Multifactor and multidimensional data quality analysis of judge scoring in diving competition - Cai, Weijun; Xiang, Rong (Frontiers in Psychology, 2024)
Data quality is vital to maintain 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. A case study examining the Women's 3 m Springboard event at the FINA Diving World Cup 2022 serves to illustrate the application of this method. The Kendall covariance coefficient is employed to assess the data quality from the judges as a unified entity, whereas the Kendall correlation coefficient is utilized to evaluate the data quality from individual judges. This approach uncovers disparities in data quality attributed to the judges' panel across each diver, each round, and the various diving maneuvers.