NFL Play Classification Using GNN and Player Tracking Data - Souza, Carlos E. G. R.; Luz, Eduardo J. S.; Freitas, Vander L. S. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
This study addresses the classification of offensive plays in the National Football League (NFL) as either pass or rush, utilizing official NFL datasets from 2022-2023 season. The central hypothesis posits that the relative spatial positioning of players on the field, along with their interactions, directly influences the offensive strategy. To investigate this, we generate graph-based representations connecting players, which serve as input for a Graph Convolutional Network (GCN) for the subsequent classification of these graph structures. The empirical results demonstrate that the GCN-based framework outperforms conventional machine learning architectures, namely Random Forest and Multilayer Perceptron.