SOCCER SEASON SIMULATION WITH FIXED MATCHES

Authors

  • Oleg Chertov National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"; Faculty of Applied Mathematics; Department of Applied Mathematics, ave. 37 Beresteyskyi str., Kyiv, Ukraine, 03056, Ukraine http://orcid.org/0000-0003-0087-1028
  • Ivan Zhuk National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"; Faculty of Applied Mathematics; Department of Applied Mathematics, ave. 37 Beresteyskyi str., Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0001-8440-427X

DOI:

https://doi.org/10.20535/kpisn.2022.1-2.287916

Keywords:

football season, team grouping, Poisson distribution, goal difference histogram, type of match result

Abstract

Background. Football is a huge industry comparable to traditional economic sectors. In numerical terms, it is tens of billions of dollars. But one of the most important problems that this industry faces is match fixing (matches with a fixed result).

The most effective way to prevent this threat is a multilateral approach combined with measures aimed at expanding the potential of law enforcement agencies and sports organizations. One of the components of this approach is the use of mathematical methods for identifying suspicious match-fixing results.

Objective. The purpose of the paper is to develop an algorithm for modelling fixed matches related to earnings on bets, which is used to form matches which results are different from the expected ones and can be considered as anomalous.

Methods. Calculation of probabilities of scoring goals by teams during the game based on real data of the season; development of a simulation model of a football season without fixed matches and its analysis by means of statistical modelling; development of an algorithm for modelling fixed matches related to earnings on bets and its analysis.

Results. A simulation model of a football season has been developed, which allows, using probability distributions of the number of goals scored by teams during home or away games obtained on real data, simulating the results of matches, taking into account the strength of the teams and the game type, as well as to simulate the situations of a “fixed” match, replacing the current results. In terms of the overall distributions of match result types and goal differences of all matches, the simulated season is similar to the real season. According to the Kolmogorov-Smirnov test, the difference between the given distributions at the significance level of 0.001 is statistically insignificant.

Conclusions. The developed simulation model of a football season can be used to study the effectiveness of methods for detecting fixed matches and their comparative analysis.

References

“Resource Guide on Good Practices in the Investigation of Match-Fixing”, United Nations : Office on Drugs and Crime. //www.unodc.org/unodc/en/safeguardingsport/publications/match-fixing.html (accessed Jan. 05, 2022).

D. Forrest, and I.G. McHale, “Using statistics to detect match fixing in sport”, IMA Journal of Management Mathematics, Vol. 30, No 4, pp. 431–449, Sep. 2019. doi: 10.1093/imaman/dpz008

S. Anfilets et al., “DEEP MULTILAYER NEURAL NETWORK FOR PREDICTING THE WINNER OF FOOTBALL MATCHES”, International Journal of Computing, pp. 70–77, Mar. 2020. doi: 10.47839/ijc.19.1.1695

N. Razali, A. Mustapha, F.A. Yatim, and R.A. Aziz, “Predicting Football Matches Results using Bayesian Networks for English Premier League (EPL) ”, IOP Conf. Ser.: Mater. Sci. Eng., Vol. 226, No 1, p. 012099, Aug. 2017. doi: 10.1088/1757-899X/226/1/012099

T. Narizuka, Y. Yamazaki, and K. Takizawa, “Space evaluation in football games via field weighting based on tracking data”, Sci Rep, Vol. 11, No 1, p. 5509, Mar. 2021. doi: 10.1038/s41598-021-84939-7

Laxhammar, R., & Falkman, G. (2011). Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance. 14th International Conference on Information Fusion, 1–8.

Ho, S.-S., Schofield, M., Sun, B., Snouffer, J., & Kirschner, J. (2019). A Martingale-Based Approach for Flight Behavior Anomaly Detection. 2019 20th IEEE International Conference on Mobile Data Management (MDM), 43–52. https://doi.org/10.1109/MDM.2019.00-75

focus.ua, “Dirty games. Match-fixing in Ukrainian football”, FOCUS, Mar. 05, 2013. https://focus.ua/ukraine/263259 (accessed Jan. 05, 2022).

B. Constandt, and E. Manoli, “Understanding match-fixing in sport: Theory and practice”. 2022.

“Matchs truqués : coup de filet dans le milieu du football professionnel”, leparisien.fr, No 18, 2014. https://www.leparisien.fr/faits-divers/corruption-coup-de-filet-dans-le-milieu-du-football-professionnel-18-11-2014-4301229.php (accessed Jan. 05, 2022).

L. Chami, “Matchs truqués de Ligue 2 : 18 mois ferme pour les anciens dirigeants nîmois”, leparisien.fr, Sep. 13, 2018. https://www.leparisien.fr/sports/football/matchs-truques-de-l2-18-mois-ferme-pour-les-anciens-dirigeants-nimois-13-09-2018-7887090.php (accessed Jan. 05, 2022).

K.P. Murphy, Probabilistic Machine Learning: Advanced Topics”, MIT Press, 2021.

S.D. Langan, “Predict Football Matches: Using Spreadsheet Models to Become a Winning Sports Bettor” (Kindle Edition), 2013, 379 p.

S.M. Ross, “Introductory Statistics”, Academic Press, 2017.

N.Sh. Kremer, “Theory of Probability and Mathematical Statistics”, UNITY-DANA, 2001.

Published

2023-10-27

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