Journal of Quantitative Analysis in Sports· 2026Q2
Is Stephen Curry really a guard? A new perspective on player typologies using functional data analysis
- 1citations
- Q2SCImago
- 2026year
Short summary
A novel Functional Data Analysis (FDA) approach represents NBA players' shooting patterns as smooth functions over the half-court, revealing a data-driven player taxonomy that shows low agreement with traditional position labels.
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Key points
- NBA player shooting patterns are modeled as smooth functions over a 2D half-court domain.
- Multivariate functional principal components analysis (MFPCA) identifies primary modes of shooting variability.
- K-medoids clustering on player scores creates a data-driven player taxonomy.
- The data-driven player clusters show low agreement with traditional NBA position labels.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract We present a novel representation of NBA players’ shooting patterns based on Functional Data Analysis (FDA). Each player’s charts of made and missed shots are treated as smooth functional data defined over a two-dimensional domain corresponding to the offensive half-court. This continuous representation enables a parsimonious multivariate functional principal components analysis (MFPCA) decomposition, producing a set of common principal component functions that capture the primary modes of variability in shooting patterns, along with player-specific scores that quantify individual deviations from the average behavior. We first interpret the principal component functions to characterize the main sources of variation in shooting tendencies. We then apply k -medoids clustering to the principal component scores to construct a data-driven taxonomy of players. Comparing our empirical clusters to conventional NBA position labels reveals low agreement, suggesting that our shooting-pattern representation might capture aspects of playing style not fully reflected in official designations. The application of FDA to this area introduces a flexible, interpretable, and continuous framework for analyzing player tendencies, with potential applications in coaching, scouting, and historical player or match comparisons.
The authors' abstract, as published at the source. Journal of Quantitative Analysis in Sports, 2026 · DOI ↗
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Field: Economics and Econometrics
Economics and EconometricsEconomics, Econometrics and Finance