We address the problem of automatic extraction of patterns in the sequence of events in basketball games and construction of statistical models for generating a plausible simulation of a match between two distinct teams. We present a method for automatic construction of an attribute space which requires very little expert knowledge. The attributes are defined as the ratio between the number of entries and exits from higher-level concepts that are identified as groups of similar in-game events. The similarity between events is determined by the similarity between probability distributions describing the preceding and the following events in the observed sequences of game progression. The methodology is general and is applicable to any sports game that can be modelled as a random walk through the state space. Experiments on basketball show that automatically generated attributes are as informative as those derived using expert knowledge. Furthermore, the obtained simulations are in line with empirical data.

中文翻译：

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篮球建模的自动属性构建

我们解决了在篮球比赛中按事件顺序自动提取模式的问题，以及建立统计模型以生成对两个不同球队之间​​比赛的合理模拟的问题。我们提出了一种自动构建属性空间的方法，该方法几乎不需要专家知识。属性定义为被识别为相似游戏事件组的更高级别概念的进入和退出数量之间的比率。事件之间的相似性由描述观察到的游戏进程序列中先前事件和随后事件的概率分布之间的相似性确定。该方法是通用的，适用于可以建模为在状态空间中随机游走的任何体育游戏。在篮球上进行的实验表明，自动生成的属性与使用专家知识得出的属性一样有用。此外，所获得的模拟与经验数据相符。