Researchers have developed a new system to detect suspicious performance patterns in athletics, aiming to complement traditional anti-doping methods. The system analyzes over 1.6 million competition results from 2010 to 2025, employing eight different detection techniques including statistical rules and machine learning. Trajectory-based analysis, which models athletes' career progression, showed the most promise in identifying sanctioned athletes while minimizing false positives, though challenges remain with data completeness and rare violation instances. The system is designed to support, not replace, human judgment in anti-doping investigations. AI
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IMPACT Offers a novel AI-driven approach to enhance anti-doping efforts by analyzing performance data.
RANK_REASON Academic paper detailing a new system and benchmarking results.