Mining and Prediction of Large Sport Tournament Data Based on Bayesian Network Models for Online Data

• Published in: Wireless communications and mobile computing 2022-05, Vol.2022, p.1-8
• Main Author: Wang, Junjian
• Format: Article
• Language: English
• Subjects: Athletes; Bayesian analysis; Data mining; Players; Prediction models; Probability; Random variables; Sports; Statistical analysis; Teams; Tournaments & championships
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/1211015

In the modern society, competitive sports have an essential place in the world. Sports can reflect not only the comprehensive strength of a country to some extent but also the cohesion of a nation. Therefore, as China’s overall strength and international influence continue to rise, sports are being given more and more importance. At the same time, research and exploration on the prediction of match results have also become a hot topic. In the large sport tournaments, there are many factors that influence the outcome of a match. In actual matches, the outcome is not only determined by the strength of the participants, but also by a number of unexpected factors. The randomness brought about by these unexpected factors makes it difficult to predict the outcome of a sporting event. In recent years, many researchers have sought to enhance the understanding of complex objects with the help of prediction of sporting outcomes. One of the more traditional methods of prediction is the probabilistic statistical method. However, the traditional prediction methods have low accuracy and do not provide satisfactory stability in the prediction results. In fact, since most sporting matches are played against each other, the ability values of the players often play a key role in the match. They can determine the winner of a match, but unexpected factors such as player play, playing time, and injury situations can also have an impact on the strength of a playing team, so these factors should not be ignored. This study establishes a reasonable causal relationship between the offensive and defensive situations in the game and the players’ ability values and builds a complex Bayesian network model. A match prediction model is then built using the latent variables present during the match so that the various ability values of the match teams can be assessed.