Nonlinear Random Matrix Model and Research for Quantitative Representation of Volleyball Attacker’s Action Links

Abstract

With the promotion of volleyball, the technical and tactical level of volleyball is also constantly improving, and the competition in volleyball competition is also extremely fierce. Especially in important games, it can be seen that most of the outstanding volleyball players have the characteristics of both offense and defence. The outside hitter is the main attack point and end point of the team’s offense and defence. The outside hitter’s offensive and defensive ability plays a crucial role in the final outcome of the game. It can be said that an excellent outside hitter is one of the main indicators to measure whether a team can become a world volleyball team. This paper introduces and analyses the optimal solution of kernel norm minimization and its application in the RPCA model. Through analysis, we know that when the alternating direction method solves the RPCA problem, the most expensive calculation is the optimization of the nuclear norm. Facing the singular value decomposition calculation of large-scale matrices, in order to solve the shortcomings of time-consuming and memory-consuming to solve large-scale matrix calculations, this paper proposes new stochastic singular value decomposition algorithms. The outside hitters use the rubbing, slapping, and hanging techniques to adjust their offense, but they rarely use them in the setter’s active organization. Through the research on adjusting the offense, it is found that the outside hitter’s advantage in adjusting the offense not only compensates for the impact of the fluctuating first pass and inadequate defence but also resolves the opponent’s sharp serve and spike, which increases the morale of the team. It is the most important condition for the women’s volleyball team to win the Olympic Games. Overall, the scoring rate of the outside hitter in each offensive position is analysed, which provides important data parameters for the women’s volleyball team to distribute the ball.

Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports

“…Aldous et al [40] and Ntzoufras et al [41] are developing stochastic and Bayesian methods for analyzing game dynamics, while Dai et al [42] and Yuan et al [43] are exploring the role of technological innovations in analyzing volleyball techniques. Endriani et al [44] proposes a model based on Umbrella Learning, while Leng et al [45], Li et al [46], and Wang et al [47] use random matrix models for analyzing learning and gameplay actions. Salimifard et al [48], in turn, is developing an algorithm for scheduling in the volleyball premier league.…”

“…Endriani et al [44] put forth a lower pass model anchored in the Umbrella Learning paradigm. Meanwhile, a series of works [45,46,47] leverage random matrix models to gauge the efficacy of collegiate volleyball training and offer a quantitative portrayal of offensive player maneuvers. Rounding off the discussion, a study [48] unveils an algorithm tailored for green planning in the context of the volleyball premier league.…”