Beyond a Bit Fit
summary
University program collects GPS, heart rate and impact streams from mouthpiece impact sensor, helmet sensors and a forthcoming headband with gyroscope, then enriches them with play-by-play and lineup data to build scalable models in Tresata. Findings include a surprising quarterback harder in practice pattern, fatigue-driven crown impacts late in games, and utility of 13-shot features and lineup efficiencies once sample sizes reach midseason. Outputs are coach-facing alerts and sideline visualizations aimed at actionable, coachable insights and reduced injury risk.
AI-written summary · about
chapters (14)
- 0:00Start
- 2:46STATS
- 7:03math + data = impact
- 7:22transforming teams
- 8:18impact: game prep
- 9:59impact: game time
- 10:46impact: off-season
- 12:58transforming athletes
- 15:51Goal: lay strong ML foundation
- 18:25scalable viz
- 19:36impact by position
- 21:18fatigue → poor form
- 26:39sports analytics keys
- 27:41machine learning to stay fit
related talks
33mUsing Data Science to Better Evaluate American Football Players
42mHow Deep Learning is Revolutionizing Sports Analytics
29mHow the NFL builds computer vision training datasets at scale
41mThe evolution of basketball with data science
33mUsing machine learning to improve player safety in the NFL
31mMachine Learning Driven Injury Prediction
04mInteractive Sports Analytics
17mFrom recovering Pro Golfer to AI Entrepreneur