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While there have been many pitching biomechanics studies over the past 25+ years, we have not seen any authors specific in their recommendations linking pitching mechanics to performance and injury prevention. \u00a0While the results of these studies have provided quantitative biomechanical data outputs, the interpretation of the results of that data have all been qualitative rather than quantitative in nature. \u00a0In order to make necessary and important changes in pitching evaluation and instruction, appropriate measurement technologies and techniques need to be synergistically implemented in order to provide quantitative metric outputs for performance analysis and injury prevention.<\/p>\n
I am going to provide details on the biomechanical evaluation technologies and techniques necessary for\u00a0development of high-level biomechanics programs that will provide the relevant\u00a0quantitative<\/strong> data for both performance analysis and improvement and identification of injury mechanisms to the pitching arm side (PAS) extremity. \u00a0These plans are necessary to transition from a more typical academic research platform currently used to a results-driven applied research program. \u00a0Pitching biomechanics research has been extremely slow in adopting new technologies and techniques necessary for the measurement and analysis of high-speed motions for the purpose of improved<\/strong> performance with reduced injury probability<\/strong>.<\/p>\n I stress improved performance in the previous statement because that criteria is necessary to progress pitching biomechanics research and analysis to the next level. Currently, academic studies are performed with very specific intents, such as to compare different subject groups, or different pitch types, or to analyze one specific area of the kinematic sequence. Unfortunately they do this using measurement methodologies that are often times inadequate to elicit any quantifiable PAS kinematic<\/a> differences. \u00a0These studies pass through the peer review process<\/a> and end up getting published. \u00a0However, have they met the quantifiable performance improvement criteria? \u00a0Usually they fall short of this very important, yet often neglected step. \u00a0That is why pitching biomechanics studies have not really brought any tangible results to MLB, NCAA, or even high school baseball as pitcher arm injuries, specifically ulnar collateral ligament (UCL) tears are at an all-time high. \u00a0Even worse is that the mindset seems to be that UCL repair through Tommy John surgery is an acceptable side effect of being a modern day pitcher.<\/p>\n The main reason why baseball biomechanics research has failed to make a strong inroad into the baseball market is the use of measurement systems and methodologies that do not allow true quantifiable performance analysis, as they do not have sufficient accuracy<\/a> to do so. \u00a0Even when there are available evaluation systems, they are typically used by themselves and not in conjunction with other systems or modeling techniques that together could provide quantifiable performance improvement<\/strong>. \u00a0The unfortunate reality is that there are technologies and techniques that can currently be used to accomplish that, but the majority of studies are still using outdated systems and processes that are inadequate for true performance analysis.<\/p>\n I blame that on the fact that to date there has been no end user with strict demands on the outputs provided from modern day pitching biomechanics research. \u00a0MLB teams have used biomechanics studies more as a curiosity than anything. \u00a0The biggest limitation is that the front offices and coaching staffs don’t really have anyone that understands the data enough to implement changes, much less demand stricter outputs. \u00a0A secondary blame goes to the biomechanics research industry as a whole for not progressing from methodologies established over 20+ years ago, when technology and computational power limited the capabilities of measuring and analyzing high-speed sports motion. However, the reality is that if there are no demands from an end user, they are going to continue using the same methodologies they have used for the last two decades. Unfortunately these methodologies are too often incapable of providing the required accuracy for the kinematic outputs required for quantitative-based performance analysis and injury prevention<\/strong>.<\/p>\n The baseball biomechanics industry uses many of the same technologies used in the golf biomechanics industry<\/a>, except they are over a decade late in adopting these same technologies. \u00a0A very compelling argument could be made that the use of biomechanical technologies and techniques are even more important in the baseball industry, as the player is the only focus of the analysis. \u00a0In the golf biomechanics market, a lot of money was spent on high-level biomechanics programs to understand the interaction between golfers and clubs, with the intent of developing club fitting programs that provided increased sales for the company. \u00a0Ultimately the biomechanical programs had to provide a competitive advantage for the design, testing, and fitting of the golf equipment. \u00a0In baseball, the utilization of advanced biomechanical evaluation technologies and techniques is strictly to improve existing performance of the athlete, while also providing advanced analytical capabilities for injury identification and prevention.<\/p>\n Big data has recently become the norm in sports analytics, as front offices race to hire computer science graduates to implement data analysis capabilities within their organizations. \u00a0There are many different forms of data that an organization can use: 1.) Raw data; 2.) Data visualization; 3.) Data analytics; and 4.) Predictive analytics. Most MLB front offices have implemented some type of\u00a0sabremetrics-based analytics department. \u00a0These analysts are typically working on data visualization and data analytics. While they may claim they are working on predictive analytics, it is incredibly hard to do that when working with player output data. \u00a0The focus needs to be on developing and gathering player input data, with the important caveat that the input data needs to be capable of differentiating performance levels. \u00a0Only then will analysts be able to truly provide predictive analytics, which will separate them from their competition.<\/p>\n Recently Mark Cuban invested in the Australian-based company Catapult Sports. \u00a0One of the main reasons that Cuban invested in them was that he strongly believes that personalized healthcare will be one of the next great technological advances. \u00a0The example he has given is rather than comparing your blood levels to the general population, you should compare your levels against your own baselines and history in order to determine your personalized treatment. \u00a0Due to that believe, Cuban’s Dallas Mavericks team was the first NBA client to use Catapult Sports<\/a> athlete performance monitoring system. From\u00a0the Dallas Morning News Cuban said<\/a>, \u201cAnything that makes us smarter about our players\u2019 health is a win for us.\u201d \u00a0He added, \u201cData acquisition is critical to being proactive with every element of player health and performance, and Catapult is a key product for us in that area.\u201d \u00a0The Catapult Sports system uses a\u00a0wearable inertial sensor technology that monitors and assesses players\u2019 training loads. \u00a0The player input data is then fed into a Software as a Service (SaaS) cloud computing algorithm that provides predictive analytics regarding among other things an athletes readiness to play.<\/p>\n Another good example of the application of technology and performance is NASCAR and F1 racing teams. \u00a0With restrictions on how much time these\u00a0teams have for practicing and testing new configurations, they rely on gathering as much data as possible for analysis and verification of simulation results. \u00a0This article on big data and analytics says this about the Lotus F1 team setup<\/a>:\u00a0“Using custom and off-the-shelf products, the IT team builds its own private cloud running 50 virtual servers on site at each race. This lets the group collect and analyze as much as 30 MB of data per lap from as many as 250 sensors on the race car.<\/em>” \u00a0Their technology implementation uses “the cutting edge in cloud, big data, Internet of things, analytics, and continuous development.” <\/em><\/p>\n Both of these examples provide very good details on how professional sports teams are implementing performance monitoring systems using advanced technologies that measure\u00a0player or equipment input data<\/strong>\u00a0in order to\u00a0optimize performance level outputs<\/strong>. Unfortunately, baseball is nowhere near that. \u00a0While the sabremetrics wave that has hit baseball has led to more data-driven decisions in the front office, the analytics effort is still focused on analyzing output data<\/strong>. \u00a0The focus and effort of a cutting edge MLB front office needs to be focused on development of programs that provide appropriate input data for player evaluation and development predictive analytics.<\/p>\n I have provided performance based research programs for both academic research programs<\/a> as well as for a Major League Baseball (MLB) performance analysis and development program<\/a>\u00a0on my website. \u00a0These two programs are differentiated because they operate under different guidelines, end use requirements, and cost structures. \u00a0There are commonalities between the two programs and they share a same end goal, but the immediate “low hanging fruit” outcomes serve different purposes. \u00a0I have no doubt that in the very near future these programs will be much more similar in structure and scope.<\/p>\n In support of these topics, I have created a separate website for documenting both MLB hitting and pitching mechanics at\u00a0http:\/\/mlbmechanics.wordpress.com\/<\/a>. \u00a0This site will be used as a depository of animated gifs of high-speed video of MLB players in action. \u00a0They are strictly used for documenting biomechanical events of MLB players – e.g., hip initiated rotational dynamics, connectivity of arms to torso during the swing, pitching arm side (PAS) shoulder kinematics pre-arm cocking, PAS external\/internal rotation focus, combined PAS pronation and elbow extension characterizations, finger grip orientations and ball contact for different pitch types, etc. \u00a0Due to the strict enforcement of the MLB Advanced Media (BAM) group, the adjacent site unfortunately has to be a private site. \u00a0I will occasionally provide high-speed animated gifs on this website in support of certain topics.<\/p>\nShare this:<\/h3>