Background Dynamic visual attention is important in basketball because it may affect the performance of players and thus the match outcome. The goals of this study were to investigate the difference in dynamic visual attention characteristics between highly skilled basketball players and nonathletic college students and to explore the relationship between visual attention and game-related performance among the basketball players. Methods In total, 24 highly skilled basketball players and 24 nonathletic college students participated in a multiple object tracking task. The task was conducted so that either the number of targets that were visually tracked or the speed at which a given number of tracked targets moved was altered to examine the difference in dynamic visual attention characteristics between the basketball players and nonathletic college students. The relationship between visual tracking speed (VTS) and game-related statistics, including assists, steals, mistakes, fouls and points scored recorded for every match during the season, was assessed among the basketball players by using Pearson correlations. Results A significant main effect of target tracking load was observed (P < 0.001), with visual tracking performance significantly decreased as target number increased. In addition, the speed at which the targets moved had a significant effect on visual tracking performance (P < 0.001), with tracking performance significantly decreased as target speed increased. However, no significant difference was observed in the abilities of basketball players and nonathletic college students to simultaneously track up to six targets. By contrast, a significant interaction between group and target speed was found (P < 0.001), with the visual tracking accuracy of basketball players significantly greater than that of college students at the higher target speeds examined (P < 0.001). Among basketball players, there were positive, large, and statistically significant correlations in the accuracy in VTS trials and the number of assists (P < 0.001) and between the accuracy in VTS trials and the number of steals (P < 0.001). Conclusion The advantage of skilled basketball players to handle dynamic visual information in a multiple object tracking task was not attributable to the target number but to the target speed. Those athletes with greater dynamic visual attention were more likely to successfully assist or to steal the ball, enhancing performance of the athlete as well as contributing to a more successful team match. These findings may inform basketball training programs to improve player and team performances during matches.

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Data mining is a practice that employs mathematical algorithms to search for hidden information in a large amount of data to analyse the underlying pattern and law, and this practice is also known as knowledge discovery in data. The National Basketball Association (NBA) is the professional basketball game at the highest level in the world, and many events in an NBA game are used for statistical analysis. In this paper, data mining technology was applied based on event statistics to quantify the ability of basketball players and teams, the aim of the exercise being to predict basketball results. According to the NBA (2013–2018) season competition data, the quantitative evaluation method was firstly used to establish a player ability evaluation model, and the feature variable selection history game data weighting method was selected to construct a team player ability evaluation feature system. Secondly, machine learning algorithms such as linear regression, XGBoost and neural network models were used to predict the player performance. Considering the randomness and uncertainty of sports competitions, this paper deploys a combination of data mining algorithms and statistical simulation methods to predict the uncertainty of events, and the results indicate a good prediction effect. Therefore, this combined method is worthy of being applied in the evaluation system of team players and in game prediction.