The sports landscape is constantly changing due to innovation and entrepreneurship. The availability of technology led to the emergence of esports and augmented sports. Biofeedback and sensing technologies can be used for athlete monitoring and training purposes. Research on motor control deals with planning and execution of bodily movements and provides some insights towards formal presentation of sports.
Previous research provided many sports categorization models. On many occasions, published articles did not distinguish competitive gameplay activities (gaming) from athletic performance (esports). Our goal was to define esports by extending existing universal sport definitions and propose a novel modular computational framework for categorizing sports through environments and signals.
We have fulfilled our goals by illustrating how signals flow within competitive (sports) environments. Our esports definition introduces esports as a group of sports similar to motorsports. Moreover, we have defined mathematical foundations for signal processing by various actors (athletes, referees, environments, intermediate processing steps). We have demonstrated that representing sports as a multidimensional signal can lead to the categorization of sports through computation. We claim that our approach could be applied to transfer training methods from similar sports, analysis of the training process, and referee error measurement.
Our study was not without limitations. Further research is required to validate our theoretical model by embedding available variables in latent space to calculate similarity measures between sports.