A data analytics approach to identifying doping risk indicators - development of a doping suspect prioritisation tool

UK Anti-Doping (UKAD) is the national body responsible for the fight against doping in UK sport. They strive to make sport clean and fair for all, through their extensive education and testing programmes.

Following the World Anti-Doping Code, UKAD must conduct sport-specific risk assessments in which factors including history of doping in a sport, the physiological demands, which prohibited substances and methods may be used, and the potential incentives for athletes to dope, are considered.

From this, sports can be classified into levels of doping risk and the athlete testing programme is developed accordingly.

UKAD now wishes to evolve their risk assessment model to focus on individuals and identify those most at risk of doping.

This project aims to take a data analytics approach by identifying doping risk behaviours and indicators, and exploring how these, and psychosocial factors, interact to determine the level of individual doping risk.

To determine risk indicators, a thorough literature review will be completed, followed by interviews with key informants, such as sanctioned athletes and athlete support personnel.

Information from various UKAD datasets, such as testing data and education history of athletes, amongst others, will also be collated. Combining information gathered will allow for a comprehensive picture of the behaviours behind doping.

Using mathematical modelling, we hope to develop a risk assessment model and predictive tool that UKAD can use to accurately determine those most at risk of doping and effectively target testing.

If successful, the model could be shared with anti-doping agencies, sport federations and sport councils worldwide, and change the process of drug testing in sport.