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Judging Bias in Olympic Diving: Fairness at Risk Zones During the Tokyo 2021 Games
- 1 Shandong University
- 2 University of Beijing Normal university-Hong Kong Baptist University United International College
- 3 University of Wisconsin-Madison
- 4 Xian Jiaotong-Liverpool University
* Author to whom correspondence should be addressed.
Abstract
This study aimed to investigate whether judges exhibit bias when scoring divers from the same country, especially at the “risk moment”, which are competitively significant moments in the sequence of the event. Using the Tokyo 2021 Olympic Diving dataset (Smith 2021), the study first identified the risk zones and analyzed the judges’ scores in these areas in the preliminary and semi-final rounds. Subsequently, the permutation tests and t-tests were employed to examine whether the nationality of the divers affected the judges’ scores. The findings suggest that in non-risk zones, judges may support divers from the same country. Nevertheless, in the risk zone, there is no evidence to suggest that judges are biased, which means that judges may consciously maintain fairness during the competition rounds especially at the risk moment. In addition, we explored anti-bias, which means the home judge may give low scores to those divers who are in the risk zone but not from their own country, as these divers may pose a threat to the progress of their own diver at the risk moment. However, the results show that while some judges’ behavior is consistent with this assumption, such anti-bias lacks statistical significance with reference to the results of the corresponding permutation test.
Keywords
Risk Zone, Bias Detection, Tokyo 2021 Olympics Diving Competition, Permutation Tests
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Cite this article
Song,K.;Lin,X.;Li,J.;Yao,Y. (2025). Judging Bias in Olympic Diving: Fairness at Risk Zones During the Tokyo 2021 Games. Applied and Computational Engineering,131,212-221.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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