Research Article

LAX-Score

Published: 2021-9-9

Journal: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

DOI: 10.1145/3478076

Abstract

For the past several decades, machine learning has played an important role in sports science with regard to player performance and result prediction. However, it is still challenging to quantify team-level game performance because there is no strong ground truth. Thus, a team cannot receive feedback in a standardized way. The aim of this study was twofold. First, we designed a metric called LAX-Score to quantify a collegiate lacrosse team's athletic performance. Next, we explored the relationship between our proposed metric and practice sensing features for performance enhancement. To derive the metric, we utilized feature selection and weighted regression. Then, the proposed metric was statistically validated on over 700 games from the last three seasons of NCAA Division I women's lacrosse. We also explored our biometric sensing dataset obtained from a collegiate team's athletes over the course of a season. We then identified the practice features that are most correlated with high-performance games. Our results indicate that LAX-Score provides insight into athletic performance beyond wins and losses. Moreover, though COVID-19 has stalled implementation, the collegiate team studied applied our feature outcomes to their practices, and the initial results look promising with regard to better performance.

Faculty Members

  • Shuangquan Wang - Computer Science, Salisbury University, Salisbury, Maryland
  • Minglong Sun - Computer Science, William & Mary
  • Ken Koltermann - Computer Science, William & Mary
  • Erik Korem - CEO, AIM7 Inc. Houston, Texas
  • Gang Zhou - Computer Science, William & Mary
  • Zhenming Liu - Computer Science, William & Mary
  • Woosub Jung - Computer Science, William & Mary, Williamsburg, Virginia
  • Scott Kuehn - Strength & Conditioning, University of Arizona, Tucson, Arizona
  • Amanda Watson - PRECISE Center, University of Pennsylvania, Philadelphia, Pennsylvania

Themes

  • Impact of COVID-19 on sports practices
  • Team performance measurement
  • Relationship between practice features and performance
  • Machine learning in sports science
  • Quantification of athletic performance

Categories

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