Beyond The Radar Gun: How Dean Jackson uses Theia3D For Incremental Velocity Analysis

Summary

This article is a first in a miniseries that breaks down what motion capture can show beyond radar, ball data, and standard video. In this first installment, Dean breaks down how he uses Incremental Velocity Analysis, or IVA, to study what changes biomechanically as pitchers move closer and closer to max output.

Dean Jackson is a former professional pitcher who now trains athletes across MLB, AAA, Liga Mexicana, and other professional baseball environments. Dean’s IVA work examines how movement variables shift as pitchers move closer to max output, providing a clearer view of the biomechanical strategies each athlete uses to create velocity.
‍

Incremental Velocity Analysis: Studying the Path to Max Output
‍

‍

For his IVA datasets, Dean uses custom-built analysis tools that compare velocity against specific biomechanical variables from Theia3D.

The variables Dean analyzes include:

Max Shoulder IR Ang Velo
Max Shoulder ER Ang Velo
Max Elbow Extension Ang Velo
Peak Rotational Torque into Layback
Max Shoulder Horiz Abd/Add Ang Velo into Max Scap Retraction
Max Shoulder Horiz Abd/Add Ang Velo out of Max Scap Retraction
Max Shoulder Horiz Abd
Max Shoulder ER
Max Global Torso Ang Velo (all 3 planes)
Max Global Pelvis Ang Velo (X and Z planes)
Max Torso-Pelvis Ang Velo (all 3 planes)
Max CoM Velo (X and Z planes)
Max Rear Hip Extension Ang Velo
Max Rear Knee Extension Ang Velo
Max Rear Ankle Extension Ang Velo
Max Torso-Pelvis Angle (all 3 planes, min and max for X and Y)
Max Hand Speed

*Example IVA graph showing ball velocity on the X axis and a selected biomechanical variable on the Y axis.*

Dean also uses tools that look at full signal data for positions and angular velocities across major body segments. This gives him a more detailed way to compare how movement patterns change across throws.

*Example full-signal view showing kinematic data across throws, with the ability to compare velocity, group throws, and evaluate how movement changes across the dataset.*

How Theia3D Connected Velocity to Movement
‍

Many of the changes that matter in high-speed throwing are not easy to see accurately from video alone:
‍

“Radar tells me what changed in the velocity, and I can see some visual changes with my eyes on video. But my eyes are not even close to the accuracy of Theia3D. For example, there’s not a chance I could tell the difference between a max torso angular velo of 860 deg/sec vs 960 deg/sec, but that's the difference between 84 and 92 for the athlete in Figure 1 above.”

‍

The Takeaway: More Than One Way to Find Velocity

One of the more interesting takeaways from Dean’s IVA work is not just how many variables change as velocity increases, but how differently those changes show up from athlete to athlete:

“The main takeaway from the IVA project was that there's a huge number of different levers guys can pull to throw harder. Some are the main pathways coaches already understand as primary ways to add velocity. But there are also a lot of movements that seem to look more like compensations, that a lot of coaches would say are hurting the athlete.

Previously, I was mostly coaching one main pathway that I thought was most efficient. I still think that pathway is valuable, but IVA helped me see that there can also be value in understanding how an athlete already creates velocity. For example, if a high level pitcher already has great command and he just needs a tick or two, it may be easier to refine or leverage what he already does than teach him a completely new pattern.”

In this workflow, Theia3D enabled Dean to see that an athlete’s existing strategy may be more important, and more usable, than a coach can determine from radar or video alone.

See More

Interested in seeing how Theia3D can help connect pitching outputs to full-body movement data? Contact our team to learn how markerless motion capture can fit into your baseball performance workflow.

Recent Posts

New Research on Theia3D: Comparing Markerless and Marker-Based Shoulder Kinematics Across Full Arm Range of Motion

A 2025 study published in the Journal of Biomechanics compared shoulder kinematics measured with Theia3D markerless motion capture against gold-standard marker-based motion capture across full arm ranges of motion. The results show strong agreement for many movements, particularly in the coronal plane, while also highlighting where caution is still required, especially at the extremes of ranges of motion and in transverse-plane rotations.

Baseball Tracking System: A Practical Guide to Evaluating Your Options

Baseball tracking systems can measure everything from ball flight and bat path to workload and full-body biomechanics, but each category answers a different question. This guide compares leading options and explains how to choose the right system based on what your program needs to measure, where it will operate, and whether it can connect athlete mechanics to baseball outcomes.

How to Choose Motion Analysis Software: Features, Accuracy, and Workflow Fit

Choosing motion analysis software depends on more than whether it can track movement. This guide compares leading tools by output type, setup complexity, validation, data ownership, and workflow fit so research and performance teams can choose the right system for their needs.