In this paper we investigate the performance of common capture models in terms of the fairness properties they reflect across contenting hidden connections. We propose a new capture model, Message Retraining,as a means of providing an accurate description of experimental data. Using two fairness indices we undertake a quantitative study of the accuracy with which each capture model is able to reflect experimental data. Standard capture models are shown to be unable to accurately reflect the fairness properties of empirical data. The Message Retraining capture model is shown to provide a good estimate of actual system performance in varying signal strength conditions.