# Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

arXiv: https://arxiv.org/abs/1401.0942 · HTML: https://mlsanity.com/paper/1401.0942/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2014.

## Built on by (most cited first, 8 of 8)

- [Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis](https://mlsanity.com/paper/2302.09276/) (2023)
- [Data-driven Analysis for Understanding Team Sports Behaviors](https://mlsanity.com/paper/2102.07545/) (2021)
- [Mathematical models for off-ball scoring prediction in basketball](https://mlsanity.com/paper/2406.08749/) (2024)
- [Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions](https://mlsanity.com/paper/2306.12244/) (2023)
- [TacticExpert: Spatial-Temporal Graph Language Model for Basketball Tactics](https://mlsanity.com/paper/2503.10722/) (2025)
- [Understanding team collapse via probabilistic graphical models](https://mlsanity.com/paper/2402.10243/) (2024)
- [SportsNGEN: Sustained Generation of Realistic Multi-player Sports Gameplay](https://mlsanity.com/paper/2403.12977/) (2024)
- [HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning](https://mlsanity.com/paper/2608.29563/) (2026)

## Abstract (arXiv)

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.