LAX-Score: Quantifying Team Performance in Lacrosse and Exploring IMU Features towards ...
summary
A reproducible team-performance metric called LAX-Score = normalized weighted sum of game features maps linearly to winning percentage, fits a Gaussian (a score of 70 = top 15% over three seasons), and is validated on hundreds of collegiate games. Practice data from GPS, heart rate, and IMU-derived features feed regressions where low-load and taper models show the highest adjusted R²; one feature, A17 accel/decel count, negatively correlates with high LAX-Scores in low-load sessions. Applied guidelines coincide with reduced injury risk and improved game-performance indicators.
AI-written summary · about
chapters (8)
- 0:00Introduction
- 0:20Problem Statement
- 1:34Methodology
- 2:04Benefits
- 2:32Examples
- 3:32Second Part
- 4:58Collaboration Analysis
- 5:35Feedback
related talks
55mThe ABCs of Scoring: Why, How, and Getting a Good Grade
33mUsing Data Science to Better Evaluate American Football Players
19mFootball Analytics with the Soccermatics Package
24mBuilding Aggregated Solutions to Scale Your Geospatial Strategies
01h 10mPrincipled Performance Analytics
21mExamining the relationship between brain activity and video game performance
15mMachine Learning: 5 Additional Metrics to Avoid High‑Performing but Useless Models
16mContent Scoring