A comprehensive overview of artificial intelligence applications in basketball
Bibliographic record
Abstract
The sports industry is progressively embracing technological advancements, and artificial intelligence stands out as a prominent innovation. Basketball in particular, has captured the interest of the real-time analytics and data science community. With the development, deployment, and experience of AI models by both viewers and players, it is crucial to provide a comprehensive summary of AI applications in basketball. This review is performed based on literature sourced from Web of Science and Dimensions databases, where articles were thoroughly examined to identify AI use cases. The study was backed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework and furthermore utilized computational literature review as well as bibliometric analysis techniques for knowledge extraction purposes. Our results revealed that the area of sports analytics is gaining momentum and AI in the basketball world has more adoption in China, USA, Australia, Canada, Italy and Spain from a research perspective. Our study offers contributions to theory and practice in the sports science and applied AI domains. © JPES.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".