Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics

2026-04-23

AI Summary

This study addresses the limitations of conventional doping tests—namely high costs and short detection windows—which hinder large-scale screening. To complement existing approaches, the authors propose a performance-based auxiliary detection method leveraging 1.9 million athletics records. Their framework integrates eight anomaly detection strategies, including statistical rules, machine learning, and athlete career trajectory modeling, supported by interactive visualizations to facilitate human-in-the-loop investigation. The work presents the first systematic evaluation of these diverse methods, demonstrating that trajectory analysis achieves the best trade-off between identifying confirmed violators and minimizing false positives. These findings validate the feasibility of performance-driven screening and offer anti-doping authorities a transparent, interpretable new paradigm for detecting potential doping behavior.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• performance anomaly detection
• anti-doping
• athletics
• suspicious performance patterns
• biological testing

Innovation

• performance anomaly detection
• trajectory analysis
• anti-doping
• visual analytics
• benchmarking system