Document Type: Review Paper


1 Department of Computer Engineering, Islamic Azad University of Lahijan, Lahijan, Iran

2 Department of Computer engineering, Faculty of Computer, Islamic Azad university of Lahijan, Lahijan, Iran


In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent Identification and game strategy evaluation. Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty: the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, coaching strategy, etc. So even for experts, it is very hard to predict the exact results of individual matches. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system.


Main Subjects

1. Ian H. Witten and Eibe Frank, Data Mining, Practical Machine Learning Tools and Techniques, Second Edition, Elsevier, (2005).
2. R. Sapsford and V. Jupp, Data collection and analysis, Second edition, Sage Publications, (2006).
3. Andreas C. Müller and Sarah Guido, Introduction to Machine Learning, O’Reilly Media, (2017).
4. Da Ruan, Computational Intelligence in Complex Decision Systems, Atlantis Press, (2010).
5. Janusz Kacprzyk, Soft Computing in Artificial Intelligence, Polish Academy of Sciences, Springer, (2014).
6. Ch.M. Grinstead and J. L. Snell, Introduction to Probability, University of New Mexico, (2003).
7. Kevin P. Murphy, Machine Learning a Probabilistic Perspective, The MIT Press Cambridge, London, England, (2017).
8. C. Burges and B. Sholkopf, Improving the accuracy and speed of support vector machines, MIT Press, Neural Information Processing Systems, Volume 9, Cambridge, (2017).
9. Sadeghian, A., Mendel, J. and Tahayori, H., "Advances in type-2 fuzzy sets and systems: Theory and applications, Springer, Vol. 301, (2013).
10. Safari, R. Hosseini, M. Mazinani, A Novel Type-2 Adaptive Neuro Fuzzy Inference System Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster, International Journal of Engineering (IJE), TRANSACTIONS B: Applications Vol. 30, No. 11, (November 2017) 1746-1751.
11. M. Tilp, N. Schrapf, Analysis of tactical defensive behavior in team handball by means of artificial neural networks, IFAC-Papers On-Line 48-1 (2015).
12. A.Maszczyka, A.Gołaśa, , A.Stanulaa , P.Pietraszewskia, R.Rocznioka and A.Zająca, Application of Neural and Regression Models in Sports Results Prediction, Elsevier, Social and Behavioral Sciences 117 (2014).
13. Carson K. Leung, Kyle W. Joseph, Sports data mining: predicting results for the college football games, 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Procedia Computer Science 35 (2014) 710 – 719.
14. R. P. Bunker and F.Thabtah, A machine learning framework for sport result prediction, Applied Computing and Informatics, Vol.2, (2017) 252-259.
15. J. Patterson and A. Gibson, Deep Learning: A Practitioner's Approach 1st Edition, (2015).
16. R.Igiri, C. Peace, A.Nwachukwu and E.Okechukwu, IOSR journal of Engineering, Vol.4, Issue 12, (2014) 12-20.
17. H. Apolo, Predicting, Predicting the Outcome of a Chess Game by Statistical and Machine Learning techniques, Universitat Politecnica de Catalunya, Spain, Barcelona, (2016).
18. Zheyuan Fan, Yuming Kuang, Xiaolin Lin, Chess Game Result Prediction System Stanford University, (2013).
19. Byungho Min, Jinhyuck Kim, Chongyoun Choe and Robert Ian, "A compound framework for sports prediction: The case study of football", Knowledge-Based Systems, Vol. 21, No. 7, (2008) 551-562.
20. Farzin Owramipur, Parinaz Eskandarian, and Faezeh Sadat Mozneb, Football Result Prediction with Bayesian Network in Spanish League-Barcelona Team, International Journal of Computer Theory and Engineering, Vol. 5, No. 5, (2013).
21. Gianluca Baio and Marta Blangiardo, Bayesian Hierarchical Modelling for the Prediction of Football Results, Seminari del Dipartimento di Statistica, (2009).
22. Igiri, Chinwe Peace, Nwachukwu, Enoch Okechukwu, an Improved Prediction System for Football a Match Result, IOSR Journal of Engineering (IOSRJEN), Vol.4, Pages 12-20, (2014).
23. Paolo Giuliodori, An Artificial Neural Network-based Prediction model for underdog Teams in NBA Matches, University of Camerino, School of Science and Technology, (2017).
24. Frank Peschier, Predicting Domestic Football Matches Using Crowd Estimated Market Values, Erasmus University of Economics, (2015).
25. M. Bevc ,the Outcome of Football Matches From Point-by-Point Data, University of Glascow, Master Thesis, (2015).
26. C. Constantinaou, N. E. Fenton and M. Neil, "A Bayesian network model for forecasting Association Football match outcomes", Working Papers, Queen Mary University, (2012).
27. Kevin P. Murphy, Machine Learning a Probabilistic Perspective, The MIT Press Cambridge, London, England, (2017).
28. P. Rotshtein, M. Posner, and A. B. Rakityanskaya, football predictions based on a fuzzy model with genetic and neural tuning, Cybernetics and Systems Analysis, Vol. 41, No. 4, (2015).
29. Vashisht Madhavan, Predicting NBA Game Outcomes with Hidden Markov Models, Berkeley University, (2016).
30. Marcelo S. Vaz, Yuri S. Ribeiro, Eraldo S. Pinheiro, Fabrício B. Del Vecchio, ARTICLE Psychophysiological profile and prediction equationsfor technical performance of football players, Revista Brasileira de, (2018).
31. Alberto Tavares, Predicting Results of Brazilian Soccer League Matches, University of Wisconsin-Madison, (2018).
32. Da Ruan, Computational Intelligence in Complex Decision Systems, Atlantis Press, (2010).
33. Janusz Kacprzyk, Soft Computing in Artificial Intelligence, Polish Academy of Sciences, Springer, (2014).
34. J. Sindik and N. Vidal, Uncertainty coeffecient as a method for optimization of the competition systems in various sports, Sport Science, Vol 2, No. 1, (2009). 95-100.
35. Mitrache Georgetaa, Predoiu Radua, Coli Eugenb and Coli Danielac, A-state, A-trait and the performance of 14-15 years old football players, Social and Behavioral Sciences 127 (2014) 321 – 325.
36. Y. Y. Petrunin, Analysis of the football performance: from classical methods to neural network, Journal of Journal of Human Activity Theory, Vol 2, (2011).
37. Glickman, M.E. and Stern, H.S, A state-space model for national football league scores, Journal of the American Statistical Association, (2017).