The Effect of Physical Training of Athletes Based on Parametric Bayesian Estimation in the Context of Big Data

Abstract

Athletes must maintain their physical fitness in order to compete in any sport. The event can be organized for a single person or multiple people. Irrespective of the number of people or team participation in a sport, the people should have perfect training. The performance and physical fitness of the candidate will be measured under various categories, and the data will be stored in the database. The data to be collected about each event, player, coach, and others will result in the creation of big data with the aid of artificial intelligence and wireless networking. Wireless networking aids in the collection of data around the globe in a shorter period with the aid of intelligent servers. In this study, a recursive Bayesian estimation algorithm is implemented to perform the analysis of training and testing of the athlete’s performance with physical training. The proposed algorithm achieved an accuracy of 99%, which is a minimum increase in nine (09) percentage points over the neural network and an 18% growth over the fuzzy set model. The proposed models are able to analyze players’ success at a higher level based on their scores at each factor level. The experimental results show that the proposed model outperforms well in enhancing player performance.