Multifactor and multidimensional data quality analysis of judge scoring in diving competition
Weijun Cai, Rong Xiang

TL;DR
This paper introduces a method to evaluate the quality of judge scoring in diving competitions using statistical measures to identify inconsistencies.
Contribution
A novel method using Kendall covariance and correlation coefficients to analyze judge scoring data quality in diving.
Findings
Judge group scoring data quality is generally high, but inconsistencies were found for specific divers, rounds, and dives.
The method reveals disparities in data quality across different competition elements.
Kendall correlation may not be suitable for small data differences and sample sizes.
Abstract
In sports competitions, judge scoring data serve as an objective measure of an athlete’s performance level. However, research has indicated the unreliability of objective measurements. Controversy often arises regarding the quality of judge scoring data, undermining fairness and justice in sports competitions. This paper proposes a method utilizing the Kendall covariance coefficient and the Kendall correlation coefficient for the thorough evaluation of judging data quality in diving events. The analysis is structured around four key elements: overall competition, individual divers, specific rounds, and distinct diving techniques. Each element is analyzed across three dimensions: the collective data quality from the judging panel, interjudge data quality comparisons, and the alignment of individual judges’ scores with the final tallied scores. Two case studies serve to illustrate the…
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Taxonomy
TopicsReliability and Agreement in Measurement · Economic and Environmental Valuation · Sports Analytics and Performance
