Advertisement

Using Convolutional Neural Networks to Forecast Sporting Event Results

  • Mu-Yen ChenEmail author
  • Ting-Hsuan Chen
  • Shu-Hong Lin
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 866)

Abstract

Sporting events like the FIFA World Cup and the World Baseball Classic have increased in popularity, and the enthusiasm with which these competitions are reported and commented on is evidence of their wide-reaching influence. These games are popular discussion topics. Many who follow sports are only casual fans, but even these “temporary” fans have the same expectations that all fans do: the teams they support should be able to win. This study selects the National Basketball Association (NBA) to represent competitive ball sports. Predictions herein are based on the examination and statistical analysis of previous records of NBA games. This research integrates the field of sports outcome predictions with the methodology of deep learning via convolutional neural networks. Training and predictions are modelled on statistics gleaned from a total of 4,235 games over the past three years. We analyze the training results of a variety of model structures. While previous studies have applied convolutional neural networks to image or object recognition, our study proposes a specific encoding method that is integrated with deep learning in order to predict the results of future games. The prediction accuracy rate of the model herein is approximately 91%, while the deviation is approximately 0.2. These strong results confirm the validity of our designated encoding method.

Keywords

Sports prediction Deep learning Convolutional neural networks National basketball association (NBA) 

References

  1. 1.
    Google: Google Trend. https://trends.google.com.tw/trends/?geo=TW (2018). Accessed 4 June 2018
  2. 2.
    Zhao, W.X., Wu, H.H.: Japan’s first–full of actuarial science. Business Weekly 1500 (2016)Google Scholar
  3. 3.
    Fong, R.S.: Studies on predicting the outcome of professional baseball games with data mining techniques: MLB as a case. Department of Information Management of Chinese Culture University. Unpublished Thesis (2013)Google Scholar
  4. 4.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962)CrossRefGoogle Scholar
  5. 5.
    Fukushima, K., Miyake, S.: Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and Cooperation in Neural Nets, pp. 267–285. Springer, Berlin (1982)CrossRefGoogle Scholar
  6. 6.
    Indolia, S., Goswami, A.K., Mishra, S.P., Asopa, P.: Conceptual understanding of convolutional neural network-a deep learning approach. Procedia Comput. Sci. 132, 679–688 (2018)CrossRefGoogle Scholar
  7. 7.
    Craig, C., Overbeek, R.W., Condon, M.V., Rinaldo, S.B.: A relationship between temperature and aggression in NFL football penalties. J. Sport. Health. Sci. 5(2), 205–210 (2016)CrossRefGoogle Scholar
  8. 8.
    Maszczyk, A., Gołaś, A., Pietraszewski, P., Roczniok, R., Zając, A., Stanula, A.: Application of neural and regression models in sports results prediction. Procedia Soc. Behav. Sci. 117, 482–487 (2014)CrossRefGoogle Scholar
  9. 9.
    Bačić, B.: Towards the next generation of exergames: flexible and personalised assessment-based identification of tennis swings. In: 2018 International Joint Conference on Neural Networks (2018).  https://doi.org/10.1109/ijcnn.2018.8489602
  10. 10.
    Bunker, R.P., Thabtah, F.: A machine learning framework for sport result prediction. Appl. Comput. Inform. 15(1), 27–33 (2019)CrossRefGoogle Scholar
  11. 11.
    Kipp, K., Giordanelli, M., Geiser, C.: Predicting net joint moments during a weightlifting exercise with a neural network model. J. Biomech. 74, 225–229 (2018)CrossRefGoogle Scholar
  12. 12.
    NBA Media Ventures: NBA official website. https://nba.udn.com/nba/index (2018). Accessed 4 June 2018
  13. 13.
    Manley, M.: Martin Manleys Basketball Heaven. Doubleday Books (1989)Google Scholar
  14. 14.
    Kline, D.M., Berardi, V.L.: Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neural Comput. Appl. 14(4), 310–318 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Information and ManagementNational Taichung University of Science and TechnologyTaichungTaiwan
  2. 2.Faculty of FinanceNational Taichung University of Science and TechnologyTaichungTaiwan

Personalised recommendations