A data- and knowledge-driven framework for developing machine learning models to predict soccer match outcomes

• Published in: Machine learning Vol. 113; no. 10; pp. 8165 - 8204
• Main Authors: Berrar, Daniel, Lopes, Philippe, Dubitzky, Werner
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Computer Science; Control; Machine Learning; Mechatronics; Modelling; Natural Language Processing (NLP); Neural networks; Performance prediction; Prediction models; Robotics; Simulation and Modeling; Soccer; Special Issue on Machine Learning for Soccer; Task complexity; NN; Super league; Naive Bayes; Neural networks; Ordinal forests; Outcome prediction; Soccer analytics; 2023 soccer prediction challenge
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-024-06625-9

The 2023 Soccer Prediction Challenge invited the machine learning community to develop innovative methods to predict the outcomes of 736 future soccer matches. The Challenge included two tasks. Task 1 was to forecast the exact match score , i.e., the number of goals scored by each team. Task 2 was to predict the match outcome as probability vector over the three possible result categories: victory of the home team, draw, and victory of the away team. Here, we present a new data- and knowledge-driven framework for building machine learning models from readily available data to predict soccer match outcomes. A key component of this framework is an innovative approach to modeling interdependent time series data of competing entities. Using this framework, we developed various predictive models based on k -nearest neighbors, artificial neural networks, naive Bayes, and ordinal forests, which we applied to the two tasks of the 2023 Soccer Prediction Challenge. Among all submissions to the Challenge, our machine learning models based on k -nearest neighbors and neural networks achieved top performances. Our main insights from the Challenge are that relatively simple learning algorithms perform remarkably well compared to more complex algorithms, and that the key to successful predictions lies in how well soccer domain knowledge can be incorporated in the modeling process.