Pressing is a key tactic in football for disrupting opponents’ strategies and creating advantageous opportunities. However, existing studies often rely on static, rule-based definitions, overlooking the dynamic evolution of pressing sequences and each player’s contribution. To address these limitations, this paper introduces exPressV2, a novel framework that uses a physics-informed ‘Pressing Intensity’ metric to dynamically identify high-pressure events from continuous tracking data. Our spatio-temporal architecture, combining a Gated Recurrent Unit (GRU) and a Graph Attention Network (GAT), then analyzes the sequence leading up to a press to predict the probability of regaining possession. The proposed framework was validated using 7,800 pressing situations from 36 K League 1 matches and demonstrated superior performance to baseline models with an ROC AUC of 0.731. This framework offers practical applications that translate the model’s predictive power into objective tools for both individual player evaluation and tactical simulation. The source code is publicly available at https://github.com/leemingo/express-v2 .