Dynamic Modelling and Simulation of Major League Baseball Pitching Biomechanics to Reduce Ulnar Collateral Ligament Injury and Improve Performance

Abstract

Baseball teams of all levels rely on their pitchers to achieve the ultimate goal: win games. This fact is exacerbated in professional baseball, where pitchers are considered the most important members of the team and are compensated as such. Because of this, many efforts are made to maximize the performance of pitchers, without compromising their health. Doing so is no easy task and is often considered a balancing act, where teams want to get the most out of their pitchers, while making sure they are available long-term.

Historically, scientific work in baseball pitching typically constitutes the study of kinematics and kinetics of pitching motions, to extract relevant joint angles, forces, and moments. However, most efforts in this space do not consider elite calibre athletes. In high-level organizations, most research and development work is conducted internally, by sports science and analytics departments, which are often kept confidential, to not give opponents the same competitive advantage that the analyses might produce.

With the modelling, simulation, and computing tools available today, an opportunity exists to conduct a comprehensive analysis of elite baseball pitching biomechanics, that can inform the management and training of these athletes, without the need for physical experimentation that may impart additional strain on the pitcher. In collaboration with a confidential industry partner (a high-level baseball organization), practical strategies to reduce injury risk and improve performance for their pitchers are deduced from in-game kinematic data, shared and anonymized by the researcher partners. This work also provides expertise to the participating team in modelling and simulation, which can contribute to their on-field success and future scientific work.

This is done by employing a series of simulation strategies, the first being an inverse dynamic approach. The shared kinematic data is initially used to track the pitching motion in an inverse kinematic analysis, which will provide estimates of joint angles over time and an animation of the motion. The same data is then used to calibrate the dimensions and joint kinematics of the athletes for the development of a full-body, skeletal model. An inverse dynamic analysis was then performed to estimate the net joint moments responsible for this motion, while also considering the model’s reliability at estimating joint angles and net joint dynamics. Particular attention was paid to the throwing arm, namely the shoulder and elbow joints, to understand the etiology of ulnar collateral ligament (UCL) injuries in pitchers.

Next, a predictive forward dynamic simulation was developed. This approach was generated by solving an optimal control problem for the muscle forces and activations that maximize ball speed during a pitch, with constraints on the model's physiology, joint kinematics, and joint kinetics. First, a comprehensive musculoskeletal (MSK) model that is tuned and scaled to represent a baseball pitcher’s dimensions and forcer generating capabilities was developed. Using this model, a series of “what-if” simulations were conducted to explore the influence of variable pitching mechanics on UCL loads, while continuing to prioritize the identification of factors that have the most impact on elbow valgus torques, without compromising performance. Specifically, the predictive simulations were used to identify the pitching motion that minimized UCL loading while maintaining pitching speed. The cost function used involved minimizing actuation effort while achieving a competitive pitch speed. Constraints were imposed on joint angles and speeds, muscle activations, and ball speed to reflect realistic bounds for elite athletes. The final cost function of the optimal control problem rewarded reaching a desired pitch speed threshold, achieving a lead foot speed of zero at ball release (to maintain balance and prevent slipping), and minimizing the actuation of the shoulder rotation actuator (shoulder external rotation), which tends to load the UCL. Results demonstrated how variations in pitching mechanics alter UCL loads. For instance, faster pitches exhibited greater contralateral trunk tilt and higher arm slot, elevating UCL loads, while slower pitches demonstrated greater ipsilateral trunk tilt with a lower arm slot (sidearm), reducing UCL loads.

Throughout all of the simulations, care was made to evaluate the robustness of the model through validation using a series of case studies and comparisons to literature. Using these validated models, findings relating to the variability in pitching strategies were identified. Each pitcher and simulation analyzed employed unique biomechanics to achieve the defined costs, while all producing competitively viable throwing speeds. While performance was prioritized, each set of throwing mechanics posed different levels of threat to the athlete's UCL, indicating that these highly individualized motions can create inconsistent loading patterns within subjects. These findings can be used to develop personalized pitching and conditioning programs aimed at maximizing throwing velocity while reducing injury risk, without the need for strenuous physical testing.