SAI centres using analytics for talent pipelines
India’s Sports Authority National Centres of Excellence continue to integrate systematic athlete development with analytics, serving as operational models for scouting, performance monitoring and athlete welfare reported. Those centres show how analytics feeds into long‑term talent pathways and operations planning.
SAI operates 23 National Centres of Excellence and 67 SAI Training Centres, with about 9,025 athletes (5,579 boys and 3,446 girls) training across those facilities. (indianbureaucracy.com) India’s National Centre for Sports Science and Research has deployed an AI-driven Athlete Management and Sports Science platform for injury-prediction, age verification, diet prescription and psychological profiling at elite centres. (thehindubusinessline.com) The government-backed Khel Drishti dashboard consolidates multi-program performance and infrastructure data to support Olympic preparations for LA 2028 and the 2026 Asian Games, providing a national dataset that NCOEs are beginning to feed into. (kheldrishti.com) Regional NCOEs have upgraded sports‑science facilities — Guwahati added anthropometry and psychological labs in May 2025 — while Sonipat NCOE received a ministerial review during Dr. Mansukh Mandaviya’s visit on October 12, 2025. (timesofindia.indiatimes.com) SAI has run large-scale hiring for performance analysts — recent recruitment rounds listed 36–48 performance-analyst vacancies across physiotherapy, physiology, nutrition, biomechanics and anthropometry with application windows in early 2026. (freshersnow.com) Performance-analyst job descriptions in Indian federations require match and training video analysis, opposition scouting and delivery of statistical reports, while NCOE operations roles cover daily athlete logistics, accommodation and training schedules as outlined on SAI’s NCOE programme pages. (rugbyindia.in) Practical student projects tied to NCOE pipelines include: a scouting-dashboard prototype using Khel Drishti outputs and public match stats; an injury‑risk prototype trained on open IMU/wearable datasets from PhysioNet or the WEAR dataset; and an operations SOP built around SAI NCOE accommodation and schedule data for tournament coordination. (kheldrishti.com)
