A deep learning–based study of player styles and cross-league performance adaptation mechanisms: a case study of the NBA and CBA

This study advances the quantitative analysis of athletic performance by integrating deep learning with interpretable analytics. Its insights provide actionable references for training, player transfers, and youth talent development, supporting data-driven decisions in professional basketball management.

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Publication Details

Journal
Frontiers in Sports and Active Living
Published
2025-12-04
DOI
https://doi.org/10.3389/fspor.2025.1639972
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
0.00
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article

A deep learning–based study of player styles and cross-league performance adaptation mechanisms: a case study of the NBA and CBA

Frontiers in Sports and Active Living
Sports Performance and Training
article

A deep learning–based study of player styles and cross-league performance adaptation mechanisms: a case study of the NBA and CBA

article en

Abstract

This study advances the quantitative analysis of athletic performance by integrating deep learning with interpretable analytics. Its insights provide actionable references for training, player transfers, and youth talent development, supporting data-driven decisions in professional basketball management.

Frontiers in Sports and Active LivingVol. 7
Xi'an Physical Education University (CN)
Openalex Percentile: Top 38%
Sports Performance and Training
DOIOpen Access PDFOpenAlex
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A deep learning–based study of player styles and cross-league performance adaptation mechanisms: a case study of the NBA and CBA · Frontiers in Sports and Active Living (2025) | TGRS Research Map | TGRS