Sports
How Technology Catches Marathon Cheaters
Roberto Abrahão explains how cross-referencing split times and risk scores exposes course-cutting in road races.
Systematic timing data analysis has transformed race monitoring. Roberto Abrahão, creator of the Um Golpe por Milha project, explains how algorithms cross-reference split times and calculate risk scores to flag potential marathon fraud.
Sports result auditing has gained a definitive ally in data technology. In the Um Golpe por Milha project, the process of detecting race cheating relies on objective timing criteria and course geometry to filter out inconsistencies among thousands of finishers.
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Selection Criteria and Essential Data
For a marathon or road race to be analyzed, the primary requirement is the public release of split times. According to Roberto Abrahão, publishing these splits was rare in domestic events until recently—a trend that shifted as demands for athletic integrity grew. The focus falls primarily on large-scale events and looped or out-and-back courses prone to course-cutting, with rare exceptions made for historic events like the Boston Marathon.
Algorithm, Split Times, and Risk Scores
Once the complete results database is imported into the system, the algorithm calculates an athlete's pace segment by segment, comparing pace variations against historical averages and the runner's overall performance. A missed timing mat serves as a multiplier in an internal risk calculation. In major events such as the Mexico City Marathon, initial screening can flag hundreds of suspicious runners, requiring a secondary audit and additional tools to eliminate false positives before reaching out to race organizers.
Triangulation and Detecting Transit Use
The technology can also deduce how an infraction occurred through mathematical triangulation. By cross-referencing elapsed time between timing mats with the runner's average speed, the system estimates the actual distance covered—identifying whether a runner cut a single corner or used public transit or a vehicle. When recorded speeds are incompatible with running on foot and the math does not add up, the suspicion of motorized transit grows stronger, leaving organizers to verify the flag by comparing chip data with course photography.
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