking:
Thanks to the Driveline OpenBiomechanics Project, the public can now have a view into understanding the factors that go into a player’s exit velocity, gathering insight into how a player is able to improve their exit velocity and therefore, their overall offensive performance. When analyzing the dataset of swings that Driveline provided for the OpenBiomechanics Project, there appears to be a statistically significant relationship between a player’s Bat Speed and their Exit Velocity.
Given these findings, Bat Speed can be used as a predictive metric for Exit Velocity. This connection is intuitive, as one with a basic knowledge of physics would expect that swinging a bat faster would make the resulting contact leave the bat at a higher velocity. Therefore, if a player is focused on improving their Exit Velocity, they should place an emphasis on improving their Bat Speed.
One training method that has been used in recent years to improve a player’s Bat Speed has been weighted bat training. Popularized by Jason Ochart, founder of the Driveline Hitting Program and currently the Director of Hitting Development and Program Design with the Boston Red Sox, weighted bat training uses both overload and underload bats to aid hitters in developing more efficient movement patterns in order to generate more Bat Speed.
Multiple Major League Baseball players underwent weighted bat training over this past offseason, including three members of the Los Angeles Dodgers: Mookie Betts, J.D. Martinez, and Max Muncy. In 2023, all three players saw increases in their 90th percentile Exit Velocity, Hard Hit Rate, and Expected Weighted On-Base Average On Contact (xwOBAcon). While Bat Speed statistics for Major League players are not publicly available, it can be safely assumed that this increase in production can be attributed to their utilization of weighted bat training and an emphasis on improving their Bat Speed.
Given these findings and the demonstrable success players have had with weighted bat training this season, it can be safely assumed that more hitters will be training to improve their Bat Speed over this upcoming offseason in order to improve their overall offensive output. As shown by the adoption of weighted bat training by Major League Baseball players, Bat Speed has emerged as a pivotal, predictive metric in assessing a player’s offensive potential and will be an important metric used in the future of hitting analysis.
Swing Path Analysis:
Another metric that will be valuable in predicting a hitter’s offensive production in the future will be data regarding a player’s Swing Path. In order to hit a baseball and make sound contact with an incoming pitch, hitters need to predict the movement profile of a given pitch and make the appropriate adjustment to “get on plane” and make contact with the pitch at an appropriate angle to make ideal contact.
Launch Angle, a descriptive metric, represents the vertical angle at which the ball leaves a player’s bat after being struck. While Launch Angle provides a good description of whether a batted ball is going to be a ground ball, fly ball, or line drive after being hit, it only measures an angle at point of contact, providing little information regarding how a player arrives at a given Launch Angle.
Swing Path analysis, which holds potential as a predictive metric, quantifies the relative positions of the barrel and the ball in space. This provides insight into the efficiency of a hitter’s path to the ball, how a hitter achieves a given Launch Angle, and why certain hitters are more adept at hitting specific types of pitches.
By quantifying and analyzing a player’s Swing Path, organizations will be able to both better understand the strengths of their hitters and identify players who need to improve their Swing Path in order to produce a higher quality of contact. The San Francisco Giants appear to be one team that already uses Swing Path data effectively, in order to generate favorable matchups against opposing pitchers. When the Giants won 107 games in 2021, the team routinely used lineups that featured hitters with flatter swings against pitchers with upstairs fastballs, and lineups that featured hitters with steeper swings against pitchers who predominantly utilized Sinkers. Utilizing Swing Path data in this manner allows for teams to create better matchups against opposing pitchers, going a step further than simply taking left/right platoon splits into account.
Ethan Moore wrote a fantastic article on Medium in 2022 about how he views the future of utilizing Swing Path data, and does an outstanding job describing the role that this data can play in both scouting and player development. In his opinion, Swing Path data can be used to quantify both bat placement and batter timing, which can provide insight to player development on how to improve hitters in their organization.
Player development teams can utilize Swing Path data in various ways, such as adjusting a hitter’s timing to avoid being early or late to incoming pitches and modifying a hitter’s swing to prevent swinging over or under the incoming pitch. For instance, if a player consistently swings over a Changeup with two strikes, Swing Path data can offer insights into the hitter’s decision-making process, enabling focused improvement on the player’s swing decisions. Additionally, scouting departments can leverage this data to identify players from other organizations with specific, correctable swing deficiencies, thereby allowing the organization to acquire undervalued talent.
Analyzing a player’s Swing Path has emerged as a pivotal tool in forecasting a player’s future offensive production, offering insights that go beyond the capabilities of descriptive metrics such as Launch Angle. The utilization of Swing Path data by teams, such as the 2021 San Francisco Giants, underscores its potential in optimizing lineup configurations and exploiting pitcher-hitter matchups, and will allow for organizations to identify and refine underrated talent with correctable deficiencies in their swing. Analyzing a player’s Swing Path data stands poised to redefine strategies, enhance player performance, and be another important metric used in the future of hitting.
Biomechanics:
Collecting and analyzing each hitter’s Biomechanical data will also be a critical part of player analysis in the future. Biomechanics is defined as the study of the mechanical laws relating to the movement or structure of living organisms. In recent seasons, analyzing a player’s Biomechanical data has been a critical element in developing pitchers, as some Major League organizations and other groups such as Driveline Baseball have dedicated significant resources into building labs used to capture Biomechanical data from players to use in player development. This article in The Athletic by Eno Sarris and Alec Lewis from 2022 goes in detail about how these organizations are currently using Biomechanical data:
Since the advent of collecting and utilizing Biomechanical data in the 1980’s, there has been a persistent issue regarding how the data is collected, as the motion capture process that is typically used for collecting this data via markered motion capture, which can not be worn in a live setting, causing the data to only be collected in controlled settings. With the creation and implementation of technologies such as Hawk-Eye Pro and Kinetrax, Biomechanical data is able to be collected without use of markers, allowing for the data to not only be more easily collected, but also allowing the data to be collected in live settings such as game play.
The advent of markerless motion capture is a great opportunity for the advancement of using Biomechanical data when analyzing hitters. With the ability to collect data more easily and in more settings, Major League organizations will be able to have a much better understanding of the true talent level of the hitters in the organization, and will be able to make clearer, data-driven decisions on how to develop these hitters in order to maximize their offensive potential.
In the video above, Jason Ochart goes into incredible detail about all the phases of a hitter’s swing, and which Biomechanical checkpoints should be reached in order to produce an effective swing and improve offensive performance. The value in analyzing which players hit these checkpoints is immense, as it provides a framework for how to improve a hitter’s mechanics, allowing for teams to better develop their own hitters and make targeted acquisitions of players whose mechanics can be corrected by player development. For example, if an organization is very effective at developing a player’s pelvis rotation at foot plant, then that organization can target players with a deficiency in this area, knowing that there’s a high likelihood that this element of their swing will be “fixed” when entering their organization, likely raising the player’s overall potential.
The evolution of Biomechanical data collection and analysis stands as a cornerstone for the future of player analysis and development in baseball. The integration of innovative technologies, such as markerless motion capture, has addressed long standing challenges, enabling data collection in live settings and providing a more comprehensive understanding of a player’s true talent. This advancement offers Major League organizations a unique opportunity to make informed, data-driven decisions, optimizing player development and identifying targeted acquisitions with high potential for improvement. The continued exploration and application of Biomechanics will undoubtedly play a pivotal role in uncovering player potential, refining development strategies, and will be a critical metric used in the future of hitting analysis.
Concluding Thoughts:
As Major League Baseball organizations continue their constant pursuit of competitive advantage, the in-depth exploration of Bat Speed, Swing Path, and Biomechanics represents the future of hitting analysis. Bat Speed has revealed itself as a predictive metric, with training methodologies like weighted bat training exemplifying the potential to enhance offensive production. Swing Path Analysis, as demonstrated by its strategic implementation by teams like the San Francisco Giants, offers nuanced insights; enabling organizations to identify and refine player talent through targeted adjustments. Advancements in Biomechanics, facilitated by innovative technologies and detailed analyses of swing phases, has opened new avenues for understanding and developing a player’s inherent abilities as a hitter.
Advancements in these three areas will help enhance the scouting and player development process, as scouts will have more insight on “what to look for” regarding potential acquisitions and player development departments will be able to make more informed decisions about how to improve individual players. Speculatively, I wonder if all three elements of the future of hitting can be combined to create “Swing+”, a predictive model which can be used to project offensive performance similar to how Stuff+ is currently used to evaluate pitchers. Since every swing varies depending on the situation and the pitch’s location in the zone, constructing such a metric would be complex, a task likely reserved for top data scientists. However, I believe that the construction of such a metric can be a valuable tool in understanding a hitter’s “true” talent level. As we venture into the future, the integration and continued evolution of these analytical tools stand poised to redefine the game, uncover untapped potential, and elevate the standard of excellence in the science of hitting.
Follow @MLBDailyStats_ on X (Twitter) and Adam Salorio on Medium for more in-depth MLB analysis. Statistics provided by FanGraphs, Baseball Savant, Driveline’s OpenBiomechanics Project, and Alex Chamberlain’s Pitch Leaderboard.
