Construction and Simulation of a Multiattribute Training Data Mining Model for Basketball Players Based on Big Data

Abstract

This paper provides an in-depth analysis and research on the construction and simulation of a big data model for multiattribute training of basketball players. To get a more accurate and three-dimensional information, the training can use a multitraining target robot, i.e., to detect feedback on multiple indicators at the same time and correct the player's errors in time; the other is an auxiliary robot, which can actively correct technical movements and train the player to form muscle memory, compared with general training. The analysis results show that by either constructing a human model or designing an active assistive robot, the player's technical movements can be regulated accordingly, protecting the player's body laterally and improving the player's ability. An assisted training system with an accurate model of physiological indicators is constructed based on the data of the player throughout the season. The Warriors, who have applied this system, not only have the best record in recent years but also have the lowest injury rate in the league, indicating that this method has indeed reduced the injury rate of players.