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Related Experiment Videos

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

Yijuan Yuan1, Shaosong Wang2, Yonghong Deng3

  • 1Department of Physical Education, Liaocheng University Dongchang College, Liaocheng 252000, China.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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This summary is machine-generated.

This study introduces an information-guided multilayer perceptron (IGMLP) for predicting football pass outcomes. The model effectively uses grouped features and adaptive fusion, significantly improving prediction accuracy in football analytics.

Area of Science:

  • Sports Analytics
  • Machine Learning in Sports
  • Football Performance Analysis

Background:

  • Accurate football pass outcome prediction is crucial for tactical analysis and player development.
  • Event-level prediction is challenging due to multiple influencing factors like spatial context and player coordination.
  • Existing models struggle to integrate diverse influencing factors effectively.

Purpose of the Study:

  • To develop an advanced model for accurate event-level football pass outcome prediction.
  • To propose an Information-Guided Multilayer Perceptron (IGMLP) framework.
  • To enhance football analytics through improved pass prediction.

Main Methods:

  • Organized input variables into semantic feature groups: contextual, pressure-aware, historical coordination, and receiver-related.
Keywords:
event-level modelingfootball pass outcome predictiongrouped feature representationphysical educationstudent football

Related Experiment Videos

  • Employed separate encoding branches for each feature group.
  • Utilized a group-level gating mechanism for adaptive feature fusion and nonlinear modeling.
  • Compared IGMLP against MLP, ResNet, BT, CNN, and LSTM using the StatsBomb open-event dataset.

    • Main Results:

    • IGMLP achieved high performance in the prediction path with Accuracy (0.9184), Precision (0.9295), Recall (0.9837), F1-score (0.9558), and AUC (0.9325).
    • In the recognition path, IGMLP demonstrated superior results with Accuracy (0.9808), Precision (0.9882), Recall (0.9902), F1-score (0.9893), and AUC (0.9925).
    • Semantic feature grouping and gated feature fusion proved effective for event-level pass outcome prediction.

    Conclusions:

    • The proposed IGMLP model significantly enhances the accuracy of football pass outcome prediction.
    • The novel approach of semantic feature grouping and gated fusion is effective for complex event-level sports data.
    • This method offers a valuable tool for football tactical analysis, decision evaluation, and skill feedback.