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Home Home / Capstone Projects / Optimizing Risk-Based Anti-Doping Test Scheduling: Development of an Optimized Risk-Based Interval Test Scheduler (ORBITS)

Optimizing Risk-Based Anti-Doping Test Scheduling: Development of an Optimized Risk-Based Interval Test Scheduler (ORBITS)

Program: Data Science Master's Degree
Location: Not Specified (remote)
Student: Alex Bohl

Every National Anti-Doping Agency, NADO, strives to protect athletes and clean sport. Doping hurts the integrity of sport, poses a health risk to athletes, and steals moments and medals from athletes competing fairly. ORBITS aims to revolutionize the NADO test planning process to help plan tests at the right time on the right athlete. ORBITS consists of two main elements:

  1. An intentional doping risk classification model that produces the probability that a test will result in the detection of intentional doping.
  1. A linear optimization model that will use the results from the intentional doping risk classification model to produce an optimal test distribution plan that maximizes testing during intervals with a high probability of intentional doping while staying operationally feasible.

ORBITS aims to maximize detection and deterrence measures through more effective initial test planning and reduce human resource time and financial resources.

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