Abstract:Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by short detection windows for many prohibited substances. These constraints leave large portions of athletes without regular testing, motivating complementary screening approaches that analyze routine competition results to identify suspicious performance patterns. We present a system that processes 1.6 million athletics performances from over 19,000 competitions (2010-2025) using eight detection methods ranging from statistical rules to machine learning and trajectory analysis. We validate all methods against publicly confirmed anti-doping violations to measure their effectiveness in identifying sanctioned athletes. Trajectory-based methods, which compare performances to expected career progression, achieve the best balance between detecting violations and limiting false alarms, though all methods face challenges from incomplete data and rare confirmed violations. The system provides an interactive interface for expert-driven investigation, emphasizing transparency and human judgment to support, rather than replace, established anti-doping processes.
| Comments: | 8 pages, 5 figures, 5 tables |
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| Cite as: | arXiv:2604.21953 [cs.LG] |
| (or arXiv:2604.21953v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.21953 arXiv-issued DOI via DataCite |
From: Blessed Madukoma [view email]
[v1]
Thu, 23 Apr 2026 06:21:47 UTC (1,186 KB)
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