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1. Min, B., Choe, C., and McKay, R. I. (2006) “A compound approach for football result prediction”,

Working Papers, Seoul National University.

http://sc.snu.ac.kr/PAPERS/aj06_FRES_final.pdf

 

2.  Min, B., Kim, J., Choe, C. and McKay, R. I. (2007) “A compound framework for sport prediction: the case of football”,

Working Papers, Seoul National University.

http://sc.snu.ac.kr/PAPERS/bhmin07_KBS.pdf.

 

3. Min, B., Kim, J., Choe, C., Eom, H. and McKay, R. I.  (2008) “A compound framework for sports results prediction: a football case study”, Knowledge-Based Systems, Vol.21, No.7, pp.551-562.

DOI:10.1016/j.knosys.2008.03.016 

 

4. Rustemoglu, S. (2009) “Futbol sektorunde bir karar destek modeli uygulamasi”

MSc Thesis in Industrial Engineering, Graduate School of Science, Istanbul Technical University.

 https://polen.itu.edu.tr/handle/11527/3184

 

5. Gozutok, H, and  Geberli, U. (2011) “Futbol emek piyasalarinda ucretlendirme ve rekabetci denge”,

Conference Paper presented at International Congress on Sports Economics and Management, Conference Proceedings, pp.269-286,

12-15 October 2011, Ege University, Izmir.

http://sem.ege.edu.tr/

 

6. Petrunin, Y.Y. (2011) “Analysis of the football performance: from classical methods to neural network”,

Conference Paper, presented at Electronic Culture: Intellectual Innovative Technologies in the Socio-Cultural Sphere, Russian Interdisciplinary Conference, Moscow State University, 26 October 2011, Russia.

 http://www.ec-ai.ru/elib/15.pdf

 

7. Constantinou, A.C., Fenton, N.E., Neil, M. (2012) “A Bayesian network model for forecasting Association Football match outcomes”,

Working Papers, Queen Mary University, London.

http://constantinou.info/downloads/papers/pi-model11.pdf

 

8. Constantinou, A.C., Fenton, N.E. (2012) “Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries”,

Working Papers, Queen Mary University, London.

http://www.constantinou.info/downloads/papers/pi-ratings.pdf

 

9. Petrunin, Y. Y. (2012) “Performance management in football”,

Governance Electronic Bulletin, No.35, pp.1-23.

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10. Constantinou, A.C., Fenton, N.E., Neil, M. (2012) “pi-football: A Bayesian network model for forecasting Association Football match outcomes”,

Knowledge-Based Systems, Vol.36, pp.322-339.

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11. Constantinou, A.C. (2012) “Bayesian networks for prediction, risk assessment and decision making in an inefficient association football gambling market”,

PhD Thesis in Risk and Information Management, Queen Mary University, London.

http://constantinou.info/downloads/papers/Constantinou-Ph.D.pdf

 

12. Constantinou, A.C.,  N.E., Fentom. (2013) “Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries”,

Journal of Quantitative Analysis in Sports, Vol.9, No.1.

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13. Owramipur, F., Eskandarian, P., Mozneb, F.S. (2013) “Football prediction with Bayesian network in Spanish league-Barcelona team”,

International Journal of Computer Theory and Engineering, Vol.5, No.5, pp.812-815.

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14. Petrunin, Y. Y. (2013) “Footballmetrics:  a new scientific academic direction”,

Higher Education in Russia, No:10, pp.97-103.

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15. Arabzad, S.M., et al. (2014) “Football match results prediction using artificial neural networks: the case of Iran pro league”,

Applied Research on Industrial Engineering, Vol.1, No.3, pp.159-179.

http://grandjournals.com/NVL/Gallery/contents/1132afee-6640-4c9d-9b90-a3c55efc40a3.pdf

 

16. Flores, J.S. (2014) “Analysis of development the football game from the optical complex systems”,

PhD Thesis in Teacher Training, University of Las Palmas, Spain.

http://acceda.ulpgc.es/bitstream/10553/12204/4/0701337_00000_0000.pdf

 

17. Almedia, S.S. (2017) “Performance assessment and prediction of football players: tailoring an architecture with spatiotemporal positional and physiological futures”,

MSc Thesis in Biomedical Engineering, Faculty of Engineering of the University of the Porto, Portugal.

https://repositorio-aberto.up.pt/bitstream/10216/107499/2/214087.pdf

 

18. Cho, et al. (2018) “Using social network analysis and gradient boosting to develop a soccer win-lose prediction model”

Engineering Applications of Artificial Intelligence, Vol.72, pp.228-240.

 https://www.sciencedirect.com/science/article/pii/S0952197618300897

 

19. Escobar, et al. (2018) “Bayesian based approach learning for outcome prediction of soccer matches”

Computational Science-ICCS 2018, pp.269-279.

https://link.springer.com/chapter/10.1007/978-3-319-93713-7_22