Objectives: The planning and control of team sport training activities is an extremely important aspect of athletic development and team performance. This research introduces a novel system which leverages techniques from the fields of control systems theory and artificial intelligence (AI) to construct optimal future training plans when unexpected disturbances and deviations from a training plan goal occur.
Design: Simulation-based experimental design
Methods: The adaptation of training load prescriptions was formulated as an optimal control problem where we seek to minimize the difference between a desired training plan goal and an observed training outcome. To determine the most suitable approach to optimise future training loads the performance of an AI based feedback controller was compared to random and proportional controllers. Robust computational simulations (N=1800) were conducted using a non-linear training plan spanning 60 days over a 12-week period, the control strategies were assessed on their ability to adapt future training loads when disturbances and deviations from an optimal planning policy have occurred. Statistical analysis was conducted to determine if significant difference existed between the three control strategies.
Results: The results of a repeated-measures analysis of variance demonstrated that an intelligent feedback controller significantly outperforms the random (p <.001, ES = 7.41, very large) and proportional control (p <.001, ES = 7.41, very large) strategies at reducing the deviations from a training plan goal.
Conclusions: This system can be used to support the decision making of practitioners across several areas considered important for the effective planning and adaption of athletic training.