通过分析160万次田径比赛数据，识别异常表现以辅助反兴奋剂检测。
Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics
- 用八种方法分析运动员成绩轨迹，找异常模式。
- 基于生涯轨迹的方法在检出率与误报间平衡最优。
- 适合反兴奋剂机构和体育数据分析师使用。
反兴奋剂项目依赖生物检测来发现违禁药物，但每份样本成本超800美元，且多数禁药检测窗口期短。这导致大量运动员无法定期检测，促使开发基于常规比赛结果的补充筛查方法，以识别可疑性能异常。本文构建了一个系统，处理2010至2025年间来自超过1.9万场赛事的160万条田径成绩记录，采用八种检测方法（从统计规则到机器学习、轨迹分析）。所有方法均通过公开确认的兴奋剂违规案例进行验证，评估其识别被处罚运动员的能力。基于轨迹的方法——即比较运动员表现与其预期职业生涯进展——在检出违规与控制误报之间取得最佳平衡，尽管所有方法仍受数据不全和违规案例稀少的挑战。系统提供交互式界面，支持专家主导调查，强调透明性与人工判断，旨在辅助而非取代现有反兴奋剂流程。
原文摘要 · Abstract (English)
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.
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