Earticle

초록

영어

Artificial intelligence (AI) has transformed elite basketball performance analysis by enabling the collection, integration, and interpretation of complex performance data. Although previous reviews have summarized AI applications in sport, relatively few have synthesized how these technologies support performance analysis and evidence-informed coaching decision-making in elite basketball. Evidence remains fragmented across prediction, tactical analysis, workload monitoring, athlete development, and coaching support. This structured narrative review synthesizes AI applications in elite basketball performance analysis and examines their implications for coaching practice, player monitoring, tactical interpretation, and evidence-informed decision-making. Particular attention is given to methodological challenges, practical implementation, and ethical considerations influencing AI adoption in elite basketball. Across the literature, AI applications have evolved beyond performance prediction toward decision-support systems for tactical analysis, player tracking, wearable monitoring, and individualized athlete management. Computer vision, tracking technologies, and wearable sensing provide comprehensive information on spatiotemporal behaviour, movement coordination, tactical organization, and athlete workload. However, implementation lags behind technological development because of limited external validation and ecological validity, fragmented datasets, limited interpretability, and difficulty translating AI-generated outputs into meaningful coaching decisions. Ethical concerns regarding privacy, biometric data governance, fairness, and algorithmic transparency remain barriers to responsible implementation. Rather than replacing coaching expertise, current evidence supports AI as a complementary decision-support tool that enhances human judgement in elite basketball. AI has considerable potential to strengthen performance analysis and evidence-informed coaching in elite basketball when supported by valid, interpretable, and ethically governed decision-support systems. Future research should prioritize longitudinal validation, explainable AI, integrated data ecosystems, coach-centred system design, and responsible governance to facilitate translation of AI innovations into high-performance basketball practice.

목차

ABSTRACT
I. Introduction
1. Digital Transformation in Sports
2. Emergence of AI in Basketball
3. Purpose of This Review
Ⅱ. Methods
1. Review Design
2. Literature Search, Study Selection, and Eligibility Criteria
3. Synthesis Approach
4. Review Scope and Limitations
Ⅲ. AI Applications in Elite Basketball
1. Performance Prediction and Game Analytics
2. Computer Vision, Player Tracking, and Perceptual-Cognitive Analysis
Ⅳ. AI-Assisted Coaching and Decision-Making
1. Tactical Analysis and Coaching Support
2. Personalized Training and Talent Development Pathways
Ⅴ. Challenges and Ethical Concerns
1. Data Privacy and Athlete Monitoring
2. Over-Reliance on AI Systems
3. Accessibility and Governance Issues
Ⅵ. Future Directions
1. Integrated AI Ecosystems in Sports
2. Human–AI Collaboration
3. Sustainable and Ethical AI Development
Ⅶ. Conclusion
References
Declarations

키워드

저자

• Ma Qian [ PhD Candidate, Department of Sport Science Convergence, Dankook University ] First Author
• Wu Fan [ PhD Candidate, Department of Early Childhood Education, Pusan National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Open Regional Studies

• 간기

  부정기

• pISSN

  2951-3197

• 수록기간

  2022~2026

• 등재여부

  KCI 등재후보

• 십진분류

  KDC 912 DDC 951