Factorized point process intensities: A spatial analysis of professional basketball

Andrew Miller, Luke Bomn, Ryan Adams, Kirk Goldsberry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

2014 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.

Original languageEnglish (US)
Title of host publication31st International Conference on Machine Learning, ICML 2014
PublisherInternational Machine Learning Society (IMLS)
Pages398-414
Number of pages17
ISBN (Electronic)9781634393973
StatePublished - Jan 1 2014
Externally publishedYes
Event31st International Conference on Machine Learning, ICML 2014 - Beijing, China
Duration: Jun 21 2014Jun 26 2014

Publication series

Name31st International Conference on Machine Learning, ICML 2014
Volume1

Other

Other31st International Conference on Machine Learning, ICML 2014
CountryChina
CityBeijing
Period6/21/146/26/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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  • Cite this

    Miller, A., Bomn, L., Adams, R., & Goldsberry, K. (2014). Factorized point process intensities: A spatial analysis of professional basketball. In 31st International Conference on Machine Learning, ICML 2014 (pp. 398-414). (31st International Conference on Machine Learning, ICML 2014; Vol. 1). International Machine Learning Society (IMLS).