• Corpus ID: 248965160

Multidimensional heterogeneity learning for count value tensor data with applications to field goal attempt analysis of NBA players

@inproceedings{Hu2022MultidimensionalHL,
  title={Multidimensional heterogeneity learning for count value tensor data with applications to field goal attempt analysis of NBA players},
  author={Guanyu Hu and Yishu Xue and Weining Shen},
  year={2022}
}
We propose a multidimensional tensor clustering approach for studying how professional basketball players’ shooting patterns vary over court locations and game time. Unlike most existing methods that only study continuous-valued tensors or have to assume the same cluster structure along different tensor directions, we propose a Bayesian nonparametric model that deals with count-valued tensors and projects the heterogeneity among players onto tensor dimensions while allowing cluster structures… 

References

SHOWING 1-10 OF 31 REFERENCES
Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach
TLDR
An efficient Markov chain Monte Carlo algorithm for posterior sampling that allows simultaneous inference on both the number of clusters and the cluster configurations and to establish large sample convergence properties for the posterior distribution is proposed.
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
TLDR
A machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA shows that a low-rank spatial decomposition summarizes the shooting habits of NBA players.
Bayesian group learning for shot selection of professional basketball players
TLDR
A mixture of finite mixtures (MFM) model is proposed to capture the heterogeneity of shot selection among different players based on the Log Gaussian Cox process (LGCP) to develop a group learning approach to analyze the underlying heterogeneity structure ofshot selection among professional basketball players in the NBA.
Dynamic Tensor Clustering
  • W. Sun, Lexin Li
  • Computer Science
    Journal of the American Statistical Association
  • 2019
TLDR
A new dynamic Tensor clustering method that works for a general-order dynamic tensor, and enjoys both strong statistical guarantee and high computational efficiency is proposed.
Bayesian Nonparametric Estimation for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations
TLDR
A novel nonparametric Bayesian method for learning the underlying intensity surface built upon a combination of Dirichlet process and Markov random field is presented, which has the advantage of effectively encouraging local spatial homogeneity when estimating a globally heterogeneous intensity surface.
A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes
TLDR
A framework for using optical player tracking data to estimate, in real time, the expected number of points obtained by the end of a possession, called expected possession value (EPV), derives from a stochastic process model for the evolution of a basketball possession.
A Bayesian Joint Model for Spatial Point Processes with Application to Basketball Shot Chart
The success rate of a basketball shot may be higher at locations where a player makes more shots. In a marked spatial point process model, this means that the marks are dependent on the intensity of
Bayesian Methods for Tensor Regression
TLDR
A review of some relevant Bayesian models on tensor regressions developed in recent years is provided, addressing the problem of making inference with a tensor response and a vector of covariates, with applications including task related brain activation and connectivity studies.
Provable Convex Co-clustering of Tensors
TLDR
A provable convex formulation of tensor co-clustering is developed and a non-asymptotic error bound is established for the CoCo estimator, which reveals a surprising “blessing of dimensionality” phenomenon that does not exist in vector or matrix-variate cluster analysis.
A Spatial Analysis of Basketball Shot Chart Data
TLDR
H hierarchical spatial models are developed for shot-chart data, which allow for spatially varying effects of covariates and permit differential smoothing of the fitted surface in two spatial directions, which naturally correspond to polar coordinates.
...
...