Evaluation of the value of basketball players based on wireless network and improved Bayesian algorithm
AbstractThe selection of basketball players should highlight their specific characteristics and proceed according to the essential laws of basketball. When the acquired training level becomes closer and closer, and is more and more conducive to the control of the entire training and competition, the selection of the standard paradigm of basketball players plays a key role. At present, the existing evaluation methods of basketball players are limited to the human experience of coaches, and there is a lack of further information evaluation methods. This article discusses a new type of basketball player evaluation scheme that combines wireless network and machine learning methods. First, the wireless sensor network is used to perceive basketball players' performance on the court and record various evaluation indicators. Secondly, establish a player value evaluation model through improved Bayesian algorithm and fuzzy comprehensive evaluation methods. Finally, after relevant tests and comparisons with the coaches' results, the model showed better evaluation results and a fairer value distribution.
