User Bias and Algorithmic Accuracy in Football Outcome Prediction: Evidence from the English Premier League

Ghasemi, Fateme; Khadivar, Ameneh; Moslehi, Leila; Abbasi, Fatemeh

doi:10.22044/jadm.2025.16736.2805

Document Type : Original/Review Paper

Authors

¹ Department of Management, Faculty of Social Sciences and Economics,Alzahra University,Iran.

² Department of Sport Management, Faculty of Sport Sciences, Alzahra University, Tehran, Iran.

³ Department of Industrial Management and Information Technology, Shahid Beheshti University, Tehrani, Iran.

10.22044/jadm.2025.16736.2805

Abstract

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.

Keywords

Main Subjects

H.3.2.3. Decision support

References

[1]‎ I. H. Sarker. “Machine Learning: Algorithms, Real-World Applications and Research Directions”, SN Computer Science., vol 2, no. 3, pp. 160, March 2021.

[2]‎ R. Bunker, T. Susnjak. “The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review”, The Journal of Artificial Intelligence Research, vol. 73. Apr 2022.

[3]‎ R. Pugliese, S. Regondi, R. Marini. “Machine learning-based approach: global trends, research directions, and regulatory standpoints”. Data Science and Management, vol. 4, pp. 19-29. December 2021.

[4]‎ X. Tan. “Enhanced Sports Predictions: A Comprehensive Analysis of the Role and Performance of Predictive Analytics in the Sports Sector”. Wireless Personal Communications, vol. 132, no. 3, pp. 1613-1636. September 2023.

[5]‎ S. M. Arabzad, M. E. Tayebi Araghi, S. Sadi-Nezhad, N. Ghofrani. “Football Match Results Prediction Using Artificial Neural Networks; The Case of Iran Pro League”, Journal of Applied Research on Industrial Engineering, vol. 1, no. 3, pp.159-179. Summer 2014.

[6]‎ C. S. Bojer. “Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities”. International Journal of Forecasting, vol. 38, no. 4, pp. 1555-1561. October–December 2022.

[7] D. Cica, B. Sredanovic, S. Tesic, D. Kramar. “Predictive modeling of turning operations under different cooling/lubricating conditions for sustainable manufacturing with machine learning techniques”, Applied Computing and Informatics, vol. 20, no. 1/2, pp. 162-180. 2024.

[8] F. Thabtah, D. Peebles. “A new machine learning model based on induction of rules for autism detection”, Health Informatics, vol. 26, no. 1, pp. 264-286. 2020.

[9] F. Rodrigues, Â. Pinto. “Prediction of football match results with Machine Learning”. Procedia Computer Science, vol. 204, pp. 463-470. 2022.

[10] B. Min, J. Kim, C. Choe, H. Eom, R. I. McKay. “A compound framework for sports results prediction: A football case study”. Knowledge-Based Systems, vol. 21, no. 7, pp. 551-562. October 2008.

[11] R. Ievoli, L. Palazzo, G. Ragozini. “On the use of passing network indicators to predict football outcomes”. Knowledge-Based Systems, vol. 222, 106997. June 2021.

[12] I. B. da Costa, L. B. Marinho, C. E. S. Pires. “Forecasting football results and exploiting betting markets: The case of “both teams to score”. International Journal of Forecasting, vol. 38, no. 3, pp. 895-909. July–September 2022.

[13] W. Gu, K. Foster, J. Shang, L. Wei. “A game-predicting expert system using big data and machine learning”. Expert Systems with Applications, vol. 130, pp. 293-305. September 2019

[14] R. Baboota, H. Kaur. “Predictive analysis and modelling football results using machine learning approach for English Premier League”. International Journal of Forecasting, vol. 35, no. 2, pp. 741-755. April–June 2019.

[15] R. G. Martins, A. S. Martins, L. A. Neves, L. V. Lima, E. L. Flores, M. Z. do Nascimento. “Exploring polynomial classifier to predict match results in football championships”. Expert Systems with Applications, vol. 83, pp. 79-93. October 2017.

[16] A. Ati, P. Bouchet, R. Ben Jeddou. “Using multi-criteria decision-making and machine learning for football player selection and performance prediction: a systematic review”. Data Science and Management, vol. 7, no. 2, pp. 79-88. June 2024.

[17] S. J. Koopman, R. Lit. “Forecasting football match results in national league competitions using score-driven time series models”. International Journal of Forecasting, vol. 35, no.2, pp. 797-809. April–June 2019.

[18] Q. Zhang, X. Zhang, H. Hu, C. Li, Y. Lin, R. Ma. “Sports match prediction model for training and exercise using attention-based LSTM network”, Digital Communications and Networks, vol. 8, no. 4, pp. 508-515. August 2022.

[19] M. Herold, F. Goes, S. Nopp, P. Bauer, C. Thompson, T. Meyer. “Machine learning in men’s professional football: Current applications and future directions for improving attacking play”. International Journal of Sports Science & Coaching, vol. 14, no. 6, pp. 798-817. 2019.

[20] M. Settembre, M. Buchheit, K. Hader, R. Hamill, A. Tarascon, R. Verheijen, D. McHugh. “Factors associated with match outcomes in elite European football – insights from machine learning models”. Journal of Sports Analytics, vol. 10, pp. 1-16. 2024.

[21] C. K. Leung, K. W. Joseph. “Sports Data Mining: Predicting Results for the College Football Games”. Procedia Computer Science, vol. 35, pp. 710-719. 2014.

[22] Y. Men. “Intelligent sports prediction analysis system based on improved Gaussian fuzzy algorithm”. Alexandria Engineering Journal, vol. 61, no. 7, pp. 5351-5359. July 2022.

[23] J. Knoll, J. Stübinger. “Machine-Learning-Based Statistical Arbitrage Football Betting”. KI - Künstliche Intelligenz, vol. 34, no. 1, pp. 69-80. March 2020.

[24] P. Pugsee, P. Pattawong. “Football Match Result Prediction Using the Random Forest Classifier”. Paper presented at the Proceedings of the 2nd International Conference on Big Data Technologies, Jinan, China. August 2019.

[25] O. B. Sezer, M. U. Gudelek, A. M. Ozbayoglu. “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019”. Applied Soft Computing, vol. 90, 106181. May 2020.

[26] C. Schröer, F. Kruse, J. M. Gómez. “A Systematic Literature Review on Applying CRISP-DM Process Model”. Procedia Computer Science, vol. 181, pp. 526-534. 2021.

[27] A. Khadivar, F. Abbasi, S. A. Mohseni, R. Basraei. “Utilizing Deep Learning for Aspect-Based Sentiment Analysis in Restaurant Reviews”. Journal of Information Technology Management, vol. 17. no. 2, pp. 50-68. 2025.

[28] S. Abdian, M. Hosseinzadeh Shahri, A. Khadivar. “A Bibliometric Analysis of Research on Big Data and Its Potential to Value Creation and Capture”. Interdisciplinary Journal of Management Studies, vol. 16, no. 1, pp. 1-24. January 2023.

[29] A. Khadivar, N. Hadianfar, A. Aliyari Boroujeni. “Predicting Mobile Marketing Acceptance Factors: A Hybrid Approach of Structural Equation Modeling and Artificial Neural Network (SEM-ANN)”. Journal of Business Management, vol. 17, no. 3, pp. 732-769. 2025.

[30] D. Reed, P. O’Donoghue. “Development and application of computer-based prediction methods”. International Journal of Performance Analysis in Sport, vol. 5, no. 3, pp. 12-28. 2005.

[31] A. Joseph, N. E. Fenton, M. Neil. “Predicting football results using Bayesian nets and other machine learning techniques”. Knowledge-Based Systems, vol. 19, no. 7, pp. 544-553. November 2006.

[32] A. McCabe, J. Trevathan. “Artificial Intelligence in Sports Prediction”. Fifth International Conference on Information Technology: New Generations, Las Vegas. April 2008

[33] K. Odachowski, J. Grekow. “Using Bookmaker Odds to Predict the Final Result of Football Matches”. In: M. Graña, C. Toro, R.J. Howlett, L.C. Jain (eds). Knowledge Engineering, Machine Learning and Lattice Computing with Applications. KES 2012. Lecture Notes in Computer Science, vol, 7828. Springer, Berlin, Heidelberg. 2013.

[34] N. Danisik, P. Lacko, M. Farkas. “Football Match Prediction Using Players Attributes”. World Symposium on Digital Intelligence for Systems and Machines (DISA). Slovakia. August 2018.

[35] D. Berrar, P. Lopes, W. Dubitzky. “Incorporating domain knowledge in machine learning for soccer outcome prediction”. Machine Learning, vol. 108, no. 1, pp. 97-126. January 2019.

[36] W. Yao, Y. Wang, M. Zhu, Y. Cao, D. Zeng. “Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer”. Entropy, vol. 24, no. 7. July 2022.

[37] D. Rudrapal, S. Boro, J. Srivastava, S. Singh. “A Deep Learning Approach to Predict Football Match Result”. In: H. Behera, J. Nayak, B. Naik, D. Pelusi, (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. 2020.

[38] P. K. Jain, W. Quamer, R. Pamula. "Sports result prediction using data mining techniques in comparison with base line model". OPSEARCH, vol. 58, no, 1, pp. 54-70. March 2021.

Journal of AI and Data Mining

User Bias and Algorithmic Accuracy in Football Outcome Prediction: Evidence from the English Premier League

References

References

Volume 14, Issue 1
January 2026
Pages 71-81

User Bias and Algorithmic Accuracy in Football Outcome Prediction: Evidence from the English Premier League

References

References

Volume 14, Issue 1January 2026Pages 71-81

Volume 14, Issue 1
January 2026
Pages 71-81