Abstract

This research compares gradient boosting methods and neural network architectures for predicting football action sequences. Using detailed event annotations and spatial-temporal positional data, we evaluate the models’ ability to forecast goal-scoring opportunities several actions in advance. Through feature engineering and ensemble strategies, our results reveal key contextual and spatial factors that influence goal probabilities. Ensemble models combining CatBoost, LightGBM, and XGBoost outperform individual models, achieving an F1 Score of 0.707 and PR AUC of 0.734. These findings can provide valuable insights for real-time match analysis and player evaluation.

Recommended Citation

Zaręba, M., Piłka, T. & Górecki, T. (2025). Gradient or Not? Predicting Football Action Sequences Using Boosting vs Neural NetworksIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.57

Paper Type

Short Paper

DOI

10.62036/ISD.2025.57

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Gradient or Not? Predicting Football Action Sequences Using Boosting vs Neural Networks

This research compares gradient boosting methods and neural network architectures for predicting football action sequences. Using detailed event annotations and spatial-temporal positional data, we evaluate the models’ ability to forecast goal-scoring opportunities several actions in advance. Through feature engineering and ensemble strategies, our results reveal key contextual and spatial factors that influence goal probabilities. Ensemble models combining CatBoost, LightGBM, and XGBoost outperform individual models, achieving an F1 Score of 0.707 and PR AUC of 0.734. These findings can provide valuable insights for real-time match analysis and player evaluation.