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• W2891630447 abstract "We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. This makes it difficult to draw comparisons between players and make accurate player specific predictions. Modeling shot attempt data as a point process, we create a low dimensional representation of offensive player types in the NBA. Using non-negative matrix factorization (NMF), an unsupervised dimensionality reduction technique, we show that a low-rank spatial decomposition summarizes the shooting habits of NBA players. The spatial representations discovered by the algorithm correspond to intuitive descriptions of NBA player types, and can be used to model other spatial effects, such as shooting accuracy." @default.
• W2891630447 created "2018-09-27" @default.
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• W2891630447 date "2014-01-01" @default.
• W2891630447 modified "2023-09-24" @default.
• W2891630447 title "Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball" @default.
• W2891630447 hasPublicationYear "2014" @default.
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