Developing Predictive Athletic Performance Models for Informative Training Regimens

Abstract

Individualized biometric data are being incorporated into training and competitions by many coaches and trainers to provide insights into athletic performance and physical fitness of their athletes. Currently, fitness tracking software provides coaches with minimal descriptive statistics on the collected biometric data, resulting in limited actionable outcomes. The collection of biometric data provides an opportunity to understand the variables that are indicative of athletic performance, and to create predictive models to determine appropriate training and in-game strategies. In order to develop these informative decision support tools, predictive frameworks have to address the correct performance metrics, control of subject-to-subject variability, handle data limitations, and maintain model interpretability. We demonstrate that the strenuousness of training sessions leading up to a competitive match has significant impact on the outcome of the game (win or loss) in continuous-play team sports. Specifically, a high cardiovascular training load two days prior to competition was predictive of a win. Additionally, we show that statistically significant differences exist in the physiological behaviors of different player positions. Analysis of several performance metrics also demonstrates that singular metrics or combinations of simple statistics do not directly relate to the outcome of a game, particularly in low-scoring sports such as field hockey or soccer.