“” Caught Looking: June 2021 - Driveline Baseball

Caught Looking: June 2021

| Blog Article, Research
Reading Time: 7 minutes

Intra versus Inter-Pitcher Comparisons: Associations of Ball Velocity with Throwing Arm Kinetics in Professional Baseball Pitchers


Since velocity is an important piece of pitching performance and one of the primary measures of player development, how increased velocity affects the physical demands on the pitcher’s arm is important to understand. This relationship has been studied before by ASMI’s group, and a study by Manzi et al. using data collected by Motus Global in the past just took another look at it. 91 professional pitchers were included in this analysis. 

Manzi and the authors of this study published in April 2021 arrived at a similar conclusion: the inter-subject correlation (including all 91 athletes) is not strong, but the intra-subject correlation (looking at a single-subject’s throws), is very strong. For example, this means that when an athlete throws a 90 mph fastball, the associated elbow torque for that throw is also likely going to be higher than if that athlete throws an 85 mph fastball. But when you compare an athlete who throws 90 mph to a different athlete who throws 85 mph, the 90 mph thrower may not necessarily have a higher elbow torque for those throws than the athlete who throws 85 mph. 

This is likely because there are many other differences between athletes that contribute to their elbow torque during the throw including (but not limited to) height, body composition, and technique. However, all that means is that there is more “noise” in the relationship. More noise just means that within a tight velocity or torque window, the relationship might not be that strong but if you were to compare a 65 mph thrower to a 90 mph thrower, you would likely see a big difference in elbow torque. That is what we see when we look at our entire biomechanics database which includes athletes of all levels from youth to MLB pitchers. 

You can see a clear curvilinear relationship (curved line; a logarithmic relationship in this case) between elbow torque (x-axis) and pitch velocity (y-axis). A curvilinear relationship means that even though it’s not a straight linear relationship, velocity goes up as elbow torque goes up, just at a non consistent rate. Manzi et al. touch on this in the limitations, but broadening the sample of velocities and playing levels has an effect on the inter-subject relationship; their study only looked at professional pitchers. 

Conclusion: it requires more force and torque to create elite fastball velocity than sub-elite fastball velocity. This makes managing that load on the body all the more important. Luckily, there are ways that athletes can measure and manage their workload. For more information, see our post about managing starting pitchers’ workloads.

Performance Improvement and Skill Transfer in Table Tennis Through Training in Virtual Reality


Recent improvements in virtual reality present some pretty intriguing opportunities for adding to training methods in sports. Interceptive sports that require a high degree of coordination also require highly specific training; training to recognize a dot on a screen likely doesn’t have the same effect on a hitter’s ability to detect pitch type as training against live pitchers. The cool thing about virtual reality is that we might be able to recreate this live training environment in a virtual world, making it much more accessible to athletes who want to get more reps in. 

One major concern however, is that if the virtual reality is not similar enough to an actual live setting, training with it may not work that great either. It could either not transfer well to competition, or even worse, it may train the skill negatively. It’s possible that training in a virtual environment might make you better in the virtual environment while also making you worse in the actual game. Not to say that is the most likely outcome, but it would require some creative research to ensure the training adaptations are net positive.

Oagaz et al. at the University of Colorado, Denver created a virtual reality (VR) ping pong training system and tested it out in an actual ping pong task. The system was tested by separating the subjects into two groups: an experimental group (9 people) and a control group (also 9 people). The control group didn’t do any training at all, so the experiment was comparing the virtual reality training to no training. This is because this is a new and introductory type of training. 

The authors found that the VR training improved ping pong skill relative to no training at all. Between the pre and post test, the group that received VR training improved on ball speed and ball height but not on total number of returned balls. These were the only ping pong performance measures they test

These results do not necessarily mean that we can throw together a virtual baseball field and start training against animated opponents, but results like these are very exciting for the future of baseball training. Especially in a sport where every rep can have a significant impact on player health, virtual training has the ability to truly change how players, coaches, and organizations approach long term development. 

Kinematic Sequence Classification and the Relationship to Pitching Limb Torques


Pitcher evaluation and development places a lot of focus on kinematic sequencing and the efficiency with which a pitcher transfers energy from the lower body segments, up through the arm, and to the ball. There is a set standard that a proximal-to-distal sequencing (PDS) is ideal.

In practice, this sequence is identified by the timing and order at which specific segments (typically pelvis, trunk, upper arm, forearm, and hand) reach their peak angular velocity. Aspects of the kinematic sequence in pitching and its relationship to injury risk factors have been a large focus of baseball biomechanics research. This study by Scarborough, et al. from 2020 introduces the first look at categorizing kinematic sequences and evaluating the relationship between them and biomechanical indicators of potential risk.

Scarborough, et al. took 3D motion capture data from 249 pitches thrown by 30 pitchers and categorized them by the kinematic sequencing pattern demonstrated in each pitch. The standard was based off of the PDS order of sequencing:

  1. Pelvis
  2. Trunk
  3. Upper Arm
  4. Forearm
  5. Hand

Those that followed that order were the PDS group. Those that differed were placed into groups defined by the first segment to be ‘out-of-order’ (pelvis/trunk – CORE group, Upper Arm – PUE group, Forearm – DUE group). 

After comparing biomechanical indicators of potential risk (absolute shoulder torques, both external rotation and extension, and elbow valgus torques) between the different groups, this study corroborated the findings that the PDS group produced lower amounts of stress on the throwing arm. Shoulder ER and elbow valgus torques were the highest in the DUE group. 

An interesting detail to note is that no pitches thrown in the study followed the 12345 order exactly. The closest was 12344, with forearm and hand angular velocities peaking at the same time, and those made up the PDS group. Additionally, the pitches in the PDS group were only 12% of the total number of pitches thrown in the study. 

While this study backs the concept of proximal-to-distal energy transfer being ‘ideal’, it gives more insight into the implications of specific kinematic sequences that differ from this standard. It also highlights the variability, within an individual, to demonstrate different sequencing. These results introduce a new way to categorize kinetic sequencing and analyze or evaluate an individual based on which sequencing they demonstrate most frequently.

Written by Kyle Lindley and Gretchen Pouch

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