Scientific Journal

Herald of Advanced Information Technology

PREDICTING OF SPORTS EVENTS RESULTS
Abstract:

Today, the field of betting and bookmaking is popular with a wide range of sports fans. Issues of predicting the outcome of future events are and will be relevant for everyday life, sports, politics, etc. With the increasing number and quality of methods of intellectual analysis, the idea of ​​predicting the results of sporting events became feasible. Applying different mathematical methods helps to obtain more accurate predictions of results than subjective expert estimates. The paper introduces the concept of betting and describes in general terms the task of bookmaking. The purpose of the study and the tasks that must be accomplished to achieve the goal are identified. Existing research results of different scientists who have researched this problem are analyzed. There are four basic principles for predicting the outcome of sports events. Different approaches to the task have been considered and our own way of solving it has been proposed. Methods such as Poisson distribution, simulation modeling of the Markov Monte Carlo chain, and many other research methods have been considered. The formulation of the problem is formulated and the properties of the problem are investigated. A backtesting algorithm was developed and described as a mechanism for presenting team statistics at any point in time for a particular season to collect sports event data. Correlation analysis for the selected parameters was shown to show a moderate correlation of data and the use of Google AutoML to identify patterns between the data was described. The importance of using machine learning to solve this problem is outlined. A system has been developed that collects event data and calculates statistics for each team at each point of time using the backtesting algorithm. A service has been developed to create and test the quality of the strategy. The results of experimental studies of task efficiency are presented, where we conducted experimental sets of strategies with and without adding the result of the AutoML service and for each strategy the Pearson correlation coefficient was calculated based on the results of two past seasons. The results obtained are analyzed.

Authors:
Keywords
DOI
10.15276/hait.04.2019.4
References
  1. What is betting?” [Electronic resource] – Access mode: https://marketbusinessnews.com/financial-glossary/betting/ – Active link – 15.12.2019.
  2. Norman Drey̆per & Harry Smyt. (2007)“Prykladnoj rehressyonnyj analyz. Mnozhestvennaia rehressyia” [Applied Regression Analysis. Multiple Regressions], 3-e yzd, Moscow: Russian Federation, Publ. Dyalektyka, 912 p.
  3. Baboota, R. & Kaur, H. (2018) “Predictive Analysis and Modeling Football Results using Machine Learning Approach for English Premier League”, International Journal of ForecastingForthcoming,doi: 10.1016/j.ijforecast.2018.01.003.
  4. Dobrovec, S. (2015) “Predicting Sports Results using Latent Features: A case Study”, 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 25-29 May 2015, pp. 1267 – 1272, doi: 10.1109/MIPRO.2015.7160470.
  5. Schumaker, R.P., Jarmoszko, A.T. & Labedz, C.S. (2016) “Predicting wins and Spread in the Premier League using a Sentiment Analysis of Twitter”, Decision Support Systems, Vol 88, pp. 76-84, doi: 10.1016/j.dss.2016.05.010.
  6. Radosavljevic, V. (2014) “Large-scale World Cup 2014 Outcome Prediction Based on Tumblr posts”, KDD Workshop on Large-Scale Sports Analytics, Sydney: Australia.
  7. Dorazio, T., Guaragnella, C., Leo, M. & Distante, A. (2004) “A new Algorithm for ball Recognition using Circle Hough Transform and Neural Classifier”, Pattern Recognition, Vol 37, pp. 393-408, doi: 10.1016/S0031-3203(03)00228-0.
  8. Kang, Y.L., Lim, J.H., Kankanhalli, M.S., Xu, C.S. & Tian, Q. (2004) “Goal Detection in Soccer Video using audio/visual Keywords”, IEEE International Conference on Image Processing (ICIP), Singapore, pp. 1629-1632, doi: 10.1109/ICIP.2004.1421381.
  9. Maher, M.J. (1982). “Modeling Association Football Scores”, Statistica Neerlandica, Vol 36, pp.109-118.
  10. Gianluca, B. & Blangiardo, M. (2010). “Bayesian Hierarchical Model for the Prediction of Football Results”, Journal of Applied Statistics, Vol 37, Issue 2, pp. 253-264, doi: 10.1080/02664760802684177.
  11. Karlis, D. & Ntzoufras, I. (2008) “Bayesian Modelling of Football Outcomes: Using the Skellams Distribution for the Goal Difference”,IMA Journal of Management Mathematics, pp. 229-244, doi: 10.1093/imaman/dpn026.
  12. Goddard, J. (2005) “Regression Models for Forecasting Goals and Match Results in Association Football”, International Journal of Forecasting, Vol 21, Issue 2, pp.331-340, doi: 10.1016/j.ijforecast.2004.08.002.
  13. Dixon, M.J. & Coles, S.G. (1997). “Modelling Association Football Scores and Inefficiencies in the Football Betting Market”, Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol 46, pp. 265-280, doi: 10.1111/1467-9876.00065.
  14. Forrest, D. & Simmons, R. (2000) “Forecasting Sport: the Behaviour and Performance of Football Tipsters”, International Journal of Forecasting, Vol 16, Issue 3, pp.317-331, doi: 10.1016/S0169-2070(00)00050-9.
  15. Koopman, S. J. & Lit, R. (2013) “A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League”, Journal of the Royal Statisitical Society: Series A, Vol 178, Issue 1, pp.167-186, doi: 10.1111/rssa.12042.
  16. Crowder, M., Dixon, M., Ledford, A. & Robinson, M. (2002) “Dynamic Modelling and Prediction of English Football League Matches for Betting”, The Statistician, Vol. 51, pp.157-168, doi: 10.1111/1467-9884.00308.
  17. Godin, F. (2014). “Beating the Bookmakers: Leveraging Statistics and Twitter Microposts for Predicting Soccer Results”, KDD Workshop on Large-Scale Sports Analytics, Sydney: Australia, doi: 10.13140/2.1.2168.0000.
  18. Byungho, M., Kim, J., Choe, C., Eom, H. & McKay, R.I. (2008). “A Compound Framework for Sports Results Prediction: A Football Case Study”, Knowldege Based Systems, Vol. 21, pp.551-562, doi: 10.1016/j.knosys.2008.03.016.
  19. Korolkov, A.N. (2014). “Ultradyannыe rytmы rezultatyvnosty v holfe” [Golf's Ultra-performance Rhythms], Vestnyk sportyvnoi nauky, Vol. 2, pp. 34-37.
  20. Hermanov, H.N. (2010) “Prohnoz dostyzhenyi rossyiskykh behunov na srednye y dlynnie distantsii na chempionatakh Evropi 2012-2014 hh. po rezultatam vistupleniy yuniorov i molodikh sportsmenov v Evropeiskykh Pervenstvakh” [Prognosis of Achievements of Russian Runners in the Middle and Long Distances at the European Championships 2012-2014 as a Result of the Performances of Juniors and young Athletes in the European Championships], Kultura fyzycheskaia y zdorove, Vol. 4, pp. 7-11.
  21. Korolkov, A.N. (2013).“Effektyvnost trenyrovky v holfe v vyde peredatochnoi funktsyy kvazistatsionarnikh spektrov rezultativnosty” [Golf Training Effectiveness as a Transfer Function of Quasi-stationary Performance Spectra],Teoryia y praktyka fyzycheskoi kulturi, Vol. 10,pp. 62-64.
  22. Kupalova, H.I. (2008) “Teoria ekonomichnoho analizu” [The theory of Economic Analysis], Kyiv: Ukraine, 639 p.
  23. Dan M. Frangopol. (2007) “Reliability and Optimization of Structural Systems: Assessment, Design, and life-cycle Performance: Proceedings of the Thirteenth IFIP WG 7.5”,Working Conference on Reliability and Optimization of Structural Systems,  Kobe: Japan, 269p.
  24. Ditlevsen, O. & Madsen, H.O. (2003) “Structural Reliability Methods”.Department of mechanical engineering. Technical University of Denmark maritime engineering, Denmark, 323 p.
  25. Hitoshi Furuta. (2002)“Reliability and Optimization of Structural Systems: Proceedings of the 10th IFIP WG7.5”,Working Conference on Reliability and optimization of structural systems, Osaka: Japan, 276 p.
  26. “Metod hruppoho uchëta arhumentov”[The Method of Group Accounting of Arguments] [Electronic resource] – Access mode: http://www.machinelearning.ru/wiki/metod hruppoho uchëta arhumentov/ – Active link – 15.12.2019.
  27. “Dokumentatsiia do servisu AutoML vid Google” [Google AutoML Documentation] [Electronic resource] – Access mode: https://cloud.google.com/automl/ – Active link – 15.12.2019.
  28. “Matematychne prohnozuvannia rezultativ futbolnykh matchiv”[Mathematical Prediction of Football Match Results] [Electronic resource] – Access mode: http://naub.oa.edu.ua/2015/ – Active link – 15.12.2019.
  29. “Market Business News” [Electronic resource] – Access mode: https://marketbusinessnews.com/financial-glossary/betting/ – Active link – 15.12.2019.
  30. “Gambling Sites” [Electronic resource] – Access mode: https://www.gamblingsites.com/sports-betting/introduction/what-a-bookmaker-does/ – Active link – 15.12.2019.
  31. Zhdanova, O.H., Romanchenko, B.V. & Sperkach, M.O. (2019).“Prohnozuvannia rezultativ sportivnykh podiy” [Predicting the Results of Sports Events],Matematychne ta imitatsiine modeliuvannia system MODS 2019,Chotyrnadtsiaty mizhnarodna naukovo-praktychna konferentsiia, pp. 288-292
Published:
Last download:
29 June 2020

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