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Data Analytics for Uncovering Fraudulent Behaviour in Elite Sports

Maxx Richard Rahman; Thomas Piper; Hans Geyer; Tristan Equey; Norbert Baume; Reid Aikin; Wolfgang Maaß
In: ICIS 2022 Proceedings. International Conference on Information Systems (ICIS-2022), December 9-14, Copenhagen, Denmark, ICIS, 12/2022.


Sports officials around the world are facing societal challenges due to the unfair nature of fraudulent practices performed by unscrupulous athletes. Recently, sample swapping has been raised as a potential practice where some athletes exchange their doped sample with a clean one to evade a positive test. The current detection method for such cases includes laboratory testing like DNA analysis. However, these methods are costly and time-consuming, which goes beyond the budgetary limits of anti-doping organisations. Therefore, there is a need to explore alternative methods to improve decision making. We presented a data analytical methodology that supports anti-doping decision-makers on the task of athlete disambiguation. Our proposed model helps identify the swapped sample, which outperforms the current state-of-the-art method and different baseline models. The evaluation on real-world sample swapping cases shows promising results that help advance the research on the application of data analytics in the context of anti-doping analysis.


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