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Simulation of interaction patterns and simulative effectiveness analysis of creative plays in team sports by means of neural networks

利用神经网络模拟团队运动中创意玩法的交互模式和模拟效果分析

基本信息

批准号：
66106926
负责人：
Professor Dr. Daniel Memmert
金额：
--
依托单位：
Institut für Informatik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2008
资助国家：
德国
起止时间：
2007-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/66106926?language=en
关键词：
Simulation interaction patterns simulative effectiveness

项目摘要

The characterization and analysis of complex games like football cannot be reduced to static distributions of numbers but requires the consideration of the game´s very own dynamic processes. Therefore, success of specific actions cannot simply be stated in percentages without giving the context in which these actions have taken place. Such analysis tools are not yet available. In the course of the underlying project, concepts of earlier projects shall be used as a basis to develop a computer-based analysis procedure that recognizes process patterns in a game and evaluates their effectiveness. The particular focus is on creative processes that do not occur frequently enough to be recognized, neither by a qualitative-context nor by a quantitative-statistical analysis. In order to analyze the effectiveness of such rare process patterns, the development of a specific game-simulation tool is planned: this way, the impact of creative actions on the further course of the game can be simulated and analyzed, both online-manually and offline-systematically. The project outline can be divided into two core areas:* The simulation serves the generation of action processes and their effectiveness analysis based on data collected from real matches.* The creativity analysis uses the results of the simulation specifically for the recognition, generation and evaluation of creative action processes and poses the basis for training of creative actions in the match practice.In preparation of the project, software tools have been developed that will need to be adjusted and developed further: The analysis tool SOCCER combines conventional data analysis, dynamic modeling based on states and events and artificial neural networks. Here, events such as win or loss of the ball are calculated from player and ball positions. From these positions, neural networks can also detect time-dependent player positions, thus enable the embedding of individual movements and its superordinate processes (e.g. defensive teamwork, winning the ball back, or attacking) into the respective situational context. By means of appropriate success-indicators such as ball possession, actions and processes can automatically be evaluated with regard to success and both quantitative as well as qualitative analyses will be possible in situation-specific contexts. More difficult is the analysis of creative actions and processes which are mostly characterized by their unexpected and rare occurrence and thus are difficult to identify within the dynamics of the game when using conventional recognition methods. For this, TriTop (Triangular Topology), a new type of a self-organizing network has been developed. With the help of a fractal triangular topology and a dynamic generation of neurons, the program is able to distinguish between rare and frequent events without losing the cluster landscape of neurons that characterizes the process-distribution.

足球等复杂游戏的特征和分析不能简化为数字的静态分布，而是需要考虑游戏本身的动态过程。因此，不能简单地以百分比来说明具体行动的成功，而不说明采取这些行动的背景。目前还没有这种分析工具。在基础项目的过程中，早期项目的概念应被用作开发基于计算机的分析程序的基础，该程序识别游戏中的过程模式并评估其有效性。特别关注的是创造性的过程，不经常发生的识别，无论是通过定性的背景下，也不是通过定量的统计分析。为了分析这种罕见的过程模式的有效性，一个特定的游戏模拟工具的开发计划：这样，创造性的行动对游戏的进一步进程的影响可以模拟和分析，在线手动和离线系统。项目大纲可分为两个核心领域：* 模拟服务于行动过程的生成及其基于从真实的比赛中收集的数据的有效性分析。*创造力分析使用模拟的结果，专门用于识别、生成和评估创造性动作过程，并为比赛实践中的创造性动作训练奠定基础。在项目准备过程中，开发了软件工具，需要进一步调整和开发：分析工具SOCCER结合了传统的数据分析，基于状态和事件的动态建模和人工神经网络。在这里，诸如球的赢或输的事件是根据球员和球的位置来计算的。从这些位置，神经网络还可以检测到与时间相关的球员位置，从而将个人运动及其高级过程（例如防守团队合作，赢回球或进攻）嵌入到相应的情境背景中。通过适当的成功指标，如控球，行动和过程可以自动评估成功，定量和定性分析将有可能在特定的情况下。更困难的是分析创造性的行动和过程，其主要特征是它们的意外和罕见的发生，因此难以识别的动态游戏时，使用传统的识别方法。为此，开发了一种新型的自组织网络TriTop（三角拓扑）。在分形三角拓扑和动态生成神经元的帮助下，该程序能够区分罕见事件和频繁事件，而不会丢失表征过程分布的神经元簇景观。