Can machine learning help reveal the competitive advantage of elite beach volleyball players?

Abstract

As the world of competitive sports increasingly embraces data-driven techniques, our research explores the potential of machine learning in distinguishing elite from semi-elite beach volleyball players. This study is motivated by the need to understand the subtle yet crucial differences in player movements that contribute to high-level performance in beach volleyball. Utilizing advanced machine learning techniques, we analyzed specific movement patterns of the motion of the torso during spikes, captured through vest-mounted accelerometers. Our approach offers novel insights into the nuanced dynamics of elite play, revealing that certain movement patterns are distinctly characteristic of higher skill levels. One of our key contributions is the ability to classify spiking movements at different skill levels with an accuracy rate as high as 87%. This current research provides a foundation of what separates elite players from their semi-elite counterparts.