Nice concept—let's crunch the numbers. First, decide the latency you can tolerate, say 15 ms. That’s 0.015 s. If your game ticks every 33 ms, you’re already in the ballpark, but to anticipate you need to predict the next tick before it happens. So calculate the prediction window: 0.015 s / 0.033 s ≈ 0.45 ticks. In practice you want a full tick ahead, so push your sensor to 10 ms. That gives 0.010/0.033 ≈ 0.30 ticks, still too low, so target 5 ms. 5 ms gives 0.15 ticks, but with a fast reaction buffer you can handle it. Next, use a Kalman filter to smooth player movement; the state vector should include position, velocity, and acceleration. Update the filter at the sensor’s sample rate, say 1 kHz, then output the predicted position at the game tick. Keep the matrix size small—just a 2‑dimensional vector—to avoid latency. Test with a moving target and tweak the process noise covariance until the error stays under 1 pixel. If you want a riskier edge, drop the acceleration term and accept the jitter; but that’s a gamble. Start with the filter, measure the error, iterate. You’ll have a device that’s almost as sharp as my reflexes. Good luck!