Abstract
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.
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