Detection of fixed football matches based on the theory of conformal predictors using the modified Stepanets indicator function

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.282645

Keywords:

fixed result, power martingale, measure of non-conformity, offline algorithm, p-value, F1 m etric

Abstract

An urgent issue of modern football competitions is the detection of fixed matches. Known methods for predicting the outcome of a match by analyzing bets on a match or analyzing the actions of football players on the field use a large amount of data that is not always available. To overcome this obstacle, there can be applied a method for detecting suspicious fixed match results based on conformal predictors and power martingales, which uses publicly available public data. But in practice, this method does not always detect such matches with high precision. An improved method for determining a suspicious match is proposed, based on the theory of conformal predictors using a modified Stepanets indicator function, which is compared with a threshold. The modified Stepanets indicator function is applied to the power martingale and shows the relative change in the martingale value of the current match compared to the previous match. The threshold value was determined experimentally according to the criterion of the maximum of the F1 metric. Data from the 2013–2014 season of the French II League were used as a training sample, and data from the 2014–2015 season of Serie B in Italy were used as a test sample. Team clustering was performed on all samples. For each of the formed classes of matches on both samples, the measure of non-conformity, the degree of non-conformity, the power martingale, and the modified Stepanets indicator function were calculated. The resulting indicators of precision metrics and F1 are higher (average values of metrics P=0.84, F1=0.87) than the same indicators of martingale and p-value rules (average values of metrics P=0.75, F1=0.78), applied to the same data. The proposed method reveals 4 out of 5 matches of the 2014–2015 Serie B season in Italy, which are considered fixed according to information from official Italian law enforcement sources

Author Biographies

Oleg Chertov, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Doctor of Technical Sciences, Professor

Department of Applied Mathematics

Ivan Zhuk, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Postgraduate Student

Department of Applied Mathematics

References

  1. Abarbanel, B., Johnson, M. R. (2018). Esports consumer perspectives on match-fixing: implications for gambling awareness and game integrity. International Gambling Studies, 19 (2), 296–311. doi: https://doi.org/10.1080/14459795.2018.1558451
  2. Lilley, E. (2015). A Review of the recommendations of the ‘Report of the Sports Betting Integrity Panel’ in assessing the progress towards tackling Match-fixing in Sport. Laws of the Game, 1 (1).
  3. Huggins, M. (2018). Match-Fixing: A Historical Perspective. The International Journal of the History of Sport, 35 (2-3), 123–140. doi: https://doi.org/10.1080/09523367.2018.1476341
  4. Forrest, D., McHale, I. G. (2019). Using statistics to detect match fixing in sport. IMA Journal of Management Mathematics, 30 (4), 431–449. doi: https://doi.org/10.1093/imaman/dpz008
  5. Razali, N., Mustapha, A., Yatim, F. A., Ab Aziz, R. (2017). Predicting Football Matches Results using Bayesian Networks for English Premier League (EPL). IOP Conference Series: Materials Science and Engineering, 226, 012099. doi: https://doi.org/10.1088/1757-899x/226/1/012099
  6. Anfilets, S., Bezobrazov, S., Golovko, V., Sachenko, A., Komar, M., Dolny, R., Kasyanik, V. et al. (2020). Deep multilayer neural network for predicting the winner of football matches. International Journal of Computing, 19 (1), 70–77. doi: https://doi.org/10.47839/ijc.19.1.1695
  7. Spapens, T., Olfers, M. (2015). Match-fixing: The Current Discussion in Europe and the Case of The Netherlands. European Journal of Crime, Criminal Law and Criminal Justice, 23 (4), 333–358. doi: https://doi.org/10.1163/15718174-23032077
  8. Stepanets, A. I. (2005). Methods of Approximation Theory. Boston. doi: https://doi.org/10.1515/9783110195286
  9. Chertov, O., Zhuk, I., Serdyuk, A. (2021). Search of the Deviation from the Natural Process Using Stepanets Approach for Classification of Functions. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). doi: https://doi.org/10.1109/idaacs53288.2021.9660997
  10. Vovk, V. (2014). The Basic Conformal Prediction Framework. Conformal Prediction for Reliable Machine Learning, 3–19. doi: https://doi.org/10.1016/b978-0-12-398537-8.00001-8
  11. Ho, S.-S., Wechsler, H. (2010). A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (12), 2113–2127. doi: https://doi.org/10.1109/tpami.2010.48
  12. Zhuk, I., Chertov, O. (2023). Framework based on conformal predictors and power martingales for detection of fixed football matches . Eastern-European Journal of Enterprise Technologies, 2 (4 (122)), 6–15. doi: https://doi.org/10.15587/1729-4061.2023.276977
  13. Historical analysis of closing odds. Available at: https://github.com/Lisandro79/BeatTheBookie
  14. Catania’s owner admits to match fixing in five Serie B games. The Guardian. Available at: https://www.theguardian.com/football/2015/jun/30/catania-match-fixing-serie-b
Detection of fixed football matches based on the theory of conformal predictors using the modified Stepanets indicator function

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Published

2023-06-30

How to Cite

Chertov, O., & Zhuk, I. (2023). Detection of fixed football matches based on the theory of conformal predictors using the modified Stepanets indicator function. Eastern-European Journal of Enterprise Technologies, 3(4 (123), 22–32. https://doi.org/10.15587/1729-4061.2023.282645

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Section

Mathematics and Cybernetics - applied aspects