User Bias and Algorithmic Accuracy in Football Outcome Prediction: Evidence from the English Premier League
نویسندگان
1 Department of Industrial Management and Information Technology, Shahid Beheshti University, Tehrani, Iran.
2 Department of Management, Faculty of Social Sciences and Economics,Alzahra University,Iran.
3 Department of Management, Faculty of Social Sciences and Economics,Alzahra University,Iran.
4 Department of Sport Management, Faculty of Sport Sciences, Alzahra University, Tehran, Iran.
doi
10.22044/jadm.2025.16736.2805چکیده
This study investigates the effectiveness of machine learning algorithms, including Neural Networks, Bayesian Networks, Support Vector Machines, and Random Forests, in predicting football match outcomes using data from the English Premier League (2018–2022).By incorporating user-generated probabilities for home win, away win, and draw alongside conventional features, the models were evaluated under binary and multi-class classification scenarios. The Support Vector Machine achieved the highest accuracy (69%) in the win-loss scenario, while the Neural Network reached 51% in the win-draw-loss scenario. Results indicate that user-derived features enhance predictive performance, though user predictions show a bias toward home teams, especially in uncertain cases. These findings highlight the potential of integrating user perspectives into predictive modeling and underscore the importance of addressing cognitive bias in sports analytics.
