Interesting idea. You can gather data on punch speed, angle, and opponent reaction, then use probability models to estimate the most likely moment a knockout will land. By treating each round as a data set, you could apply Bayesian inference to refine timing and improve efficiency over traditional guesswork.

Here’s a minimal sheet structure you can copy into Excel or Google Sheets: Columns: 1. Round 2. Time (seconds) 3. Punch Type (jab, hook, cross, uppercut) 4. Speed (mph) 5. Angle (degrees) 6. Opponent Reaction (blocked, dodged, hit) 7. Outcome (score, KO, TKO, no effect) Example row: Round 1, 3.2, Hook, 18.5, 30, Hit, KO Collect these for every round, then calculate the probability of a KO given each punch type, speed, and angle. Use a simple frequency count or a Bayesian formula to update the odds after each fight. The key is to keep the data set clean and remove any extraneous columns that don’t contribute to predicting a knockout.