Fuzzy Study on the Winning Rate of Football Game Betting

  • Woo-Joo Lee Department of Mathematics, Yonsei University, Seoul, Korea
  • Hyo-Jin Jhang School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang, Korea
  • Seung Hoe Choi School of Liberal Arts and Science, Korea Aerospace University, Goyang, Korea
Keywords: winning rate prediction, ELO rating, fuzzy number, fuzzy partition, regression model


This study aims to find variables that affect the winning rate of the football team before a match. Qualitative variables such as venue, match importance, performance, and atmosphere of both teams are suggested to predict the outcome. Regression analysis is used to select proper variables. In this study, the performance of the football team is based on the opinions of experts, and the team atmosphere can be calculated with the results of the previous five games. ELO rating represents the state of the opponent. Also, the selected qualitative variables are expressed in fuzzy numbers using fuzzy partitions. A fuzzy regression model for the winning rate of the football team can be estimated by using the least squares method and the least absolute method. It is concluded that the stadium environment, ELO rating, team performance, and importance of the match have effects on the winning rate of Korean National Football (KNF) team from the data on 118 matches.


“FIFA,” http://www.fifa.com/index.html, July 05, 2019.

H. O. Steller, D. Sandor, and R. Verlander, “Issues in Sports Forecasting,” International Journal of Forecasting, vol. 26, no. 3, pp. 606-621, July-September 2010.

F. Wunderlich and D. Memmert, “Analysis of the Predictive Qualities of Betting Odds and FIFA World Ranking: Evidence from the 2006, 2010, and 2014 Football World Cups,” Journal of Sports Sciences, vol. 34, no. 24, pp. 2176-2184, August 2016.

M. Carpita, E. Ciavolino, and P. Pasca, “Exploring and Modelling Team Performances of the Kaggle European Soccer Database,” Statistical Modelling, vol. 19, no. 1, pp. 74-101, February 2019.

Y. F Alfredo and S. M. Isa, “Football Match Prediction with Tree Based Model Classification,” International Journal of Intelligent Systems and Applications, vol. 11, no. 7, pp. 20-28, July 2019.

T. G. Omomule, A. J. Ibinuolapo, and O. O. Ajayi, “Fuzzy-Based Model for Predicting Football Match Results,” International Journal of Scientific Research in Computer Science and Engineering, vol. 8, no. 1, pp. 70-80, February 2020.

E. Esme and M. S. Kiran, “Prediction of Football Match Outcomes Based on Bookmakers Odds by Using K-Nearest Neighbor Algorithm,” International Journal of Machine Learning and Computing, vol. 8, no. 1, pp. 26-32, February 2018.

J. M. Pérez-Sánchez, E. Góme-Déniz, and N. Dávila-Cárdenes, “A Comparative Study of Logistic Models Using an Asymmetric Link: Modelling the Away Victories in Football,” Symmetry, vol. 10, no. 6, June 2018.

C. P. Igiri and E. O. Nwachukwu, “An Improved Prediction System for Football a Match Result,” IOSR Journal of Engineering, vol. 4, no. 12, pp. 12-20, December 2014.

H. Liu, M. Á. Gomez, C. Lago-Peñas, and J. Sampaio, “Match Statistics Related to Winning in the Group Stage of 2014 Brazil FIFA World Cup,” Journal of Sports Sciences, vol. 33, no. 12, pp. 1205-1213, March 2015.

“Korea Football Association,” http://www.kfamatch.or.kr/svc/man/selectMainInfo.do, July 15, 2019.

J. Goddard and I. Asimakopoulos, “Forecasting Football Results and the Efficiency of Fixed-Odds Betting,” Journal of Forecasting, vol. 23, no. 1, pp. 51-66, January 2004.

R. D. Baker and I. G. McHale, “Forecasting Exact Scores in National Football League Games,” International Journal of Forecasting, vol. 29, no. 1, pp. 122-130, January-March 2013.

G. Boshnakov, T. Kharrat, and I. G. McHale, “A Bivariate Weibull Count Model for Forecasting Association Football Scores,” International Journal of Forecasting, vol. 33, no. 2, pp. 458-466, April-June 2017.

M. J. Dixon and M. E. Robinson, “A Birth Process Model for Association Football Matches,” Journal of the Royal Statistical Society Series D (The Statistician), vol. 47, no. 3, pp. 523-538, September 1998.

J. H. Kim, G. T. Ro, J. S. Park, and W. H. Lee, “The Development of Soccer Game Win-Lost Prediction Model Using Neural Network Analysis,” Korean Journal of Sport Science, vol. 18, no. 4, pp. 54-63, 2007.

A. C. Constantinou, N. E. Fenton, and M. Neil, “Profiting from an Inefficient Association Football Gambling Market: Prediction, Risk and Uncertainty Using Bayesian Networks,” Knowledge-Based Systems, vol. 50, pp. 60-86, September 2013.

H. Rue and O. Salvesen, “Prediction and Retrospective Analysis of Soccer Matches in a League,” Journal of the Royal Statistical Society: Series D (The Statistician), vol. 49, no. 3, pp. 399-418, September 2000.

J. Sargent and A. Bedford, “Improving Australian Football League Player Performance Forecasts Using Optimized Nonlinear Smoothing,” International Journal of Forecasting, vol. 26, no. 3, pp. 489-497, July-September 2010.

S. H. Choi and J. H. Yoon, “General Fuzzy Regression Using Least Squares Method,” International Journal of Systems Science, vol. 41, no. 5, pp. 477-485, March 2010.

H. Y. Jung, J. H. Yoon, and S. H. Choi, “Fuzzy Linear Regression Using Rank Transform Method,” Fuzzy Sets and Systems, vol. 274, pp. 97-108, September 2015.

H. Tanaka, S. Uejima, and K. Asai, “Linear Regression Analysis with Fuzzy Model,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 12, no. 6, pp. 903-907, 1982.

L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338-353, June 1965.

S. H. Choi and J. J. Buckley, “Fuzzy Regression Using Least Absolute Deviation Estimators,” Soft Computing, vol. 12, no. 3, pp. 257-263, May 2007.

H. J. Jhang, H. Kwak, and S. H. Choi, “Analysis of the Outcome for the Korean Pro-Basketball Games Using Regression Model,” Journal of The Korean Institute of Intelligent Systems, vol. 25, no. 5, pp. 489-494, October 2015.

L. A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—I,” Information Sciences, vol. 8, no. 3, pp. 199-249, 1975.

C. Mencar, M. Lucarelli, C. Castiello, and F. A. Maria, “Design of Strong Fuzzy Partitions from Cuts,” 8th Conference of the European Society for Fuzzy Logic and Technology, September 2013, pp. 424-431.

H. K. Kim and S. H. Choi, Statistical Analysis with Applications, Korea: Kyung Moon Sa, 2002. (In Korean)

J. Lasek, Z. Szlávik, M. Gagolewski, and S. Bhulai, “How to Improve a Team’s Position in the FIFA Ranking? A Simulation Study,” Journal of Applied Statistics, vol. 43, no. 7, pp. 1349-1368, October 2016.

K. Suzuki and K. Ohmori, “Effectiveness of FIFA/Coca-Cola World Ranking in Predicting the Results of FIFA World Cup Finals,” Football Science, vol. 5, pp. 18-25, March 2008.

L. M. Hvattum and H. Arntzen, “Using ELO Ratings for Match Result Prediction in Association Football,” International Journal of Forecasting, vol. 26, no. 3, pp. 460-470, July-September 2010.

“WFER: The World Football Elo Rating system,” http://www.eloratings.net, June 10, 2019.

“Sportstoto,” http://www.sportstoto.co.kr/index.jsp, May 12, 2019.

“Wisetoto,” http://www.wisetoto.com/index.htm, May 12, 2019.

“Betman,” www.betman.co.kr, June 02, 2019.

“William Hill,” http://sports.williamhill.com/bet/en-gb, April 04, 2019.

“Chosunilbo,” http://premium.chosun.com/site/data/html_dir/2014/07/03/2014070300008.html, March 03, 2019.

M. J. Dixon and P. F. Pope, “The Value of Statistical Forecasts in the UK Association Football Betting Market,” International Journal of Forecasting, vol. 20, no. 4, pp. 697-711, October-December 2004.

How to Cite
W.-J. Lee, H.-J. Jhang, and S. H. Choi, “Fuzzy Study on the Winning Rate of Football Game Betting”, Adv. technol. innov., May 2021.