From Open Loop to Closed Loop: The Missing Link in Virtual Reality Hitting
By Ken Cherryhomes ©2026
Virtual reality hitting systems and highly accurate programmable pitching machines such as Trajekt share an important advantage over live pitching: the pitch is predetermined. But VR adds something especially valuable to that equation: access. A pitching machine capable of reproducing high-level game pitching may cost hundreds of thousands of dollars and is available only in limited settings. VR can put a comparable level of programmable pitch variation in front of far more hitters, far more often, at a fraction of the cost.
That gives VR tremendous value as a training platform. The hitter can see large numbers of pitches, face different release points, velocities and locations, repeat specific pitch scenarios, and train without requiring a pitcher, field, cage, or expensive machine.
Yet despite those advantages, VR and programmable pitching machines are still generally limited by the same open-loop training model. They deliver the pitch, the hitter reacts, and the result is evaluated afterward. The hitter improves through repetition, feedback, and trial and error.
The Advantage of a Known Future State
The defining advantage of a programmable pitching environment is that the future state of the pitch is known and controlled. Before the hitter even decides to swing, the system already knows where the ball is going, how fast it is traveling, the path it will follow, and where it will arrive. That’s fundamentally different from live pitching, where the hitter, coach, and measurement system are responding to an event whose future state must first be perceived and predicted.
Yet current programmable systems largely use that advantage to create repeatable practice rather than to create an objective instructional framework. They control the future state of the pitch, but they do not fully connect that known future state to the hitter’s required future state. As a result, the technology can reproduce the problem with remarkable precision while still leaving much of the interpretation of the solution to the coach.
The paradigm shift comes from taking advantage of the information the system already possesses. Adding a three-dimensional collision map provides the missing spatial connection by defining where the known pitch should intersect with the hitter’s bat across different pitch locations. Once the future state of the pitch is linked to an objective collision requirement, the pitch is no longer simply something being delivered to the hitter. It becomes one side of a solvable relationship.
That spatial connection is the lynchpin of the closed loop. Without it, programmable pitching remains primarily a sophisticated practice engine. With it, the system gains the objective reference framework necessary for the analytical and instructional functions described in the sections that follow.
Closing the Loop
In a virtual reality simulation, the system controls the pitch and knows what is going to happen before the hitter sees it.
At first, the system can treat home plate and the strike-zone as the end of the pitch. It knows where the ball will cross that plane and how long it will take to get there. Once the hitter is added, however, the important terminal point is no longer simply the strike zone. It becomes the collision point, the location in three-dimensional space where the bat and ball should meet.
This creates a closed-loop system between the simulated pitch and the individual hitter. The system knows the exact path of the ball, the hitter has a defined collision point, and the system knows the exact time when the ball will reach that collision point. The future event the system is working toward is therefore the bat-ball collision:
Pball(tc) = Pcollision
where tc is the time when the ball reaches the collision point.
Once that collision point and collision time are known, the system can work backward. Instead of simply showing the hitter a pitch and then categorically judging the result as late, early, or on time, it can determine when the hitter must begin the process that gets the bat to the collision point on time. That is what makes the system both prescriptive and analytical. It can tell the hitter when to initiate the swing based on where the pitch is going, when it will arrive, and how much time that particular hitter consumes to get there.
Because the system operates as a closed loop with foreknowledge of the pitch trajectory, arrival time, intended collision location, and hitter-specific timing requirements, it can do more than prescribe the correct initiation point. It can also compare what was required with what actually occurred. The same information used prospectively to define the hitter’s timing requirement can therefore be used retrospectively to quantify the resulting error and identify where the hitter’s response departed from the required solution.
This closes the instructional loop. The system does not merely present a pitch, observe the outcome, and assign a broad category afterward. It establishes the required solution in advance, measures the hitter’s actual response against that solution, and uses the difference between the two to provide both instruction and analysis.
Adding the Hitter's Time to Impact
To make the system specific to an individual hitter, we need to know how long that hitter takes to get the bat to each possible collision point. That measurement is Time to Impact, or TTI. TTI is the sum of two components: Adjusted Reaction Time, called Swing Delay™, and Mechanical Swing Time.
Adjusted Reaction Time is the time between the hitter’s decision or internal go message and the start of the bat’s forward movement. It includes the hitter’s motor response delay and any additional mechanical latency, such as a hitch, that delays swing initiation. Mechanical Swing Time begins when the bat starts moving forward and ends when the bat reaches the selected collision point.
Time to Impact is the total of those two measurements:
TTI = Swing Delay™ + Mechanical Swing Time
Once the system knows the hitter’s TTI to a particular collision point and the optimal barrel orientation at that point, it can match that information to the simulated pitch. Suppose the system knows exactly when the ball will reach that collision point. It can count backward by the hitter’s TTI and determine when the hitter must begin the response, measured in time and feet, in order for the barrel to arrive at the correct collision point, at the correct time, and with an optimal barrel orientation.
Why Time to Impact Is Different
Other baseball swing-measurement systems, including current virtual reality hitting systems, measure Mechanical Swing Time, from forward swing initiation to contact. They don’t measure the hitter’s Adjusted Reaction Time from the internal go message to forward swing initiation and combine it with Mechanical Swing Time to calculate the hitter’s complete Time to Impact.
That distinction is important because two hitters can have the exact same Mechanical Swing Time but very different overall timing requirements. One hitter may convert the decision to swing into forward bat movement quickly, while another may have a longer motor response delay or additional mechanical latency before the bat starts forward. Mechanical Swing Time alone would make those two hitters appear the same from a timing standpoint, even though they’re not.
By including Adjusted Reaction Time, TTI accounts for that individual difference. Without the complete TTI, the system knows how long the swing itself takes, but not how long the hitter actually consumes from the decision to swing through arrival at the collision point. That limits how precisely the system can prescribe cues or analyze timing for the individual hitter.
Once that complete, hitter-specific TTI has been established at a baseline collision point, it can also serve as the foundation for calculating timing requirements at other locations. Because different pitch locations create different collision depths, TTI can vary by location. The system therefore algorithmically extrapolates from the hitter’s baseline TTI to determine the TTI required to reach other potential collision points.
Prescriptive Timing: Telling the Hitter When to Go
This is the prescriptive side of the system. Because the VR system knows where the ball is going and when it will reach the collision point, and because the hitter’s TTI provides the hitter’s corresponding timeline to that collision point, the system can align the two and calculate the correct moment for the hitter to begin the swing response.
At that calculated moment, the system can issue an instructive timing cue. These cues are called synthetic priors. A synthetic prior is essentially a precisely timed go cue based on the known pitch and the individual hitter’s measured timing. It’s not guessing when the hitter should swing. It’s calculated backward from the future collision point.
Diagnostic Timing: Measuring Early and Late
The same system can also diagnose what happened after the swing. Because it knows the intended collision point and time, it can compare the hitter’s actual timing and bat orientation with the prescribed collision. Instead of simply saying that the hitter was early or late, the system can measure exactly how early or late the hitter was.
The timing error can be shown in three relevant ways: milliseconds of time, inches of collision depth, and degrees of horizontal barrel angle. For example, if the hitter is late, the system can report how many milliseconds late the swing was, how many inches deeper the resulting collision point was from optimal, and how much the horizontal barrel angle changed. An early swing can be measured the same way.
The result is a system that can both instruct and diagnose. The same closed loop therefore tells the hitter when to go and explains exactly what happened if the timing was early or late.