{
  "abstract": "Introduction: Artificial intelligence is increasingly applied in sports analytics; however, exceptionally high predictive performance may result from target construction, data leakage, or deterministic label structures. This study reassessed predictive models for youth basketball using a leakage-aware framework.\n\nMethods: A publicly available youth-basketball dataset containing 1,000 records and 25 variables was analyzed. Development-score construction and injury-risk label structure were audited. Construction-related predictors were subsequently excluded, and model performance was evaluated using repeated cross-validation and a locked 200-athlete holdout. Subgroup analyses by gender and age and an external-validation feasibility assessment were also conducted.\n\nResults: Development_Score was reconstructed almost perfectly from its documented components, with repeated cross-validation R2 of 0.999925. Injury_Risk_Level also demonstrated deterministic structure based on strength and endurance. After excluding construction-related predictors, locked-holdout performance decreased to 0.750 accuracy, 0.329 balanced accuracy, 0.286 macro-F1, and 0.622 macro AUC. High-risk sensitivity was 0.000 across the overall sample and examined subgroups. No compatible public dataset was identified for external validation.\n\nDiscussion: The initially exceptional performance largely reflected target construction and deterministic labeling rather than independent predictive information. These findings emphasize leakage-aware validation, transparent reporting, and cautious interpretation of sports-analytics prediction models.",
  "abstract_shown": true,
  "affiliations": [
    "Nanjing Institute of Technology (NJIT), Nanjing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chen Yao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c2c0k48trvrqb.bib",
  "cited_by": 0,
  "doi": "10.3389/fphys.2026.1912035",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736777/",
  "id": "c2c0k48trvrqb",
  "issns": [
    "1664-042X"
  ],
  "landing_url": "https://doi.org/10.3389/fphys.2026.1912035",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2732|Sports injuries and prevention"
    ]
  ],
  "openalex": "W7223183052",
  "pdf_url": "https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1912035/pdf",
  "pmcid": "PMC13736777",
  "pmid": "42991284",
  "published": "2026-10-22",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/c2c0k48trvrqb.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:09Z",
      "native_id": "PMC13736777",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fphys.2026.1912035"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2732|Sports injuries and prevention",
      "subfield": "surgery"
    }
  ],
  "title": "Predictive frameworks for youth basketball: a leakage-aware reassessment of athlete development and injury-risk models",
  "title_text": "Predictive frameworks for youth basketball: a leakage-aware reassessment of athlete development and injury-risk models",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:09Z",
  "url": "https://research.draconitic.com/paper/c2c0k48trvrqb",
  "venue": "Frontiers in Physiology",
  "versions": []
}