A Game of Runs: Extracting Initiation Rules for Basketball Scoring Runs through Focused Sequence Mining

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Abstract Basketball games are often defined by scoring runs, where one team gains momentum while the opponent struggles to respond. Although it is widely recognized that such runs frequently decide the outcome of games, predicting when they will occur remains unexplored. This research addresses three key areas. First, we develop an algorithm to identify patterns that lead to scoring runs. Second, we present a model capable of predicting the onset of these runs. Finally, building on these findings, we propose a generative model that, based on partial sequences, suggests full sequences likely to result in a run. Our predictor employs a novel pattern recognition method using convolutional neural networks, achieving an accuracy of 81%. To our knowledge, this is the first study to explore and predict basketball scoring runs using this approach.

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