基于网络的变参数隐马尔可夫模型对篮球运动员价值的评价
Basketball Player's Value Evaluation by a Networks-based Variant Parameter Hidden Markov Model
摘要：
通过对篮球运动员行为的分析来确定篮球运动员的价值，对现代篮球队的管理者具有重要意义。然而，传统的方法总是利用孤立的统计数据，导致无效和不准确的评估。基于动态网络理论的现有模型对此类评估的结果进行了重大改进，但所述模型仍然不精确，因为它们仅关注评估单个球员的价值，而不是在当前团队中考虑这些价值。针对这一问题，提出了一种基于网络和隐马尔可夫模型的分析评价模型。据我们所知，我们是第一个将代表玩家的网络形式与使用隐马尔可夫模型相结合来挖掘网络并生成所需结果的人。将我们的方法应用于从NBA收集的SportVU数据表明，这种分析和评价模型可以有效地分析每个球员在比赛中的表现，并为球队管理者提供了一个辅助工具。
英文摘要：
Determining the value of basketball players through analyzing the players' behavior is important for the managers of modern basketball teams. However, conventional methods always utilize isolated statistical data, leading to ineffective and inaccurate evaluations. Existing models based on dynamic network theory offer major improvements to the results of such evaluations, but said models remain imprecise because they focus merely on evaluating the values of individual players rather than considering them within their current teams. To solve this problem, we propose an analysis and evaluation model based on networks and a hidden Markov model. To the best of our knowledge, we are the first to combine a network form representing the players who are playing with the use of a hidden Markov model to mine the network and generate the desired results. Applying our approach to SportVU data collected from the National Basketball Association shows that this analysis and evaluation model can effectively analyze the performance of each player in a game and provides an assistive tool for team managers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
