From “Can Feed” to “Can Train”
In March, Mavio completed the EVT-3 prototype build.
This stage moved the focus from “can the machine feed a ball?” to “can the machine complete a real training sequence?”
The value of a ball machine is not just feeding one ball. It is about delivering a repeatable rhythm that supports footwork, reaction timing, contact timing, and consistent stroke practice.
This Month’s Focus
EVT-3 was designed to address the issues found during EVT-2.
The focus was on feeding structure, continuous rhythm, feed count accuracy, landing consistency, and clearer machine response during training.
Key Progress
EVT-3 prototype build completed The new prototype entered structural validation with a focus on issues found during EVT-2.
Feeding structure was revalidated The ball tray, ball frame, baffle, and track structure were tested again to improve ball entry stability.
Continuous training rhythm became more consistent Feed interval consistency improved, reducing sudden rhythm changes during continuous drills.
Landing consistency improved under the same command
We tested whether the same training command could produce more consistent ball placement across repeated feeds. This helped us evaluate not only whether Mavio could feed continuously, but whether it could deliver a repeatable training experience.
Feed count accuracy improved The difference between the set number of feeds and the actual number of feeds was reduced.
App training states became clearer Continuous training, pause, resume, stop, and mode-switching states were validated together, making the training process easier to follow.
What We Learned
Real training is not just pressing one button.
A session may involve pausing, resuming, changing modes, adjusting rhythm, or stopping immediately. Mavio needs to do more than execute commands. It needs to stay consistent throughout the training flow and provide clear feedback after each action.
What’s Next
The next stage will place EVT-3 under higher-frequency continuous feeding tests to validate feeding stability, rhythm consistency, and error handling under more realistic training pressure.
