Player Movement

Analyzing player movement in sports is crucial for performance enhancement and strategic development, focusing on extracting meaningful patterns from various data sources like video and text commentary. Current research employs diverse approaches, including machine learning algorithms (e.g., multi-layered perceptrons, pattern mining algorithms like LCCspm and LCS) and graph-based models to track player trajectories, predict future movements, and identify strengths and weaknesses. These analyses provide valuable insights for coaches and athletes, enabling data-driven improvements in training, strategy, and injury prevention across various sports.