Analyzing NBA player positions and interactions with density-functional fluctuation theory
Abstract The increasing availability of high-precision player-tracking data in sports—centimeter-precision positional information of athletes captured dozens of times per second—has the potential to improve the quantification of player abilities and overall team strategies. Working toward achieving...
Saved in:
| Main Authors: | Boris Barron, Nathan Sitaraman, Tomás Arias |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-04953-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Quantitative Analysis of the Determinants of NBA Player Performance and Market Value
by: Lin Chuhao
Published: (2025-01-01) -
Stacked ensemble model for NBA game outcome prediction analysis
by: Guangsen He, et al.
Published: (2025-08-01) -
Getting NBA Shots in Context: Analysing Basketball Shots with Graph Embeddings
by: Schmid Marc, et al.
Published: (2025-05-01) -
Social post-error adaptations across four NBA basketball seasons
by: Ayala Denul, et al.
Published: (2025-05-01) -
Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction
by: Yeung Manho
Published: (2025-01-01)