Grouped Feature Representation and Gated Multilayer Perceptron for Event-Level Football Pass Outcome Prediction

Accurate prediction of football pass outcomes is important for tactical analysis, decision evaluation, and skill-oriented feedback in student football training and physical education. However, event-level pass outcome prediction remains challenging because pass success is jointly influenced by spatial context, defensive pressure, receiver-related cues, and historical coordination between players. To address this issue, this study proposes an information-guided multilayer perceptron (IGMLP) based on grouped feature representation and gated feature fusion using structured event data. In the proposed framework, input variables are organized into interpretable semantic feature groups, including contextual features, pressure-aware features, historical coordination features, and receiver-related features. These groups are encoded through separate branches and adaptively fused by a group-level gating mechanism for nonlinear pass outcome modeling. Unlike conventional gated neural architectures that usually apply generic gates to hidden units, channels, or sequential states, the proposed gated design operates at the semantic feature-group level and adaptively weights football-specific information sources according to their relevance to each pass event. Using the StatsBomb open-event dataset, both prediction and recognition paths were constructed, and the proposed model was compared with standard multilayer perceptron (MLP), residual neural network (ResNet), boosting tree (BT), convolutional neural network (CNN), and long short-term memory network (LSTM). In the prediction path, IGMLP achieved an Accuracy of 0.9184, Precision of 0.9295, Recall of 0.9837, F1-score of 0.9558, and AUC of 0.9325. In the recognition path, IGMLP achieved an Accuracy of 0.9808, Precision of 0.9882, Recall of 0.9902, F1-score of 0.9893, and AUC of 0.9925. These results indicate that semantic feature grouping and gated feature fusion are effective for event-level football pass outcome prediction.