RETRACTED ARTICLE: Evaluation of the value of basketball players based on wireless network and improved Bayesian algorithm

Abstract

The 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.

Application of Artificial Intelligence in Basketball Sport

“…The author used this method to evaluate the fast dribbling ability and shooting accuracy of 492 young male and female basketball players between the ages of 18 and 20. The results showed that the model had better evaluation results and fairer value distribution compared with the results of coaches [20]. Fans have closely watched the annual draft for prospects of the NBA and the prediction of the future performance of these prospective players.…”

Seepage behavior assessment of earth-rock dams based on Bayesian network

“…Bayesian network is often used to establish human health assessment and structural safety assessment. 12,13 It is suitable for evaluation and analysis based on related multi-factors [14][15][16][17] and can take into proper account multi-factor seepage behavior assessment of earth-rock dams and cause analysis of abnormal phenomena and abnormal measured values. 18 So, it can assess the possibility of seepage damage based on the project quality, maintenance and repair status, monitoring data, and on-site inspection and analyze the most likely cause of damage from the monitoring data and on-site inspection results.…”