StravaSegmentAuditLog
Tracks suspicious Strava segment records and alerts cyclists/runners when competitors post anatomically impossible times, with AI-powered anomaly detection
The Problem
Strava segment leaderboards are rife with fake records from GPS spoofing, e-bike cheating, and car-aided runs, but there's no way for legitimate athletes to audit suspicious times or get alerted when their records are beaten by impossible performances. Cyclists waste mental energy disputing fraudulent KOMs instead of focusing on actual training.
Target Audience
Serious amateur cyclists and runners (age 25-55) who care about segment leaderboard credibility, particularly in competitive local cycling clubs and running communities
Why Now?
Strava fraud is getting worse as cheap GPS spoofing tools proliferate, and AI makes it feasible for a solo dev to build decent anomaly detection without ML PhDs
What's Missing
Strava treats all records equally and only acts on manual reports; there's no proactive, algorithmic auditing that highlights statistically impossible performances based on physics (gradient speed limits, human power output thresholds)
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