Predictive frameworks for youth basketball: a leakage-aware reassessment of athlete development and injury-risk models
Year: 2026
Type: article
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. Methods: 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. Results: 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. Discussion: 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.
Source: Frontiers in Physiology
Author: Chen Yao
Institution: Nanjing Institute of Technology
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223183052