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Paper Number
2411
Paper Type
Complete
Description
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.
Recommended Citation
Rahman, Maxx Richard; Piper, Thomas; Geyer, Hans; Equey, Tristan; Baume, Norbert; Aikin, Reid; and Maass, Wolfgang, "Data Analytics for Uncovering Fraudulent Behaviour in Elite Sports" (2022). ICIS 2022 Proceedings. 13.
https://aisel.aisnet.org/icis2022/data_analytics/data_analytics/13
Data Analytics for Uncovering Fraudulent Behaviour in Elite Sports
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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13-DataAnalytics