Bayesian clustering for synchronized diving

Synchronized diving is one of the most widely viewed Olympic events after its first appearance at the Sydney Olympic Games in 2000. It gives spectators the ability to compare performances of divers on their own without much understanding of the technical details of the sport. In this project, we develop methodology to investigate the complexity of judges' scores and the relative behaviour of judges from synchronized diving events. We explore a Bayesian clustering methodology as introduced in Gill, Swartz and Treschow (2007) to cluster judges. A model that captures the characteristics of the judges' scores is introduced and a dataset from the 12th FINA World Championships in Melbourne 2007 is fit using the proposed model. We demonstrate how the missing values raised from the judging system can be easily handled in a Bayesian analysis via implementation in WinBUGS. The analysis may reveal associations among judges.