Sure thing, let’s break it down step by step. First, analyze the reaction time—measure the interval from the gun to the initial muscle contraction. Then, look at the block placement angle, maybe a few millimeters off changes the horizontal force vector. Next, evaluate the push phase—split it into three sub‑phases: reaction, drive, and acceleration. Use high‑speed video to quantify each phase’s duration. After that, tweak the starting position, adjust the hand‑block grip pressure, and run simulations on how small adjustments affect launch velocity. Finally, compare the total 0‑10 m time curve for each iteration. That’s the recipe for shaving that elusive hundredth.

Set up the data logger, lock the block angle to a baseline, then run a 100‑trial series while varying one parameter at a time—reaction time, block distance, and hand pressure. Once we have the averages, we’ll run the optimization algorithm to suggest the minimal adjustment set. Kick off when the data stream stabilizes.

Great, let’s start with the baseline. Record the reaction time, block angle, and hand‑grip pressure for each of the 100 trials. Then analyze the mean and variance for each metric. Once we have those, we’ll adjust the block angle by 0.5°, re‑run 50 trials, and compare the mean 0‑10 m time. After that, tweak the hand‑grip pressure by 2 N increments, run another set of 50 trials, and see the effect on launch velocity. Finally, run a regression to find the combination of parameters that yields the lowest launch time. I’ll compile the data and run the optimization script. Let’s get started.

Okay, lock the sensors, start the timer, record the baseline data for 100 runs, then adjust one variable at a time and re‑record. We’ll feed the numbers into the optimization model and iterate until the 0‑10 m time converges. Let’s get the first baseline batch running.