IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/106821.html
   My bibliography  Save this paper

Betting models using AI: a review on ANN, SVM, and Markov chain

Author

Listed:
  • Kollár, Aladár

Abstract

In today's modern world, sports generate a great deal of data about each athlete, team, event, and season. Many people, from spectators to bettors, find it fascinating to predict the outcomes of sporting events. With the available data, the sports betting industry is turning to Artificial Intelligence. Working with a great deal of data and information is needed in sports betting all over the world. Artificial intelligence and machine learning are assisting in the prediction of sporting trends. The true influence of technology is felt as it offers these observations in real-time, which can have an impact on important factors in betting. An artificial neural network is made up of several small, interconnected processors called neurons, which are similar to the biological neurons in the brain. In ANN framework, MLP, the most applicable NN algorithm, are generally selected as the best model for predicting the outcomes of football matches. This review also discussed another common technique of modern intelligent technique, namely Support Vector Machines (SVM). Lastly, we also discussed the Markov chain to predict the result of a sport. Markov chain is the sequence or chain from which the next sample from this state space is sampled.

Suggested Citation

  • Kollár, Aladár, 2021. "Betting models using AI: a review on ANN, SVM, and Markov chain," MPRA Paper 106821, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:106821
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/106821/1/MPRA_paper_106821.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Frank Percy, 2015. "Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1840-1849, November.
    2. Srivastav, Bhanu, 2021. "The novel Artificial Neural Network assisted models: A review," MPRA Paper 106499, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michal Friesl & Liam J. A. Lenten & Jan Libich & Petr Stehlík, 2017. "In search of goals: increasing ice hockey’s attractiveness by a sides swap," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1006-1018, September.
    2. Manner Hans, 2016. "Modeling and forecasting the outcomes of NBA basketball games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 31-41, March.
    3. Johnston, Iain G., 2022. "Optimal strategies in the fighting fantasy gaming system: Influencing stochastic dynamics by gambling with limited resource," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1272-1281.
    4. Michal Friesl & Jan Libich & Petr Stehlík, 2020. "Fixing ice hockey’s low scoring flip side? Just flip the sides," Annals of Operations Research, Springer, vol. 292(1), pages 27-45, September.
    5. Collingwood, James A.P. & Wright, Michael & Brooks, Roger J., 2023. "Simulating the progression of a professional snooker frame," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1286-1299.

    More about this item

    Keywords

    Artificial Intelligence; ANN; Betting; sports; SVM; Markov chain;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:106821. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.